the excess calorie consumption for U.S. obese adults (BMI over 35) of roughly 500 kcal/day (Hall et al. 2011a). That represents the increased calorie consumption to maintain obese conditions for U.S. adults, and is actually more than double the increased calorie consumption necessary to become obese. This estimate represents a revised view upward compared to the traditional view of only around 200 kcal/day, which did not account for the greater calorie intake required to maintain the larger body size of the overweight or obese. The 500 kcal/day assumes that all obese children have a similar overconsumption. Similarly, FAO has estimated that consumption of 2,700 to 3,000 kcal per person per day will lead to obesity by people with sedentary lifestyles (FAO 2004). Using the midpoint of 2,850 kcal, and assuming that an acceptable diet would consist of 2,350 kcal per person, we estimate that the elimination of obesity would reduce consumption by 500 kcal per person per day.

118. Authors' calculations from Ng, Fleming, et al. (2014). Regionally, the smallest caloric reductions occur in lower-income regions (e.g., 1% in sub-Saharan Africa) with the largest reductions in higher-income regions (e.g., 4% in the United States and Canada).

119. Searchinger, Hanson, Ranganathan, et al. (2013); Ranganathan et al. (2016).

120. Ng, Fleming, et al. (2014).

121. Skinner et al. (2018).

122. As discussed further in Course 4, the 2050 baseline projection assumes a 10% decrease in wild fish catch relative to 2010, and it assumes that all increases in global fish demand are met by aquaculture (thus farmed fish make up a larger proportion of total fish consumption by 2050).

123. These reductions set maximum thresholds of animal-based foods of 613 kcal per person per day for the 10% reduction and 399 kcal for the 30% reduction (Figure 6-8). They also reduced the global increase in animal-based food consumption between 2010 and 2050 from 68% (baseline) to 51% (10% reduction) and to 18% (30% reduction).

124. In order to be conservative, for the diet scenarios explored in this chapter, whenever land use in 2050 was reduced below 2010 levels, we set land-use-change emissions between 2010 and 2050 to zero (rather than assuming perfect restoration of all "liberated" lands back into native vegetation by 2050).

125. Ranganathan et al. (2016), Figure 10. Using data on UK vegetarian diets from Scarborough et al. (2014), Ranganathan et al. (2016) found that a scenario that halved U.S. meat and dairy consumption reduced the proportion of animal-based food calories to total calories to 14%, whereas the typical UK vegetarian in Scarborough et al. (2014) consumed 18% of their calories in the form of animal-based foods (including dairy and eggs, but not fish or meat).

126. Scarborough et al. (2014).

127. A 10% global cut, relative to 2050 baseline, set a threshold level of 373 calories of meat per person per day, slightly above the level of meat consumption in the United States in 2010 (Figure 6-9). A 30% global cut in meat requires a threshold of 238 calories of meat per day, slightly higher than per capita meat consumption in Russia in 2010. These scenarios reduced the global increase in meat consumption between 2010 and 2050 from 71% (baseline) to 54% (10% reduction) and to 20% (30% reduction).

128. GlobAgri-WRR model using source data from FAO (2019a).

129. In addition to a "less ruminant meat, shift to poultry and pork" scenario, we could have also modeled shifts to other nonmeat animal proteins, such as fish, dairy, or eggs. Because fish, dairy, and eggs are of a similar level of resource intensity as poultry and pork (more resource-intensive than plant proteins, and much less resource-intensive than ruminant meats), the results in terms of closure of the food, land use, and GHG emissions gaps would have been similar.

130. Springmann, Godfray, et al. (2016).

131. Willett et al. (2019), Springmann et al. (2018). These authors, however, did not assess changes in pastureland use under their healthy diet scenario, which would have certainly shown a large decline given the pronounced move away from ruminant meat and, to a lesser extent, away from dairy.

132. For example, Springmann, Godfray, et al. (2016) estimated that 97 percent of the GHG emissions reductions in their "healthy global diets" scenario were attributable to the reductions in red meat consumption (and we assume the vast majority of those reductions were due to reductions in ruminant meat consumption, since pork is of similar resource intensity to poultry).

133. For the calculations in this section, we used 2010 global average production efficiencies (shown in Figure 6-6). GlobAgri-WRR can also analyze the addition to the world of a person in the United States alone, which would assign more production to the United States because it would follow existing trade patterns. We followed the global average approach because we wished to analyze the consequences of other people adopting this diet globally.

134. The Vegetarian Diet scenario, which uses data from Scarborough et al. (2014), actually includes very small amounts of meat, as "vegetarians" were self-reported.

135. FAO (2019a).

136. Beattie et al. (2010); Tootelian and Ross (2000).

137. Larson and Story (2009). However, studies have shown that labeling can provide an incentive to food companies to reformulate products to make them healthier (Vyth et al. 2010; Variyam 2005).

138. Capacci et al. (2012).

139. Dumanovsky (2011).
140. Hammer et al. (2009).

141. Winter and Rossiter (1988); McDonald et al. (2002).

142. Thaler and Sunstein (2008); Garnett (2014); Bailey and Harper (2015); Wellesley et al. (2015).

143. Stummer (2003); Cornish Sardine Management Association (2015).

144. Reardon et al. (2003).

145. Reardon et al. (2003); Reardon and Timmer (2012).

146. Reardon and Timmer (2012).

147. Reardon et al. (2007).

148. Pingali (2007).

149. USDA/ERS (2014a).

150. Hawkes (2008) in Garnett and Wilkes (2014).

151. USDA/ERS (2014b).

152. DEFRA (2014).

153. Suher et al. (2016).

154. Turnwald et al. (2017).

155. Bacon and Krpan (2018).

156. Keats and Wiggins (2014).

157. Haley (2001).

158. For example, based on average U.S. retail prices in 2013, the price per gram of protein ranged from 0.9 cents for dried lentils, 1.1 cents for wheat flour, 1.2 cents for dried black beans, and 2.3 cents for dried white rice, to 2.7 cents for eggs, 2.9 cents for milk, 3.1 cents for fresh whole chicken, and 4.4 cents for ground beef. Authors' calculations based on USDA/ERS (2015a); USDA/ERS (2015b); BLS (2015); and USDA (2013a). See also Lusk and Norwood (2016).

159. Adjoian et al. (2017).

160. Arnold and Pickard (2013).

161. Corvalán et al. (2013); Jacobs (2018).

162. Sharp (2010), Chapter 12.

163. Sharp (2010), Chapter 12.

164. Robinson et al. (2014).

165. Ruby and Heine (2011); Rozin et al. (2012); Sobal (2005).

166. Lea et al. (2006).

167. House of Lords (2011); Reisch et al. (2013). Beattie et al. (2010); Tootellian and Ross (2000); Capacci et al. (2012); Dumanovsky (2011).

168. Downs et al. (2013).

169. USDA and HHS (2015).

170. Smith (2008).

171. Hickman (2008).

172. Videl (2014).

173. Jha (2013); Hanlon (2012).

174. Maastricht University (2014).

175. Bello (2013).

176. Purdy (2017).

177. Impossible Foods (2017).

178. Oregon State University (2015).

179. Gewin (2017).

180. Henry (2015).

181. Close (2014).

182. Black (2014); Mushroom Council (2016).

183. *Economist* (2015).

184. Nasdaq Global Newswire (2015).

185. Upadhyay (2016).

186. MarketLine (2014).

187. House of Lords (2011).

188. See, e.g., Health Care without Harm (2017); Friends of the Earth (2017).

189. Humane Society International (2013).

190. Ecorys (2014); Thow et al. (2014); Nordström and Thunström (2009); Hawkes (2012); Thow et al. (2010); Jensen and Smed (2013); Colchero et al. (2016).

191. Thow and Downs (2014); Nordström and Thunström (2009); Hawkes (2012).

192. Gallet (2010).

193. USDA/ERS (2015b).

194. Haley (2018).

195. Dumortier et al. (2012).

196. *Economist* (2012).

197. Gallet (2010).
198. Springmann, Mason-D'Croz, et al. (2016).

199. Dong and Lin (2009).

200. Pearson-Stuttard et al. (2017).

201. Although FAO has cited this 10% figure in some publications, its own published estimates of global fuelwood amount to only about 3%. For example, the 2008 fuelwood harvest reported by FAO in its 2011 *State of the World's Forests* report amounted to 1.87 billion cubic feet, which by conventional conversion factors should contain roughly 17.5 EJ, relative to 2010 global energy demand of around 500 EJ.

202. Many papers have estimated that charcoal production is a major source of forest degradation. For example, Sedano et al. (2016) estimated that charcoal production in Mozambique had effects on forest carbon comparable to those of agricultural expansion, and fuelwood and charcoal production are the main components of forest degradation in Africa generally. Hosonuma et al. (2012); Chidumayo and Gumbo (2013). See Vinya et al. (2011), which focuses on Zambia, as an example of REDD+ (Reduced Emissions from Deforestation and Forest Degradation) plans that try to address charcoal production.

203. Nijhuis (2017).

204. For a good graphic display of the recovery of European forests, see Noack (2014). For the global decline of conventional forms of bioenergy (the vast majority of which occurred in industrialized countries), see Krausmann et al. (2013). For a discussion of the role that declining wood use for energy had on the recovery of eastern U.S. forests, see Hanson et al. (2015).

205. EIA (2013).

206. EIA (2013).

207. EIA (2013).

208. 1 exajoule = 238,902,957,619,000 kilocalories.

209. Authors' calculations based on biofuel production estimates from the U.S. Energy Information Agency, published information from diverse sources of feedstocks for ethanol and biodiesel in different countries, standard conversion factors for estimating energy in different crops, and data from FAOSTAT on total 2010 crop production. These calculations do not include the portion of food crops that produce useable by-products.

210. A 10% transportation fuel target would generate 2.5% of global final delivered energy on a gross basis, but because ethanol and biodiesel production requires more energy to produce than gasoline and diesel, the net gain would probably be no more than 2%.

211. GlobAgri-WRR model.

212. Haberl et al. (2010).

213. This calculation assumes 395 bushels of maize per hectare (equivalent to about 160 bushels per acre), 2.8 gallons of ethanol per bushel, and thus 1,106 gallons per hectare. It also assumes that 30% of the maize traditionally enters the animal feed supply chain as an ethanol by-product. This implies that 0.7 hectares produce the 1,106 gallons, and thus a full hectare would produce 1,580 gallons, which rounds up to 1,600 gallons.

214. The yields used by the EPA are described in Plevin (2010).

215. To match the yields of maize ethanol, perennial grasses must achieve yields of 16 metric tons of dry matter per hectare per year (t/ha/yr), and very high conversion efficiencies of 100 gallons (376 liters) per metric ton. Hudiburg et al. (2014) estimates that replacing maize and soybean rotations with switchgrass in the United States would achieve yields of 9.2 tons per hectare and miscanthus would achieve yields of 17.2 tons per hectare. The EPA estimated average switchgrass yields of 8.8 tons per hectare (Plevin [2010]), and average switchgrass yields today are 4.4 t/ha/year to 8.8 tons per hectare (Schmer et al. 2010). Although miscanthus might achieve slightly higher yields that maize, it presents a number of challenges, including reproducing rhizomatically, so that new rhizomes must be dug up and separated from existing plants and replanted elsewhere.

216. Searle and Malins (2014).

217. Searle and Malins (2014).

218. International Energy Agency Statistics (2018).

219. Chum et al. (2011); Creutzig et al. (2014).

220. The International Energy Agency, for example, has called for supplying 20% of global energy by 2050 with biomass. Although ambiguous, the IEA (2017) encourages this goal on top of any traditional uses of biomass for fuelwood.

221. This 20% is based on projected global energy use of around 900 EJ in 2050 (OECD 2011). Although 20% of 900 is only 180, biomass cannot be used as efficiently as fossil fuels, so more biomass would be needed. Exactly how much would depend on the form in which the bioenergy or fossil fuel is used. Primary energy measures the energy in the original fuel, e.g., wood or crude oil. Delivered energy is the energy in a useable form, such as electricity, gasoline, or ethanol. There is substantial loss of energy in the conversion process because of the energy needed to mine, produce, or refine feedstocks into liquid fuel, or the energy lost (primarily through waste heat) in turning a fuel into electricity. Our assumption is that, overall, the conversion of biomass into delivered energy would occur at 80% of the efficiency of fossil fuels, which is optimistic for bioenergy. This calculation looks at the total amount of useable energy generated, such as the energy in ethanol, versus the total biomass and fossil energy used to generate it. For calculations of the relative efficiency of converting crude oil and biomass into useable energy forms, see JRC (2011), chapter 9.
222. IPCC (2014); Rogelj et al. (2018).

223. Technically, this estimate is based on estimates of total biomass harvest for the year 2000 estimated in Haberl et al. (2007). These harvests in dry tons of biomass are multiplied by an average energy content of 18.5 GJ per ton of dry matter. There has probably been some modest growth since that time. A similar figure was used by the IPCC in Chum et al. (2011).

224. Malhi et al. (2002). Failing to maintain these carbon stocks by adding more carbon from new plant growth, as microorganisms consume and release the carbon to the air from old biomass, would increase atmospheric carbon dioxide concentrations and contribute to climate change.

225. Searchinger (2010).

226. Moomaw and Moreira (2001), Table 3.31.

227. Souza et al. (2015); van Vuuren et al. (2009); Hoogwijk et al. (2005).

228. Papers estimating the effects of bioenergy in woody savannas while counting the lost carbon have consistently found long periods before bioenergy would reduce emissions, and include Fargione et al. (2008); Gibbs et al. (2008); and Searchinger, Estes, Thornton, et al. (2015).

229. Smeets et al. (2007); Davis et al. (2012); Cowie et al. (2018).

230. See papers summarized in Searchinger et al. (2018). Researchers have made this finding while analyzing a broad range of forest types and a broad range of harvesting regimes. See, e.g., Holtsmark (2012); Hudiburg et al. (2011); Manomet Center for Conservation Sciences (2010); and Mitchell et al. (2012). The basic reason that harvesting forests for bioenergy leads to a carbon debt is that each ton of carbon in a forest that is harvested only leads to a quarter to a third of a ton of carbon savings in its typical use for electricity generation. This is because (a) some of the live carbon in roots and branches is left behind to decompose, and (b) burning wood is less efficient than burning fossil fuels to generate electricity. In addition, young or middle-aged forests, which are most frequently harvested in commercial operations, would typically grow faster and therefore accumulate more carbon for at least some years than a newly regrowing forest, which starts with seedlings or natural regeneration. That factor increases the carbon debt of using trees for energy. Eventually, forests that are not cut reach slow rates of growth, and regrowing forests will start to catch up. Eventually, the GHG reductions from reduced fossil fuel use will equal and ultimately exceed the increase in carbon in the air from the transfer from the forest. At that point, there are GHG benefits. But governments that have explicitly recognized and addressed this accounting have generally agreed to account for these bioenergy impacts in periods of 20 or 30 years, up to which bioenergy leads to likely emissions increases.

One source of confusion in this analysis lies in the difference between the rate of uptake in any given year and the total carbon stored. Cutting a mature forest will typically increase the rate of car-

bon uptake (at least over many decades as a new forest grows), but at the expense of a large initial loss of carbon. And it will still result in a net release of carbon from the forest, and even a net release of carbon for decades, even when accounting for the reductions in fossil carbon emissions from the bioenergy use.

231. The exception is in cases where a tract of land generates biomass feedstocks that are additional to what otherwise would have grown, such as if a farmer were to plant winter cover crops during fallow seasons on some cropland and use those crops for bioenergy. Searchinger et al. (2018).

232. Agostini et al. (2014).

233. Although bioenergy is not typically called an "offset," offsetting is the physical mechanism by which bioenergy reduces carbon dioxide in the atmosphere. The principle of "additionality" is the same as for a regulatory offset for climate change. In regulatory systems, power plants are sometimes allowed to offset their emissions from burning coal by planting a new forest elsewhere on the theory that the carbon absorbed and stored by the additional trees offsets the carbon released from the coal. But a power plant cannot claim an offset by pointing to a forest that would grow anyway. Only additional forest growth counts for a forest-planting offset; the same principle is true for bioenergy. In effect, a forest-planting offset uses the additional plant growth to store more carbon in trees to offset fossil-based energy emissions, while bioenergy uses the additional plant growth to replace fossil fuels and leave more carbon underground. The concept of "additional" is the key to both forms of offsets.

234. Searchinger (2009); Haberl et al. (2012).

235. For more examples of bioenergy greenhouse gas accounting scenarios, see Appendix B in Searchinger and Heimlich (2015).

236. For two comparisons, see Decara et al. (2012) and Edwards et al. (2011).

237. Searchinger, Edwards, Mulligan, et al. (2015).

238. HLPE (2013); Dorward (2012).

239. See the discussion in Searchinger, Edwards, Mulligan, et al. (2015) of the IFPRI model, and the discussion in Berry (2011) of the GTAP model. Berry (2011) also includes a good discussion of the limited real economic evidence that higher demands spur yield growth.

240. See discussion in the supplement of Searchinger, Edwards, Mulligan, et al. (2015).

241. Berry (2011); Berry and Schlenker (2011).

242. Searchinger, Edwards, Mulligan, et al. (2015) discuss the IFPRI model used by the European Commission, which is structured so that the vast majority of increases in crop production to replace crops diverted to biofuels results from additional yield increases by farmers. Even so, estimated greenhouse gas reductions from grain-based biofuels are modest.
243. Dumortier et al. (2011), for example, assumed that expanded cropland in the United States and much of the world would first use idle cropland, which is the equivalent of assuming that expanded crop production results from an increase in cropping intensity (the percentage of cropland cropped in a given year).

244. World croplands continually shift and truly abandoned cropland regenerates carbon. There is also a category of land that comes in and out of crop production in part in response to fluctuations in demand and yields and in part in response to physical limitations on crop growth every year. Those fluctuations will continue to exist in a future with more biofuels, and that means there will always be this kind of cropland that comes in and out of production. The argument that biofuels will use this cropland confuses a structural change in demand with the effect of annual fluctuations in the demand for cropland. E4tech (2010) made similar assumptions for European biofuel production.

245. This assumption, for example, was implicitly built into the regulatory analysis by the EPA for its biofuel greenhouse gas regulations, and was also part of the assumptions in E4tech (2010). It derives from satellite studies that identify extensive "savanna" in Indonesia, while the savannas in Indonesia are in fact originally forest that has been cut and is typically in some kind of mosaic use if not in some level of reforestation.

246. Searchinger et al. (2017) review 12 modeling analyses of BECCS. In 9 of them, biomass is automatically treated as carbon neutral and effects on terrestrial carbon storage are not counted. In 3 models, the modelers project potential but only at high cost and only based on a number of unlikely conditions, including that governments worldwide perfectly protect forests and other high-carbon lands. The combination of this protection and high bioenergy demand saves land in the different models either because the cost of ruminant products rises so high that hundreds of millions of hectares of grazing land are converted to bioenergy or because governments also spend large sums of money to intensify agricultural production on existing agricultural land.

247. Searchinger et al. (2017).

248. When starches are fermented into ethanol, one gram of carbon is lost for each two grams of carbon in the ethanol, as shown in Searchinger, Edwards, et al. (2015).

249. Haberl et al. (2010).

250. Haberl et al. (2010) discusses the various estimates.

251. The Haberl et al. (2011) estimate is of 25 EJ of unused residues, which could generate 12.5 EJ of transportation biofuels according to high conversion efficiency estimates.

252. Smil (1999) provides a compelling analysis of the uses and needs for crop residues worldwide. Even in the United States, Blanco-Canqui and Lal (2009) found that at least in a part of the U.S. maize belt, the removal of residues resulted in substantially negative effects on maize yields.

253. Liska et al. (2014). Many studies have estimated conditions under which the removal of residues might not reduce soil carbon, but the more salient factor is the difference in soil carbon with and without the residues. If the residues would add to soil carbon, then their removal reduces carbon sequestration.

254. Author's calculations based on data from FAOSTAT and assumption that all tops and branches are available and equal to 30% of harvested roundwood.

255. Booth (2018).

256. Haberl et al. (2010).

257. IEA (2017).

258. Haberl et al. (2010).

259. Kladivko et al. (2014).

260. Hughes et al. (2012).

261. NRC (2012), 2.

262. Wigmosta et al. (2011). The water challenge exists in large part because algal biofuel production is expensive (estimated at US$300–US$2,600 per barrel in 2010, in Hannon et al. 2010), and strategies to achieve a reasonable cost require production in open ponds, from which much water evaporates. Although some other estimates of potential water use are lower, nearly all still estimate the need for large quantities, according to the NRC (2012). One possibility might be to use saline waters, but NRC (2012) concluded that some fresh water would be necessary.

263. Moody et al. (2014).

264. Waite et al. (2014).

265. See note 211.

266. Kissinger et al. (2012).

267. For example, one paper estimated land-use demands to meet existing electricity production in the United States in 2005 as varying from 1% to 9% for states east of the Mississippi (Denholm and Mar-golies 2008). This figure would obviously need to increase to meet the greater electrical generation needs of 2014. But it would decline if power were imported from the sunnier, drier, and less populated states in the U.S. West, as PV conversion efficiencies grow (and they have grown greatly even since 2008) and as costs come down, which permits more dense packing of PV cells in tilted configurations.

268. Authors' calculations. These numbers require information only about the solar radiation received in an area of production, the crop or biomass yields, the quantities of biofuels per ton of crop, and the energy of the biofuel. Brazilian sugarcane ethanol numbers assume average solar radiation of 2,000 kilowatt hours per square meter per
year in Brazilian sugarcane producing areas based on Solargis global solar radiation map, which yields 72,000 GJ/ha/yr. (Map available at http://solargis.info/doc/_pics/freemaps/1000px/ghi/SolarGIS-Solar-map-World-map-en.png). It also assumes a yield of 80 metric tons of sugarcane per hectare per year, dry matter (DM) content of 27%, and an energy content of 17 GJ/tDM, for 367.2 GJ/ha/yr. If sugarcane is produced every year, then it generates a 0.51% efficiency of the energy in sugarcane relative to solar radiation, but if it is produced only seven of eight years to factor in replanting, the efficiency is 0.45%. Assuming 75 liters per ton of sugarcane and 23.4 MJ/l, that results in 140 GJ/ha/yr of energy in ethanol. The result is 0.19% assuming both annual production and 100% of fossil fuels used in production are offset by an electricity energy credit from burning sugarcane bagasse. The energy in the biomass other than sugar, the bagasse, is therefore counted in this net calculation.

269. For maize ethanol grown in Iowa, the figures are around 0.3% into biomass and 0.15% into ethanol, even when fully accounting for the feed by-product. These figures, calculated for Iowa, assume 9.7 tons per hectare (180 bushels per acre) of maize, 487 liters of ethanol per ton (2.8 gallons per bushel), a 35% reduction in land-use estimates to recognize feed by-product, 23.4 MJ/liter of ethanol, and solar radiation of 1,600 kWh per square meter per year (~57,500 GJ/hectare/yr). This calculation also assumes optimistically that the net energy yield of maize ethanol is 50% after accounting for all the energy used in its production.

For cellulosic ethanol, using the highest projected future switchgrass yield by the Department of Energy at any point in the United States of 24 tCM/ha/yr, Geyer (2013), and the assumption of 100 gallons of ethanol per ton of dry matter implies a conversion efficiency of solar radiation into fast-growing grasses of perhaps 0.7%, and into ethanol of 0.35%. (Switching to less productive land would reduce this efficiency because the solar energy would remain generally the same or grow but the biomass output would decrease.)

270. Calculations for rooftop solar and solar farms differ. This figure for rooftop solar assumes a 16% photovoltaic cell, a 20% loss in actual operation of a rooftop solar installation, including losses from conversion of DC power to AC power and a further 11% cost for paying back the energy used to construct and install the system. Photovoltaic efficiencies and payback times are from Fthenakis (2012), and the 20% efficiency loss is based on typical conversion cost figures using the PVWatts calculator website by the National Renewable Energy Laboratory of the U.S. Department of Energy (http://pvwatts.nrel.gov/pvwatts.php).

271. See the explanation in the following note.

272. This calculation was originally performed for Installment 4 in this series, "Avoiding Bioenergy Competition for Food Crops and Land" (Searchinger and Heimlich 2015) and was subsequently published in peer-reviewed literature in Searchinger et al. (2017). The supplement to that paper explains this calculation as follows:

The global solar energy vs bioenergy comparative calculation was based on a GIS (geographic information systems) analysis, which compared the net energy output of potential bioenergy production against the output of photovoltaics. The area analyzed excluded area covered permanently by ice and the driest deserts because such areas could not produce bioenergy although they could produce solar energy.

Biomass production was estimated by cell using a modified version of the LPJmL model (Beringer et al. 2011; Searchinger, Estes, Thornton, et al. 2015) that simulates energy crop productivities comparable to net primary productivity (NPP). This model adjusts LPJmL biomass production to match the NPP of the native vegetation of a cell. In general, agricultural biomass production rarely exceeds that of native vegetation (Field et al. 2008; Haberl et al. 2013). We further assumed production of 379 liters per metric ton of biomass as discussed above, and that all energy used to produce and transport biomass and refine it into ethanol would be either provided by the biomass itself or offset by electricity by-products. Using ethanol, these assumptions imply that 47 percent of the gross energy in the biomass becomes useable energy.

For PV production, this analysis used a global data set of Global Horizontal Irradiance (GHI) available from the U.S. National Renewable Energy Laboratory. The GHI is the total solar radiation received by a horizontal PV cell and is a weighted sum of the Direct Normal Irradiance (DNI) and the diffuse light (all sunlight that comes to the panel from other areas of the sky except the narrow beam from the sun. (https://eosweb.larc.nasa.gov/sse/global/text/22yr_swv_dwn).

We used a net efficiency of 10% for solar radiation. This efficiency is based on the 17% PV efficiency of standard PV cells today, and an 85% performance ratio (halfway between standard 80% and 90% ratios today) (ISE 2016), plus our estimate from above that 11% of the energy generated by the PV is used to pay back the energy used to produce and install the PV. We then further assume a coverage factor of 78%. As noted above, coverage factors can vary greatly for PV in practice, in part because PV is typically installed on infertile land, for which land area needs are not a concern. As the primary purpose of this analysis is to compare PV on land that might grow bioenergy reasonably well, we assume that some effort would be made not to use land unnecessarily. Where tilting is still desired, for example, solar arrays, can be spaced to allow grazing to occur between arrays, and as they become cheaper, tilting becomes less important. With space constraints varying from 50% to nearly full coverage, we deliberately chose 78% in part to generate an even 10% to avoid creating a false sense of precision in this analysis.
Although we are counting energy used for PV to obtain net efficiencies, we are not incorporating production energy use into the efficiencies for bioenergy.

This analysis calculated that on 73% of the world's land, the useable energy output of PV would exceed that of bioenergy by a ratio of more than 100 to 1. For the remaining quarter of the world's land, the average ratio is still 85 to 1, and the lowest ratio is 40 to 1. This relatively "better" land for bioenergy consists primarily of areas whose native vegetation would have been dense forest, and which today includes the world's densest remaining tropical forests and the North American and European areas of the world's best farmland. This land is therefore the land most valuable for carbon storage, food, and timber. If energy production chose from the top 25% of land with the highest efficiency advantage for PV, the minimum ratio of PV to bioenergy production would be 5,000 to 1.

This analysis should be viewed only as illustrative. At finer resolution, much land, such as some steeply sloped land, would be suitable neither for biomass production nor for PV.

273. See Table 3 in Searchinger et al. (2017).

274. Searchinger et al. (2017).

275. Calculations are shown in Searchinger et al. (2017).

276. These numbers actually understate the real differences in efficiency for three reasons. First, the cellulosic ethanol figures compare solar PV conversion efficiencies in commercial operation today with ethanol production that assumes large future improvements both in growing grasses or trees and in refining them into ethanol. Although progress in cellulosic ethanol has been slow, increases in solar PV conversion efficiencies have been proceeding at a rapid rate, and if and when cellulosic bioenergy achieves the efficiencies we cite, PV land-use efficiencies will very likely have grown as well.

Second, solar cells do not require land with plenty of water and good soils. Because of the increases in global demand for food and timber, highly productive lands are already needed for these uses, not for energy generation. On less fertile land, the efficiency of bioenergy drops greatly, but the efficiency of converting the sun's rays to electricity via solar PV is unchanged. And the overall performance and economics of solar PV would even improve if the less fertile land has more solar radiation per square meter than more fertile lands—for example, the U.S. desert West relative to the U.S. maize belt. Even assuming high future cellulosic yields, PV systems available today would generate more than 100 times the useable energy per hectare over a majority of the United States. Because the United States has highly productive agriculture, it is reasonable to assume that this figure would be equally true of the globe.

Third, at least for transportation, shifting to solar implies even greater efficiency gains. Internal combustion engines convert at best around 20% of the energy in either fossil fuels or biofuels into motion, while electric engines today convert around 60%, a threefold increase. Today, much of that increased efficiency is lost by the high energy needs for building car batteries. But if battery production can become more energy efficient and batteries longer lasting, a combination of solar energy and electric engines could become 200–300 times more land-use efficient than biofuels.

277. IEA (2014).

278. See HLPE (2013).

279. Steenblik (2007); Koplow (2007); Koplow (2009).

280. Sperling and Yeh (2010).

281. Searchinger, Edwards, Mulligan, et al. (2015).

282. Kitzing et al. (2012).

283. IEA (2013); Brack and Hewitt (2014).

284. Bernier and Paré (2013); Holtsmark (2012); Hudiburg et al. (2011); McKechnie et al. (2011); Mitchell et al. (2012); Manomet Center for Conservation Sciences (2010); Zanchi et al. (2012).

285. Authors' calculations using FAO (2019a). This figure is calculated by using FAO's total reported timber harvest, using conversion factors to estimate their energy content, and comparing them to estimates of global energy consumption. See also a February 9, 2015, letter to U.S. EPA Administrator Gina McCarthy by more than 70 scientists, available at http://www.caryinstitute.org/sites/default/files/public/downloads/2015_ltrCarbon_biomass.pdf.

286. Massachusetts regulations can be found at http://www.mass.gov/eea/docs/doer/rps-aps/rps-class-i-regulation-225-cmr-14-00.pdf. The approach properly calculates both the savings in fossil fuel carbon and the reductions, and therefore emissions, from harvesting trees and calculates the balance over a period of 20 years.

287. Searchinger et al. (2009). See Appendix A in Searchinger and Heimlich (2015).

288. For different estimates, see Mallory et al. (2012); Tyner (2010); and Abbott (2012).

289. Abbott (2012).

290. UNDESA (2017). Total population by major area, region, and country ("medium-fertility variant" or medium growth scenario).

291. The fertility rate refers to the number of children per woman. More specifically, the total fertility rate is "the average number of children a hypothetical cohort of women would have at the end of their reproductive period if they were subject during their whole lives to the fertility rates of a given period and if they were not subject to mortality" (UNDESA 2017).
292. Statistics New Zealand (2013). http://www2.stats.govt.nz/domino/external/omni/omni.nsf/wwwglsry/replacement+level.

The level allows for the sex ratio at birth (roughly 105 males born for every 100 females) and for some mortality of females between birth and childbearing. The actual replacement level will vary slightly from country to country and over time depending on the sex ratio at birth and mortality rates.

293. UNDESA (2017). Total population by major area, region, and country (medium-fertility variant).

294. For studies showing strong statistical correlations between declines in fertility and increases in girls' education, increased access to family planning, and declines in infant mortality, see Shapiro and Gebreselassie (2008); Leeson and Harper (2012); and Upadhyay and Karasek (2010).

295. The correlation between female education, a decrease in fertility rates, and an increase in contraceptive use is well documented in both developed and developing countries, including across sub-Saharan Africa. For example, see Shapiro and Gebreselassie (2008); Bbaale and Mpuga (2011); and Bloom and Canning (2004). Shapiro and Gebreselassie (2008); Bloom and Canning (2004); and Bbaale and Mpuga (2011) also show that education is correlated with declining family size and increased use of condoms in Uganda.

296. Ethiopia Central Statistical Agency (2017).

297. Schmidt et al. (2012).

298. Chaaban and Cunningham (2011).

299. Shapiro and Gebreselassie (2008); Bongaarts (2005); Bbaale and Guloba (2011); Bbaale and Mpuga (2011).

300. UNDESA (2015). According to Sing and Darroch (2012), 58 million women in Africa—including 53 million in sub-Saharan Africa—would like to space or limit their next birth but do not use contraception.

301. WHO and UNICEF (2013).

302. Shapiro and Gebreselassie (2008) found that in 24 sub-Saharan African countries, progress in women's education and reductions in infant and child mortality were the key factors contributing to sustained declines in fertility rates since the early 1990s. They also found that in countries where these education and mortality indicators had stopped improving or were deteriorating, fertility rates tended to stall instead of drop further. Hossain et al. (2005) found that reduced mortality rates correlated with reduced fertility in Bangladesh.

303. WHO (2013a); WHO (2013b); CIA (2013).

304. Pool (2007).

305. Bloom et al. (2003); Bloom and Williamson (1998); Bloom et al. (2000); Mason (2001). Locations where the demographic dividend contributed to economic growth include Hong Kong, Malaysia, Singapore, South Korea, Taiwan, and Thailand.

306. Sippel et al. (2011).

307. According to UNDESA (2017), the "low variant" simply uses a projection of 0.5 children below the fertility rates in the "medium variant" over most of the projection period—starting from a downward adjustment of -0.25 child from 2015 to 2020, -0.4 child from 2020 to 2025, and -0.5 child from 2025 onward.

308. UNDESA (2017). This replacement level (2.16) is higher than 2.1, likely due to higher-than-global-average rates of female infant and child mortality in sub-Saharan Africa.

309. To estimate the lower population under this scenario, we made several assumptions. We first compared UNDESA's (2017) "high-fertility" scenario for sub-Saharan Africa (3.7 fertility rate) to its "medium-fertility" scenario (3.2 fertility rate) and found a difference in population of 226 million people in 2050 between the two scenarios. We then compared the "medium-fertility" scenario to the "low-fertility" scenario and found a difference in population of 216 million people. We then assumed that the population difference between a "low-fertility" and a "replacement fertility" scenario with a 2.16 fertility rate would be proportional to the differences between "high fertility" and "medium fertility," and between "medium fertility" and "low fertility," estimating a population difference of 230 million people between "low fertility" and "replacement-level fertility." We distributed that population difference of 230 million people (across sub-Saharan Africa) proportionally across all countries. The "replacement-level fertility" scenario led to a population of 1.8 billion in sub-Saharan Africa in 2050—very similar to an earlier estimate of 1.76 billion by the Oxford Institute of Population Ageing for a paper earlier in this report series (Searchinger, Hanson, Waite, et al. 2013).

310. Authors' calculations from GlobAgri-WRR model. Projected growth in crop calorie production in sub-Saharan Africa between 2010 and 2050 is approximately 1,840 trillion kcal.

311. Robinson (2016) notes that two-thirds of sub-Saharan African countries adopted national population policies since the 1980s to reduce population growth, and that countries with such policies experienced statistically greater declines in fertility rates between 1987 and 2002 than those without.

312. Shapiro and Gebreselassie (2008). The 14 countries with a declining trend in national total fertility rates between the 1990s and mid-2000s were Benin, Burkina Faso, Chad, Côte d'Ivoire, Eritrea, Ethiopia, Madagascar, Malawi, Namibia, Nigeria, Senegal, Togo, Zambia, and Zimbabwe. Three countries (Mali, Niger, and Uganda) were classified as "pre-fertility transition," with no significant declines in fertility rates; and seven countries were classified as "stalled fertility decline," with fertility rates that had initially declined but then stalled during the study period. Across all countries, fertility declines tended to be stronger in urban areas.

313. UNDESA (2013). Since this 2013 publication, the United Nations has twice revised sub-Saharan Africa's projected population upward (in 2015 and 2017), presumably for the same reasons.

314. World Bank (2011a).
315. Athena Ballesteros (senior associate, Institutions and Governance Program, World Resources Institute), personal communication, July 12, 2013.

316. Oxfam (2007); Inter Press Service (2016).

317. Pingali et al. (2008); Lynd et al. (2015); Lynd and Woods (2011).

318. HLPE (2011); Wright (2014).

319. Oxfam (2012); ActionAid (2012).

320. Pingali et al. (2008).

321. Lau et al. (2015).

322. World Bank (2008).

323. Dorward (2013).

324. World Bank overview of poverty numbers, available at http://www.worldbank.org/en/topic/poverty/overview.

325. Filipski and Covarrubia (2010); Jayne et al. (2010).

326. Dorward (2012); Ivanic et al. (2011).

327. Headey (2014); Jacoby (2013); Ivanic and Martin (2014).

328. FAO (2012b), 104, Fig. 33; van Ittersum et al. (2016).

329. See discussion in Searchinger, Edwards, Mulligan, et al. (2015). When there are independent (exogenous) sources of growth in demand for crops, crop prices rise as a consequence. These price increases stimulate additional supply and production by farmers, and reduced demand and less consumption by preexisting consumers. The percentage of the crops that are not replaced due to reduced consumption depends on the ratio between these responses, which is reflected in the ratio of the supply and demand elasticities. Although there is some uncertainty about those elasticities, estimates of individual crop supply and demand elasticities usually place the demand response as lower than the supply response but still a substantial fraction of that response. For typical estimated supply and demand elasticities, see elasticities referenced in Hochman et al. (2014). One paper (Roberts and Schlenker 2013) estimated the global supply and demand responses for calories from the world's major crops and indicated that 20% of crops going to satisfy new demands will not be replaced.

330. HLPE (2011); Muhammad et al. (2011).

331. Bobenrieth et al. (2013). The trends in crop prices discussed in this paper provide an excellent example and reflect the fact that demand growth that results in temporary shortages has a greater price effect in the short term than the long term. See also discussion in Wright (2014) and in the supplement to Searchinger, Edwards, Mulligan, et al. (2015).

## REFERENCES

To find the References list, see page 500, or download here: www.SustainableFoodFuture.org.

## PHOTO CREDITS

Pg. 49 istockphoto, pg. 50 istockphoto, pg. 64 Ella Olsson, pg. 70 istockphoto, pg. 89 Flickr/Lyza, pg. 96 Pexels/Igor Haritanovich, pg. 114 Flickr/Kiran Jonnalagadda, pg. 116 Mokhamad Edliadi/CIFOR, pg. 121 Flickr/H6 Partners, pg. 123 Flickr/Tushar Dayal, pg. 126 Flickr/Guillén Pérez
<figure><img src="image_90.png" /></figure>
## COURSE 2

# Increase Food Production without Expanding Agricultural Land

In addition to the demand-reduction measures addressed in Course 1, the world must boost the output of food on existing agricultural land.

To approach the goal of net-zero expansion of agricultural land, improvements in crop and livestock productivity must exceed historical rates of yield gains. Chapter 10 assesses the land-use challenge, based on recent trend lines. Chapters 11–16 discuss possible ways to increase food production per hectare while adapting to climate change.

## TABLE OF CONTENTS

<table><tr><td>Chapter 10. Assessing the Challenge of Limiting Agricultural Land Expansion</td><td>147</td></tr><tr><td>Chapter 11. Menu Item: Increase Livestock and Pasture Productivity</td><td>167</td></tr><tr><td>Chapter 12. Menu Item: Improve Crop Breeding to Boost Yields</td><td>179</td></tr><tr><td>Chapter 13. Menu Item: Improve Soil and Water Management</td><td>195</td></tr><tr><td>Chapter 14. Menu Item: Plant Existing Cropland More Frequently</td><td>205</td></tr><tr><td>Chapter 15. Adapt to Climate Change</td><td>209</td></tr><tr><td>Chapter 16. How Much Could Boosting Crop and Livestock Productivity Contribute to Closing the Land and Greenhouse Gas Mitigation Gaps?</td><td>221</td></tr></table>
<figure><img src="image_91.png" /></figure>
ASSESSING THE
CHALLENGE OF LIMITING
AGRICULTURAL LAND
EXPANSION

How hard will it be to stop net expansion of agricultural land?

This chapter evaluates projections by other researchers of
changes in land use and explains why we consider the most
optimistic projections to be too optimistic. We discuss estimates
of "yield gaps," which attempt to measure the potential of farmers
to increase yields given current crop varieties. Finally, we examine
conflicting data about recent land-cover change and agricultural
expansion to determine what they imply for the future.
## The Challenge

The baseline scenario we use to define our “gaps” assumes the continuation of crop and pasture yield gains similar to those achieved in the 50 years since the Food and Agriculture Organization of the United Nations (FAO) first began estimating global yields in 1961. But even achieving such baseline yield gains will be difficult because many of the major transformative factors that drove yield gains for these decades—a period that encompassed the Green Revolution—have already been heavily used. For cropland, these transformations have come in three areas:

* **Fertilizers.** Farmers worldwide used very little synthetic fertilizer in 1960. Today, most of the world heavily exploits synthetic fertilizers, and some countries apply far more than needed. Only sub-Saharan Africa as a region uses little fertilizer, and it could make large gains by applying more.¹

* **Irrigation.** From 1962 to 2006, irrigation area roughly doubled.² However, because few additional areas remain that can plausibly be irrigated with available water, FAO projects that irrigated land will expand by only an additional 7 percent between 2006 and 2050.³

* **Seeds.** In 1962, most of the world used seeds improved only by farmers. But in the subsequent five decades, much of the world adopted scientifically bred seeds, although use of improved seeds remains low in Africa.⁴

Although technology is still improving, the agricultural community will have a hard time matching the effect of introducing—for the first time—such fundamental technologies as fertilizers, irrigation, and scientifically bred seeds.

A major factor in the improvement of pasture and the efficiency of livestock production has been the replacement of animal power with fossil fuel power. In much of the world, even in 1960, animal power played a major role in agriculture and transportation. Switching to fossil fuels reduced the need for vast areas of pasture that would have been devoted

to grazing and growing feed for animals. Fossil fuels also reduced the energy and therefore feed burden on multipurpose animals, allowing them to use the energy in their feed exclusively for building weight or producing milk rather than for producing power. Although the effects of these transformations have been quantitatively estimated carefully in only a few countries,⁵ these transformations have occurred worldwide to a greater or lesser extent.

The shifting of agricultural production toward developing countries presents another yield challenge. Because food demand is growing mostly in these countries, and most of the demand will be met through domestic production rather than through imports, the share of global cropland located in developing countries is projected to grow. Average yields in those countries currently are lower than they are in the developed world. This shift in cropland toward developing countries thus will drag down global average yields until developing world yields catch up. For example, even if annual maize yields were to roughly triple in East Africa between 2010 and 2050, every additional hectare produced in Africa would still generate only slightly more than half the yield that a U.S. hectare produced in 2010.⁶

Even matching historical rates of yield growth overall will not be enough. Absent efforts to reduce growth in food demand, the amount of absolute growth in annual food production that will be needed each year from 2010 to 2050 is larger than the increase in food production that was achieved each year in the previous 50 years. And between 1962 and 2006, even though yield growth supplied 80 percent of all the growth in crop production (measured by weight), cropland area still expanded by 220–250 million hectares (Mha), equivalent to roughly 30 percent of the continental United States and more than total U.S. cropland.⁷ Demand for milk and ruminant meat is also likely to grow at a substantially faster rate in the next four decades than it did in the previous five decades.⁸ Therefore, going forward, both crop output per hectare and milk and meat output from ruminants per hectare must grow each year more than they did historically if we are to avoid net land-use expansion.
<table><caption>**Table 10-1** | Selected projections of future agricultural land requirements</caption><thead><tr><th>FEATURE</th><th>OECD / IMAGE</th><th>FAO</th><th>GLOBIOM</th><th>BAJZELJ ET AL.</th><th>TILMAN ET AL.</th><th>GLOBAGRI-WRR (THIS REPORT)</th></tr></thead><tbody><tr><td>Time period</td><td>2010-50</td><td>2006-50</td><td>2000-50</td><td>2009-50</td><td>2005-50</td><td>2010-50</td></tr><tr><td>Cropland</td><td>-8 Mha</td><td>+69 Mha</td><td>+266 Mha</td><td>+655 Mha</td><td>+1,000 Mha</td><td>+192 Mha</td></tr><tr><td>Pastureland</td><td>-52 Mha</td><td>N/A</td><td>+121 Mha</td><td>+426 Mha</td><td>N/A</td><td>+401 Mha</td></tr><tr><td>Natural ecosystems</td><td>N/A</td><td>N/A</td><td>-503 Mha gross</td><td>N/A</td><td>N/A</td><td>-593 Mha net</td></tr><tr><td>Comment</td><td>Cropland increase of 110 Mha from 2010 to 2030, but net decline of 8 Mha by 2050</td><td>Cropland increase of 107 Mha in tropics, offset by decline of 48 Mha in temperate zone<br>Projection on low side because 2050 UN population projections have since grown by 0.6 billion people</td><td>Decline in natural ecosystems offset by 103 Mha of plantation forest growth</td><td>Based on the continuing growth of crop and pasture yields at historical rates</td><td>Extrapolation from current trend lines in yield growth, income growth, and demand for crop calories</td><td>See Chapter 2 for assumptions</td></tr></tbody></table>

*Note:* N/A signifies that data are not available or not discussed in the respective study.

*Sources:* GLOBIOM analysis prepared by Schneider et al. (2011); FAO projection from Alexandratos and Bruinsma (2012); OECD projection prepared by the Netherlands Environmental Assessment Agency and reported in OECD (2011); Bajzelj et al. (2014); and Tilman et al. (2011).

## Understanding Other Estimates of Agricultural Land Expansion

As with our own GlobAgri-WRR projections, most other agricultural modeling teams project large growth in agricultural area in their baseline 2050 scenarios (Table 10-1). Schmitz et al. (2014) compared 10 separate agro-economic models of cropland expansion, using similar population assumptions to ours. Six of the 10 model results projected an amount of cropland expansion at least as large as that in our baseline while only one projected a decrease.⁹ Similarly, five of the eight economic models that made pasture area projections estimated increases in pasture area, with the largest estimate of approximately 400 Mha coming from the Global Biosphere Management Model (GLOBIOM) runs used at the time.¹⁰

Noneconomic models and projections using recent trend lines tend to predict even larger expansion of agricultural land. For example, Bajzelj et al.

(2014) estimate a total of 1.1 billion ha of cropland and pastureland expansion between 2009 and 2050,¹¹ Tilman and Clark (2014) project a 600 Mha increase in cropland alone, and an earlier projection of cropland expansion by Tilman et al. (2011) was even larger, at roughly 1 billion ha (in part due to substantially higher meat demand projections at the time).¹²

Some analyses are much more optimistic. Of the agro-economic models compared in Schmitz et al. (2014), one projected a decline in cropland area, and three models projected declines in pastureland.¹³ A 2011 modeling analysis by the Organisation for Economic Co-operation and Development (OECD) using the Integrated Model to Assess the Global Environment (IMAGE) predicted a very small decline in cropland area by 2050, despite increases until 2030.¹⁴ The FAO projection in 2012 foresaw only modest net cropland expansion of 69 Mha.
Although no one can know for certain what future
growth will be, we consider key parts of the analyses
underlying the more optimistic baseline projections
to be too optimistic because of their reliance on out-
of-date population estimates or overly optimistic
yield growth estimates.

Estimates of Yield Growth May Be
Overly Optimistic

**Population estimates.** Some of the more optimistic projections are now out of date because population projections have been revised upward since the original analysis was completed. For example, the 2012 FAO projection used UN population projections of 9.1 billion for 2050, while the most recent midlevel UN projections estimate 9.8 billion people by 2050.¹⁵ As a result, the amount of projected population growth between 2010 and 2050 is now nearly one-third higher than previously estimated. Because we use FAO projected yields in 2050 and account for the larger food demands of a higher population, our cropland expansion estimates are higher.

**Yield growth estimates.** Some models assume faster yield growth than others. On balance, the FAO estimates that we use project yield gains from 2006 to 2050 at roughly the same rates as those achieved from 1962 to 2006 in terms of absolute annual increases in production (additional kilograms per hectare per year [kg/ha/yr] relative to the immediately preceding year).¹⁶ By contrast, the OECD/IMAGE projection, citing essentially stable cropland (Table 10-1), projects yield growth by 2050 that is 25 percent higher than forecast in the 2012 FAO projections. Although no one can legitimately predict the future with high confidence, we are skeptical of very high growth rates in crop yields or meat and dairy output per hectare of grazing land, for a number of reasons that we discuss in the subsections below.

Use of compound (instead of linear) crop
growth rates

Some projections have mistakenly assumed that yields have percentage growth rates that compound each year, instead of growing in a linear fashion.¹⁷ Compound, or exponential, growth rates are like bank interest: to generate the same *percentage* growth in yield over time, the *absolute* increase in yield must get larger each year.¹⁸ However, crop yields have usually grown linearly. The global yield of cereals, for example, has grown for more than 50 years at a surprisingly consistent rate on an absolute basis, with each hectare globally producing roughly 45 kg more each year than it did the previous year (Figure 10-1). Careful analyses have shown that even regional growth rates in crop yields—although they have varied by region, crop, and period—are best represented by linear growth.¹⁹ The assumption of compound growth rates by some studies has therefore led to assessments of future yields that are far too optimistic (Box 10-1).

<figure><img src="image_92.png" /></figure>
<figure><img src="image_93.png" /><figcaption>**Figure 10-1** | Global cereal yields have grown at a linear rate over the past five decades</figcaption></figure>

<figure><img src="image_94.png" /></figure>

Source: WRI analysis based on FAO (2019a).
BOX 10-1 | The significance of linear yield growth for predicting future land-use needs

A poorly grounded assumption that explains
several overly optimistic projections of future
crop yields and land-use needs is that yields
grow by a stable percentage each year. In
other words, if yields grow by 1.5 percent this
year, they will continue to grow at 1.5 percent
year after year and, like a bank account, the
growth will compound. This assumption of
compound growth leads to large absolute
yield growth over time, as illustrated by a
figure borrowed from Grassini et al. (2013),
which shows how compound growth
rates used by six separate studies led to
projections of high future yields (Figure 10-2).

In fact, as Grassini et al. (2013) also showed,
although yields grow at different rates in
different places at different times, when
yields grow, they almost always grow in
linear fashion. In other words, as illustrated
in Figure 10-2, U.S. maize yields increase by a
consistent number of kilograms per hectare
each year. Papers that use compound growth
rates are overly optimistic, such as one paper
claiming that the world had reached "peak
farmland," meaning that the world would

no longer need to expand cropland to meet
rising food needs,ª

By contrast, other papers improperly project
an alarming future by pointing out that
percentage growth rates for cereals have
been declining: they were 3 percent per year
in the 1960s and are now around "only" 1
percent. From this decline, the studies infer a
decline in technical improvements and grave
problems in the future.ᵇ But linear growth
means that the percentage growth rate
declines. When average world cereal yields
were only 1.5 tons per hectare per year (t/ha/
yr) in 1962, producing an additional 45 kg/ha
each year meant 3 percent growth. By 2017,
once world yields reached 4.1 t/ha/yr, that
same 45 kg/ha means growth closer to only
1 percent.ᶜ

Studies can also mislead when they express
future growth in demand as a compound
growth rate. Future demand growth out
to 2050, measured linearly, is going to be
larger than previous growth. Yet because
of the same fundamental math, the same
absolute increase in demand for food each

year will result in declining compound
(percentage) growth rates in demand. As
a result, using a compound growth rate for
demand can make it seem as though the rate
of growth in demand is declining. A seminal
report by the FAO, which recognized that
yield growth rates are linear, nevertheless
characterized growth in demand as declining
using compound rates.⁴ Using linear rates
correctly to characterize growth in yields
but compound growth rates incorrectly
to characterize declining growth rates in
demand can lead to a mistaken impression
that land use will not expand.

Notes and sources:

a. Ausubel et al. (2012). In this paper, the compound growth rate is complicated by the fact that the authors analyzed different contributions to yield growth, but the overall effect was to use a compound rate.

b. For example, Alston et al. (2010) include a chart showing large declines in annual crop yield growth rates from the period 1961–90 versus 1990–2007. See also Foresight (2011a).

c. Authors' calculations from FAO (2019a). This comparison is between the average yield from 1961-63 and the average yield from 2012-14.

d. Alexandratos and Bruinsma (2012).

<figure><img src="image_95.png" /><figcaption>**Figure 10-2** | The example of U.S. corn (maize) shows how compound yield growth rates lead to overly optimistic future projections</figcaption></figure>

Note: Each line with a number in this figure refers to a separate study as follows: (1) Nelson et al. (2010); (2) Reilly and Fuglie (1998); (3) Heisey (2009); (4) Edgerton (2009); (5) Hertel et al. (2010).

Source: Grassini et al. (2013).
## Inconsistency with trend lines

Our "alternative 2050 baseline" scenario, which uses more recent (and slower) crop yield growth trends from 1989 to 2008,²⁰ projects even larger cropland expansion than our 2050 baseline (Figure 2-4). Thus, estimates that use FAO's projected (faster) yield growth based on 1962 to 2006 rates of gain may be too optimistic. One study that used detailed agricultural census data for subnational units found some worrisome conditions over the 1989–2008 period, including stagnating wheat yields in Bangladesh and in some parts of India and Europe.²¹ This study also showed that yield growth had, at best, plateaued over more than one-quarter of all lands producing wheat, maize, soybeans, or rice.

## Overly optimistic estimates of economic responses to demand

The Future Agricultural Resources Model (FARM) model, which is the only model in the 10-model comparison by Schmitz et al. (2014) that predicts a decline in cropland area, builds in an assumption that, as demand increases, yields also increase substantially and these gains are enough to lead to cropland area decline.²² Other models also incorporate such an assumption to varying degrees, including GLOBIOM, the Global Trade Analysis Project (GTAP), and Modelling International Relationships in Applied General Equilibrium (MIRAGE) models.²³ This important assumption warrants discussion.

There are many reasons why yields are likely to increase over time. For example, improvements in technology will increase yields. In addition, as countries develop economically, the relative costs of nonland inputs decline due to such factors as improved transportation, manufacturing, and distribution, and even improved education and training. As a result, agricultural yields are likely to grow, just as productivity grows in other sectors. In addition, as wages increase with development, use of machinery becomes more economical relative to labor. Mechanization increases the benefit of using flatter, often more productive, lands, which favor use of larger machines.

Yet none of these drivers of yield growth mean that demand growth itself will push up yields even more. Yields today represent a mix of different inputs, including fertilizers, water, seeds, machinery, labor,

and land. Yield increases generally require a shift by farmers toward proportionately greater reliance on inputs other than land, or they require gains in the efficiency of use of all assets (which economists call gains in total factor productivity). Implicit in the claim that increases in demand and prices will cause producers to increase their yields is a claim that higher crop prices will cause producers, when they expand production, to use less additional land and more additional inputs of other kinds (such as fertilizers, pesticides, labor, and machinery). That would cause food production to expand via higher yields rather than via use of more land with existing or even lower yields. However, there is no inherent theoretical reason why this should occur.

In areas where land is limited, farmers may boost yields because increasing production by means of nonland inputs is, on average, cheaper than accessing new land. This scenario would seem more likely to occur in relatively land-constrained areas, such as Asia and North America. But in other regions where extending agricultural land is cheaper, such as parts of Africa and Latin America, land expansion will play a larger role. Because yields are also lower in these regions, any expansion of production there due to increased demand will lower global average yields. The effect on global yield depends on the global average response to increased demand.

Although demand growth may push up yields, there is little rigorous economic evidence to show that it actually does—as we discuss in Chapter 7 on bioenergy.

## Overly narrow focus on grains

Although modeling studies tend to address all crops, some papers focus only on grains, which creates a more optimistic picture because demand for grains is likely to grow more slowly in the future than in the past. For example, as shown in Figure 10-3, wheat and rice yields would not need to grow at their historical rates to meet future demand without land expansion, but fruits and vegetables, soybeans, pulses, and roots and tubers would need to grow significantly faster. Overall, as discussed in Chapter 1, yields would have to grow roughly 10 percent faster from 2010 to 2050 to meet our projected demands without net expansion of agricultural land.²⁴
<figure><img src="image_96.png" /><figcaption>**Figure 10-3** | Future yield growth in many crops will need to be higher than in the past to meet projected food demand on existing agricultural land</figcaption></figure>

Source: GlobAgri-WRR model, WRI and ACE analysis based on Alexandros and Bruinsma (2012).

## Overly optimistic estimates of government and private action

Some analysts adopt a baseline that represents their best estimate of what will happen in the future, including changes they anticipate in government policies, technology, and corporate or farmer behavior. For example, the Model of Agricultural Production and its Impact on the Environment (MAgPIE) model assumes that governments faced with the prospect of higher demand and crop prices will increase their investments in agricultural productivity. This in turn is assumed to lead to larger future yield gains than those that have occurred in the past or that would occur in the future without this additional investment.²⁵ Such an optimistic

approach raises important questions about how most usefully to set a baseline.

It is true that growth in yields between now and 2050 will in part reflect government and private policies that respond to the challenge of a sustainable food future. But if a baseline projection predicts bold, helpful responses, observers might perversely interpret such an optimistic baseline scenario as a signal that there is no problem that needs fixing. The bold responses would then never materialize. We think the most useful “business-as-usual” 2050 scenario should more or less reflect historical trends in food production and consumption patterns so that policymakers can compare the future challenge with what has occurred in the past.
## Overly optimistic estimates of pasture efficiency gains

Projecting the future need for pastureland is inherently challenging. Too few solid data exist on which to make projections of increasing yields of ruminant meat and milk per hectare of grazing land. This “pasture yield” depends on the growth in the share of ruminant feed that is derived from crops and other nonpasture sources, on increasing efficiency in turning each kilogram of feed into a kilogram of meat or milk, and on increasing production or offtake of grass from each hectare of grazing land. Unfortunately, the data for each of these three factors are poor for recent years, and worse to nonexistent for previous decades. Small changes in any of these projections can result in very large changes in pasture area requirements because the world already has so much pasture area—more than one-quarter of the world’s vegetated land (roughly 3 billion ha). Our projections use indirect ways to estimate each of these numbers, and all of them are debatable.

It is very hard to determine why some models have low pasture expansion projections because the underlying assumptions are rarely described adequately. Nearly all economic models have extremely rough representations of the livestock sector in general. The decline in pasture area predicted by the Global Change Assessment Model (GCAM), which projects one of the larger declines, is due to a larger assumed increase in agricultural productivity than we project and a smaller increase in demand for milk and meat than we project. Chapter 11 on increasing livestock efficiency describes the challenges in greater detail.

There are enormous challenges in estimating total pastureland area, but we are skeptical of optimistic baseline estimates of declining pasture area by 2050 for several reasons:

- As described below, recent years have witnessed large-scale gross clearing of forest and woody savannas for pasture.

- Just as shifts in crop production to lower-yield countries will hold down average global rates of crop yield growth, so will those geographic shifts in meat and milk production hold down average global pasture yields.

- Most important, a simpler way of projecting trends leads to even more pessimistic results than our baseline. A simple projection would merely examine previous average global growth trends in meat and milk per hectare of pastureland over time and project that growth forward to 2050. This simple ratio of output per hectare of grazing land would reflect all different drivers of efficiency gains (more output per kilogram of feed, more use of crops as feed, more grass per hectare, and shifts in locations of production). Although we do not truly know how much grazing land is used for meat versus for milk, dividing all meat and milk production by the total pastureland area leads to trend lines that project only 30 to 35 percent increases in meat and milk per hectare by 2050 relative to 2010 (Figure 10-4).²⁶ In contrast, expected increases in global demand of 88 percent (for ruminant meat) and 67 percent (for dairy)—described in more detail in Chapter 6 on shifting diets—mean that meat and milk output per hectare of pastureland must grow at well above historical rates to avoid pastureland expansion.
<figure><img src="image_97.png" /><figcaption>**Figure 10-4** | Historical growth in pasture output per hectare shows a linear pattern</figcaption></figure>

*Source:* WRI analysis based on FAO (2019a).

Our own baseline scenario for 2010–50 projects 53 percent growth in dairy output, 62 percent growth in beef output, and 71 percent growth in sheep and goat meat output per hectare. These projections are based on more complicated methods of estimating historical trends that attempt to tease out separate trends in output per animal, increases in the use of crop-based feeds, and increases in the quantity of grass consumed per hectare of pasture. Although these growth rates are faster than the historical trend measured just as output per hectare, they are not enough to prevent pastureland from expanding by 401 Mha between 2010 and 2050.

Using “yield gap” analysis to estimate potential to meet food needs without expanding agricultural land

One way of analyzing the potential to increase food production while maintaining the same net area of agricultural land is to estimate “yield gaps.” Yield gaps represent the difference between the

actual yields that farmers currently obtain and the potential yield that they could obtain. Farmers can increase yields either by planting crops that have been bred for a higher potential yield, or by improving farm management so that actual yields come closer to achieving the crops' yield potentials (i.e., closing the yield gap).

The definition of yield potential is not straightforward, and researchers use different methods to estimate that potential, which effectively establish different meanings of the “gap.” Several approaches focus on “technical potential,” but even they use different standards for estimating this potential. Researchers compare actual yields to potential yields that can be estimated in three different ways: as the highest global yield, as the yields achieved by researchers in the region under careful management, or as the yields estimated by crop models assuming excellent management without pests or pathogens.²⁷ Each method of comparison will generate a different yield gap.
One persuasive analysis, however, has estimated that farmers are unlikely to achieve more than 80 percent of potential yields in the real world, in part because of economic constraints and in part because of the significant role played by chance in determining annual yields.²⁸ Applying this “80 percent rule” to technical potential is one way of estimating a “practical” yield potential and, by comparing with existing yields, of estimating “practical” yield gaps.

Another approach to estimating a “practical potential” involves comparing average yields of one set of farmers with yields achieved in comparable agroecological settings by other farmers. These other farmers may be nearby or anywhere in the world deemed to have comparable agroecological conditions. For example, yield gaps may be defined as the difference between the average yields farmers actually achieve and yield levels that are just higher than yields achieved by 90 percent of farmers in the same conditions.²⁹

A challenge of this approach is that farms that appear comparable will often have important site-specific differences. In reality, high and low performers often use lands of different qualities even within the same region. In addition, some farms generate high yields in some years because farmers plant at just the right time—planting is followed by the right rainfall patterns and temperatures during growth, reproduction, and harvesting. Yet planting decisions involve a significant element of luck. The element of luck means that different farmers tend to be high performers in different years,³⁰ and using the highest yields will overestimate what even the best farmers can achieve on a consistent basis. Both of these challenges mean that estimates of yield gaps using these methods will tend to be too large.

An even more fundamental factor in overestimates of yield gaps is the effect of data errors, even when they are random. Yield gap studies use different data sets to find differences in yield that can be explained only by management, and these data sets in effect create two basic maps. One map shows yield potential and the other shows actual yields. Errors in the maps that lead to a higher yield potential than actually exists, or that lead to lower actual yields than really occur, will each lead to erroneously large “yield gaps” between the actual and potential. Moreover, errors in opposite direc-

tions will not offset each other and balance out the estimates of aggregate yield gaps because yield gaps are based on the high estimates of yield potential and, often, the low estimates of actual yields. It is the spread between potential and actual yields that defines the gaps, and, because data errors lead to larger spreads than actually exist, they lead to higher gaps than actually exist.³¹

Beyond this tendency to overestimate yield gaps, different yield gap analyses often generate widely varying results, even when they focus on a relatively small local area.³² Global analyses face greater challenges because of data quality, which Neumann, Verburg, et al. (2010) forthrightly acknowledge “might even outrange the yield gap itself.” Even when analyses generate similar aggregate estimates, they may hide widely varying results at national and regional level. For example, two well-known global exercises both found large, somewhat consistent global yield gaps—a 58 percent gap for total calories in Foley et al. (2011), and roughly 50 percent gaps for wheat and rice and a 100 percent gap for maize in Neumann, Verburg, et al. (2010). Yet Foley et al. (2011) found that the largest yield gaps exist among farmers in intensively managed regions, not among farmers in less intensively managed regions. The farmers in the former regions, such as India, northeastern China, and parts of the United States, had gaps of more than 4 t/ha/yr, whereas yield gaps in most of sub-Saharan Africa were mostly less than 1 t/ha/yr.³³ These results would be discouraging because high crop prices, government support, and infrastructure already provide farmers in the high yield-gap regions of India, China, and the United States with high incentives to boost yields. However, in complete contrast, the global yield gap study by Neumann, Verburg, et al. (2010) estimated large maize yield gaps in Africa (5–9 t/ha/yr) and much smaller gaps in the United States (less than 2 t/ha/yr in most areas). All of these limitations suggest that yield gap analyses should be used with great caution.

Nevertheless, a wide variety of studies, using a wide variety of methods, find substantial yield gaps. Fischer et al. (2014) used this range of evidence, and good scientific judgment, to estimate yield gaps crop by crop and region by region. The study amounts to a case for both optimism and caution when summed to global averages. Among the major crops, the review found that the largest potential
for closing yield gaps exists for maize, with a global weighted yield gap of roughly 100 percent (i.e., a potential for doubling), with generally much larger gaps in developing countries. The rice yield gap was similarly found to be large, at roughly 70 percent. The review also found high yield gaps of 100 percent or more in the developing world for other important food crops, including sorghum, millet, and cassava. By contrast, global estimated yield gaps were only roughly 50 percent for wheat, and 30 percent for soybeans. In the case of soybeans, the lower yield gap is explained mainly by the fact that all three countries that dominate global soybean production—the United States, Brazil, and Argentina—have high yields already.

These yield gaps are grounds for tempered optimism, but applying the 80 percent rule of practical yields achievable by farmers leads to more sobering results. For example, applying the 80 percent rule to wheat results in only a 40 percent gap. That is roughly enough to meet projected demand for

wheat consumption, but only if all farmland everywhere achieves this practical potential—a big challenge.

Ultimately, we derive three lessons from this review. One, although the world has significant technical potential to increase yields even on rainfed land, the potential is not so great that achieving necessary yield gains will be easy. Two, because the existing practical potential is not huge, the world cannot afford to waste any farmland, or “leave any farmland behind.” Three, in addition to just closing yield gaps, crop breeding will probably be necessary to increase yield potentials. The ability to increase potential yields has probably diminished as yields grow higher and higher, and researchers mainly estimate potential by focusing on recent rates of change. Yield potentials continue to grow rapidly for some crops, such as maize, while others grow more slowly.³⁴ Only new breeding can increase potential yields, and we focus on that along with other breeding opportunities in Chapter 12.

<figure><img src="image_98.png" /></figure>
# Data Limitations Obscure the Extent of Agricultural Land Expansion

What can we learn from recent evidence regarding agricultural land expansion? The answer, unfortunately, is unclear due to imperfect data. The answer also involves three different analytical challenges: the analysis of gross forest-cover loss, which can be driven by agricultural conversion but also by logging or fire; the analysis of gross forest-cover gain in separate areas, and therefore the calculation of net forest-cover loss that must combine gross forest-cover loss and gain; and the allocation of forest-cover losses and gains to different drivers. Overall, there is very strong evidence that gross tree-cover loss is continuing at high rates and probably accelerating, good evidence that gross agricultural conversion is a major driver of that conversion, and less clear evidence of what is occurring on a net basis. We briefly review the different kinds of evidence.

## Satellite studies of cropland and pasture

Perhaps the best evidence of trends in land use comes from studies of satellite imagery. Most historical satellite-based land-use change studies use satellite images from the Landsat program of the U.S. National Aeronautics and Space Administration. These images cover the majority of the earth's surface many times each year, and analyzing such large data quantities in many regional contexts still remains a scientific challenge. Different research groups develop different computer algorithms to interpret what land changes are occurring based on the amount of light reflected from the sun in various ranges of the electromagnetic spectrum. These algorithms often result in very different interpretations of land-cover change.

Satellite images cover the whole earth but the images are grainy. Human interpretation of large areas is not practical, although human interpretation is usually more accurate than computer algorithms when analyzing individual satellite images for changes in land use and land cover. As discussed

below, large discrepancies in different satellite mapping programs are reported in the literature, as are higher rates of inaccuracy when comparing these automated global mapping interpretations to more reliable manual interpretation using higher-resolution imagery available on aerial photography platforms such as Google Earth.³⁵

WRI's Global Forest Watch (GFW) publishes maps of loss of “tree cover” using estimates from the Hansen data set based on algorithms developed at the University of Maryland (UMD).³⁶ According to Zeng, Estes, et al. (2018), this data set has a higher rate of accuracy (that is, the percentage of land-cover classes that was correctly determined) than other global land-cover mapping data sets, as determined by comparisons with manual interpretation of high-resolution aerial photographs in selected geographic locations.³⁷ On the basis of this data set, GFW estimates that the world had average “gross” losses of 20 Mha of forest cover each year from 2001 through 2018 (Figure 10-5). Moreover, the levels of forest-cover loss have been rising unevenly but substantially from an average of roughly 15 Mha in 2001 and 2002 to almost 30 Mha in 2016 and 2017.

Tree-cover loss may be due to causes other than agricultural expansion, including forestry and fire. Curtis et al. (2018) analyzed forest loss data from 2001 to 2015 and estimated that roughly half of tree-cover loss was due to forestry and wildfires while the remainder, roughly 10 Mha per year, was due to conversion to agriculture.

Curtis et al. (2018) attributed roughly half of the agricultural conversion to a category they called “shifting agriculture,” which was not considered “deforestation” because the authors theorized that agriculture was not expanding but just shifting around within an area in long-term rotations of agriculture and forest. “Shifting” or “swidden” agriculture is a type of agriculture long recognized and practiced by farmers with limited access to fertilizers to allow crop fields to regain fertility through natural regrowth.
<figure><img src="image_99.png" /><figcaption>Figure 10-5 | The world lost more than 360 million hectares of tree cover between 2001 and 2018</figcaption></figure>

*Source:* Hansen et al. (2013). Accessed through Global Forest Watch on June 20, 2019. http://www.globalforestwatch.org.

We disagree that the actual areas cleared should be characterized as “shifting agriculture” rather than as agricultural expansion and therefore new conversion, on the basis of the methodology used in the study. We believe that a more appropriate term is “mosaic” agricultural conversion. The most significant criterion used by Curtis et al. (2018) to designate “shifting agriculture” was that, in any 100 square kilometer grid cell, if more than a minimal part of the cell was reforesting then all the expansion would be characterized as “shifting agriculture” and not “deforestation.” That defini-

tion encompasses a wide array of areas that would be experiencing true expansion of agricultural land area if any of the following were also present in these areas:

- Some true rotational agriculture

- Some agricultural abandonment (regardless of whether the farmers who abandon the land are shifting to other parts of the area)

- Some regrowth of forest area from local clearing of forest for wood products
This method results in nearly all agricultural expansion in Africa being defined as “shifting agriculture” and not “deforestation”—a problem acknowledged by the authors—even though multiple studies, including by many of the same authors, have found that agricultural expansion into new areas is occurring in Africa on a large scale.³⁸ Not only are completely new areas being cleared in Africa, but some farmers who have long practiced shifting agriculture also are reducing the length of their rotations, thus allowing less forest regrowth.³⁹ That is also a form of net agricultural expansion. In addition, the methodology explains, for example, why Curtis et al. (2018) generally attribute agricultural clearing in northern Thailand as being for “shifting agriculture” and not “deforestation.” However, separate, more detailed local analyses have shown that agriculture is not just shifting around in this region but also expanding, both in lowlands and in mountains areas.⁴⁰ While expansion is carried out by smallholder farmers, they are not practicing subsistence agriculture. They are predominantly producing commodity crops such as maize and should be viewed as part of the global response to increased food demands.⁴¹

At the global level, the Hansen maps incorporated into Global Forest Watch (GFW) support the proposition that gross global agricultural conversion of forests has amounted to at least 10 Mha per year since 2001, and that this level of conversion has likely been increasing. These estimates also leave out some additional areas of agricultural expansion. For example, they will not capture some conversion of natural forests to tree crops, such as rubber. Nor will they include conversion of many woody

savannas and grasslands because the Hansen maps apply only to clearing of forests with 30 percent tree canopy or more (meaning that at least 30 percent of the ground is covered by leaves on trees).

Satellite-derived maps that try to interpret conversion of sparser, savanna woodlands are less likely to be accurate. Other studies, some using radar-based approaches, find that substantial conversion of such woodland savanna areas is occurring as well.⁴² These savanna landscapes occupy large portions of Africa and Latin America that are known to be areas of agricultural expansion.⁴³

Gross expansion, however, is not the equivalent of net expansion. Although areas identified by Curtis et al. (2018) as expansions of shifting agriculture should be viewed as gross deforestation, the reforestation found in these areas suggests that some agricultural land is being abandoned both long-term and as a part of the multiyear rotations of croplands and forest that are a part of traditional “shifting” or “swidden” agriculture. Even in large-scale commodity agriculture, as we discuss in Course 3, substantial areas of land may be abandoned as agriculture shifts to other areas that can be hundreds of kilometers or even continents away. GFW researchers estimate that roughly one-third of total deforestation between 2001 and 2012 was offset by reforestation of some kind of forest somewhere in the world. They further estimate that the greater part of reforested area was likely regrowth following previous fire or forestry and not agricultural abandonment.⁴⁴ But the method used for this part of the analysis was unlikely to capture all reforestation.⁴⁵
Although we focus here on the implications of GFW studies, a variety of alternative analyses of deforestation and other land-use changes complicate the lessons. Some studies are broadly consistent with GFW. For example, one study by Kim et al. (2015) of the 34 tropical countries with extensive forest areas found gross forest loss rates of 7.8 Mha per year, and net loss of 6.5 Mha per year. This is comparable to GFW's estimate of gross annual loss of 7.5 Mha per year and net loss of 5.5 Mha per year in these same 34 countries between 2001 and 2012. Other analyses are inconsistent with GFW and find lower rates of gross and net forest loss.⁴⁶ In part reflecting these lower forest loss rates, they also sometimes find only modest net expansion of cropland and pasture area since 2000, which GFW does not explicitly estimate although its results suggest much more.

There are other methodological differences, but one important factor may be the spatial resolution of satellite images used. The GFW and Kim et al. (2015) analyses used Landsat images that cover, on average, about one-tenth of a hectare, whereas alternative analyses that find less net forest loss are often derived from images with coarser resolution, with pixels representing 6 or 10 ha, or even larger areas on the ground.⁴⁷ In landscapes that have a mix of patches of forest and cropland, it is often difficult to interpret both land-cover and land-use changes from satellite images with larger pixel sizes.⁴⁸ The evidence indicates that analyses using images with larger pixel sizes tend to detect fewer small farm fields⁴⁹ and therefore may leave out expansion of small farm fields in complex landscapes.

Overall, the implications of the GFW estimates we have presented are that gross conversion of forest for agriculture, both cropland and pasture, has likely been greater than 10 Mha per year since 2001. Additional conversions of savannas and natural grasslands to agriculture are likely, though not reflected in these data.

## FAO cropland data

FAO reports two kinds of data regarding cropping, one suggesting an unprecedented expansion, the other suggesting meaningful but more modest expansion.

*Harvested area* refers to the number of hectares actually harvested each year, which is different from the area classified as “cropland.” If farmers plant and harvest two crops on a hectare in a year, it counts as two harvested hectares, and if they do not plant or harvest crops on a hectare in a year, it counts as zero. *Cropland*, according to FAO’s definition, is supposed to refer to any land that has been planted to a temporary or perennial crop at any time over the previous five years, although FAO does not actually insist that countries use this definition, and at least some do not.

According to FAO data, global harvested area expanded from 2002 to 2016 at an unprecedented rate of 15.1 Mha per year.⁵⁰ (That increase compares to an average annual increase of only 4 Mha from 1982 to 2002; see Figure 10-6.)⁵¹ By contrast, according to FAO data, global cropland has been expanding at a rate of roughly 4.3 Mha per year since 2002.⁵²

In theory, the difference between harvested area and cropland area could reflect a large increase in double-cropping, or a large decrease in the number of hectares left fallow. Both practices increase harvested area without increasing cropland. Some researchers interpret the data in these ways.⁵³ However, we believe that independent data do not support this explanation, and that the discrepancy probably represents flaws in the data for cropland, or harvested area, or both. For example, the few specific analyses of changes in double-cropping do not support the idea of large increases in the practice. Independent reports suggest that double-cropping in Brazil increased by a total of roughly 6.5 Mha from 2002 to 2014, with nearly all the
double-cropping involving maize after soybeans.⁵⁴
But elsewhere, the independent data do not show
large increases in double-cropping. For example,
FAOSTAT data on harvested area versus cropland
area would logically imply either an increase in
double-cropping or a decline in fallow area of 13
Mha in China from 2000 to 2011.⁵⁵ However, a
remote-sensing study found a 4 Mha decline in
double-cropping and an increase—not a decrease—
in fallow lands of 1 Mha during this time period.⁵⁶
In the United States, although FAO data might
suggest an increase in double-cropping, there was
virtually no change in double-cropping from 1991 to
2012, according to U.S. Department of Agriculture
(USDA) statistics.⁵⁷

One explanation is that some countries are prob-
ably undercounting their expansion of cropland by
not reporting cropland in ways that meet the FAO
definition. For example, FAOSTAT reports a 20
Mha decline in U.S. cropland from 2002 to 2012,
which reduces the global expansion of cropland
reported by FAOSTAT. This decline reflected
reporting by the USDA, but, according to the USDA,
true cropland area did not decline in the United
States.⁵⁸ The decline in “cropland” reported was due
instead to a decline in reported area of “cropland
pasture,” that is, land that the U.S. government
had characterized as cropland because of historical
use as cropland but much of which had long been
used for pasture. The decline in cropland area thus

<figure><img src="image_100.png" /><figcaption>Figure 10-6 | Harvested area for 15 major crops has expanded by about 125 million hectares since 2002</figcaption></figure>

Note: The 15 major crops are barley, cotton, groundnuts, maize, millet, oats, rapeseed, rice, rye, sorghum, soybeans, sugar beet, sugar cane, sunflower seed, and wheat.

Source: WRI analysis based on FAO (2019a).
appears to be mainly a consequence of a recategori-
zation of land, most of which should not previously
have been considered cropland according to FAO
definitions because it had not been cropped for at
least five years. According to FAO definitions, the
United States should also have declared a 4 Mha
increase in cropland between 2002 and 2016<sup>59</sup>
due to the return to cropping of land previously
taken out of production for more than five years in
the Conservation Reserve Program. However, the
United States did not report an increase in cropland
because it had continued to report land in the pro-
gram as "cropland" even though it had been planted
in grasses and trees for more than five years.

Although such underreporting may play a role, the
reality is that we do not really know what explains
the discrepancy between the expansion of harvested
area and the expansion of cropland because the
data are just too uncertain. FAO uses data reported
by countries, and there is no independent way of
evaluating the data on harvested area or even any
integrated source of information on the different
methods countries use.

Cropland area might appear to be easier to estimate
because of the potential use of aerial or satellite
photographs, but at this time, the challenges,
uncertainties, and discrepancies in satellite inter-
pretations create major uncertainties. Even reports
from advanced agricultural countries that devote
substantial resources to assessing cropland appear
to have limitations. In one unsettling example, a
2018 satellite study suggests that Brazil has been
widely misreporting its cropland. Although FAO-
STAT reports Brazilian cropland as increasing from
65 Mha to 86 Mha between 2000 and 2004, this
study found that Brazil's cropland was actually only
26 Mha in 2000 and had expanded to 47 Mha by
2014. The study suggested that part of the discrep-
ancy probably occurred because Brazil had been
reporting harvested area as cropland, but much of
the discrepancy could not be explained.⁶⁰ Because
Brazil is renowned for its satellite studies of land
use and for its agricultural research agency, this
result raises questions about data from countries
with fewer resources available for such analyses.

Overall, substantial uncertainty remains. There
appears to be a large and perhaps unprecedented
increase in harvested area globally. The cropland
data do not show a similar increase. The discrep-

ancy cannot be well explained by an increase in
double-cropping or a decline in fallow lands. Some
of the discrepancy—at least in the United States—
appears to be due to an underreporting of cropland.
In general, however, the data are too uncertain to
be reliable and data discrepancies raise questions
that cannot now be answered.

FAO pastureland data

FAOSTAT pastureland data report that global pas-
ture area actually *declined* by 140 Mha from 2001
to 2016. If true, these data would indicate a trend
toward future pasture area declines, but a closer
look suggests otherwise.

Of the reported decline, 81 Mha occurred in Austra-
lia—the result of a decision to no longer character-
ize some extremely dry grazing lands as permanent
pasture. An additional 36 Mha of the reported
decline occurred in Sudan (both Sudan and South
Sudan), which might be the result of drought but
given changes in government may also be the result
of an estimation or accounting change. Some real,
but much smaller declines do seem plausible in
places such as China, due to reforestation programs
on dry, hilly pastures.⁶¹

The challenges with Australian and Sudanese
pastureland data are emblematic of much larger
challenges, which start with an ambiguity about
what constitutes pasture in the first place.⁶² Esti-
mates of pastureland area range from less than 2
billion ha,⁶³ to 2.8 billion ha (based on adjustments
to FAO data),⁶⁴ to 3.35 billion in FAOSTAT as of
2010, and reach 4.5 billion ha in another study.⁶⁵
The largest estimate includes wide areas assumed
to support occasional browsing by animals even
if not consistently grazed. Among the critiques of
the FAO figure, one research team found that 500
Mha of pastureland reported by FAO, on the basis
of country reports, were simply too dry to support
permanent grazing on any meaningful level (e.g.,
large areas reported by Saudi Arabia).⁶⁶

One puzzle is that FAOSTAT reports an increase
in pastureland in Latin America of only 11 Mha
from 2001 to 2013.⁶⁷ Both a region-wide study
and numerous local studies have documented that
much larger gross deforestation in Latin America
is largely and probably primarily due to expansion
of pasture.⁶⁸ Between 2001 and 2013, a study using
Moderate Resolution Imaging Spectroradiometer
(MODIS) satellite images found gross pasture expansion of 97 Mha.⁶⁹ A 30 Mha conversion of pasture to cropland reduced this net expansion, as well as some unknown amount of reversion to forest, but the gross figures still suggest a large net expansion of pasture.

For these reasons, we do not consider FAOSTAT data on pasture reliable and think that net pasture expansion is likely occurring based on the analyses in Latin America. However, on a global and probably also regional basis, there also appears to be a shift from drier, less productive grazing land, such as that being reforested by Chinese conservation programs, toward wetter, more productive grazing land, such as that in Latin America. This shift in effect uses more of the productive potential of land even if land area does not expand.

## Reasons for Optimism: Smarter Agriculture

Although the ability to increase output simply by adding fertilizer or water has been declining because fertilizers are already heavily used in most

areas and additional water resources for irrigation are limited, agricultural output has continued to grow. Since 1960, the annual growth rate of agricultural production, as measured by economic output, has remained constant. (The increase in economic output is not exactly the same as an increase in yield but they are closely related.)⁷⁰ Yet the role of increased inputs and land in this growth declined from 95 percent in the 1960s to only 25 percent in the 2000s.⁷¹ Instead, 75 percent of the gain in output in the 1990s and 2000s resulted from improvements in total factor productivity, which means improved technology or better use of existing technology (Figure 10-7).⁷² Much of the gain has resulted from the spread of advanced farming technologies, particularly to China, Brazil, and Argentina. Although these farming improvements have not been sufficient to eliminate agricultural land expansion altogether, they suggest the potential power of farming advances.

The following chapters discuss a variety of menu items for farming smarter and “leaving no farm-land behind.”

<figure><img src="image_101.png" /><figcaption>**Figure 10-7** | The primary source of growth in agricultural output has shifted from input increases to improvements in total factor productivity</figcaption></figure>

*Source:* Fuglie (2012).
<figure><img src="image_102.png" /></figure>
MENU ITEM: INCREASE
LIVESTOCK AND PASTURE
PRODUCTIVITY

Global attention has tended to focus on achieving increases in
crop yields. But given the much greater extent of pastureland
and the importance of croplands in providing animal feed,
increases in the efficiency of livestock farming are at least
equally important. This menu item explores opportunities
to boost livestock productivity to reduce both land use and
greenhouse gas (GHG) emissions.
## The Challenge

The world's farmers now annually raise roughly 1 billion pigs, 1.7 billion cows and buffalo, 2.2 billion sheep and goats, and 61 billion chickens,⁷³ and use more than 3 billion ha of pasture land and hundreds of millions of hectares of cropland to do so. These animals are responsible for generating most of the GHG emissions associated with production processes (as opposed to land-use change) in the agriculture sector. (This issue is discussed in more detail in Course 5.)

With projected increases in animal-based foods overall of 68 percent, increases in dairy of 67 percent, and in ruminant meats of 88 percent,⁷⁴ the world's farmers and ranchers will have to produce far more milk and meat per hectare and per animal if the world is to avoid billions of hectares of expansion of pasture area and cropland for feed and vastly increased GHG emissions from livestock alone.

Improving the efficiency of milk and meat production is critical. If the world were to achieve no further productivity gains after 2010 (efficiencies remain at 2010 levels), meeting expected demand for meat and milk in 2050 would require cropland and pasture area to expand by 2.5 billion ha. This enormous amount of land clearing would release an average level of 20.6 Gt CO₂e in land-use change emissions each year.⁷⁵ This level amounts to almost the entire global "budget" of 21 gigatons for all GHG emissions by 2050, as discussed in Chapter 2.

Increases in the efficiency of milk and meat production are also critical for holding down production-related emissions from livestock. In our base year of 2010, livestock generated 3.3 Gt CO₂e, or roughly 7 percent of total human-caused GHG emissions excluding land-use change and production of animal feeds. Without any efficiency gains in livestock production, those production emissions would rise to 6.3 Gt by 2050. In our baseline scenario, efficiency gains hold those increases to 4.9 Gt by midcentury.⁷⁶

## Projected Efficiency Gains

Fortunately, past experience suggests that milk and meat efficiencies are likely to grow. Between 2010 and 2050, at the global level, our baseline projection assumes a 53 percent increase in dairy products produced per hectare of grazing land, a 62 percent increase in beef produced per hectare, and a 71 percent increase in sheep and goat meat per hectare. These increases are the synergistic effect of three separate changes:

- More crop feeds. We project an increased use of crops in animal diets, with those crops mostly replacing crop residues, which have poor nutritional qualities for animals.

- An improvement in the efficiency of converting each kilogram of feed to meat or milk. Based on analysis of historical trend lines, we assume each ton of feed will produce 20 percent more beef, 22 percent more sheep and goat meat, and 16 percent more milk globally.

- Each hectare of land used for grazing or for cut forage will provide on average 23 percent more forage.⁷⁷

For poultry and pork production, on a global basis, our projections assume roughly 20 percent increases in output of meat per kilogram of feed for poultry and pork meat, and 10 percent for eggs. We extend the projections of Wirsenius et al. (2010) to 2050, which assume modest gains in feed efficiency in developed countries but large gains in developing countries.

Realizing these global efficiency gains even in our baseline, however, will be very challenging. One reason is that demand for livestock products is growing most where livestock productivity is lower. Even if these regions greatly improve their efficiency, the shift of some share of production from developed countries to developing countries has the effect of lowering average global efficiency levels. Another reason is that, as discussed in Chapter 10, our estimates project overall increases in output of ruminant meat and milk per hectare of grazing land that already exceed simple extensions of historical trend lines (Figure 10-4). Finally, climate change will cause many challenges for livestock production: high heat tends to stress animals, reduce production, and increase disease. In many locations, increasing temperatures also can reduce water availability.⁷⁸
Yet even with such optimistic estimates, which include efficiency improvements in every world region, our baseline still projects pastureland expansion of 401 Mha between 2010 and 2050. A less optimistic projection, involving a 25 percent slower rate of feed efficiency gain between 2010 and 2050, would see pasture area expand by 523 Mha between 2010 and 2050, and annual emissions from land-use change rise from 6 gigatons (in our 2050 baseline) to 7.1 gigatons.

## The Opportunity

The scale of opportunities for productivity gains differs between pork and poultry, on the one hand, and ruminant meat and dairy, on the other.

### Pork and poultry

Concentrated production systems for pork and poultry in developed countries have achieved such high levels of efficiency in meat and egg production, both per animal and per ton of feed, that most analysts believe they are approaching biological limits—as well as limits on humane conditions for raising animals. A European research effort concluded in 2012 that pig and poultry production in Europe was likely to improve in feed efficiency by only 1 percent or less.⁷⁹

In developing countries, there is ample room to increase the feed conversion efficiency of “backyard” pork and poultry production by shifting to crop-based feeds, but those shifts do not save land overall because backyard systems rely heavily on local wastes and scavenging, which our analysis treats as “land free.” Future land-use savings are likely to be achieved primarily by farmers in developing countries adopting developed-world production techniques. This development is already the principal driver of pork and poultry expansion in emerging economies such as Brazil and China. Although at least one paper has speculated that there is still more room for productivity gains in advanced systems such as those in Europe,⁸⁰ we consider the global efficiency gains from 2010 to 2050 in our baseline scenario already high and thus we do not model additional increases in efficiency of pork and poultry systems.

A major focus in the future should be on raising pigs and poultry in concentrated conditions that are more humane and create less air and water pollution.

Good animal husbandry requires increasing space for animals and better waste management. Some analyses have found that raising animals in more humane conditions reduces efficiency,⁸¹ but other studies have found that it can reduce mortality and lower stress, thereby increasing productivity and reducing emissions.⁸² The details obviously matter, and we believe these kinds of improvements should receive substantial attention.

### Ruminant meat and dairy

In contrast to poultry and pigs, the evidence indicates broad technical potential to increase the efficiency of meat and milk from cattle, sheep, and goats. These ruminants are responsible for more than 90 percent of estimated direct emissions from livestock both in 2010 and in our 2050 baseline scenario,⁸³ and their feeding uses all pastureland and roughly 20 percent of all crops devoted to livestock.⁸⁴ Three interrelated efficiency gains for ruminants are important to reduce both land-use demands and direct production emissions from these forms of livestock:

- **Production per hectare of land.** Growing improved grasses and shrubs, and fertilizing and grazing them well, will improve both the quantity and quality of forage the land produces and the percentage of the forage ruminants will consume.

- **Production per kilogram of animal feed.** The quality of feed, which is based largely on its digestibility and protein content, determines both how much forage a ruminant will consume and how much growth and milk the ruminant will produce from the forage. Because animals first use food energy to maintain themselves before gaining weight or producing milk, eating feeds with low digestibility provides little additional energy to add weight or produce milk. Once maintenance thresholds are met, improving feed quality results mainly in more growth or milk, which means output grows disproportionately with higher-quality feed.⁸⁵

- **Production per animal.** Even when ruminants consume no more energy than they need to maintain themselves, they still produce GHGs. In general, faster-growing or higher-milk-producing animals that receive higher-quality feed direct more of their feed into milk or meat
and less into just maintaining themselves. The effect is to reduce the GHG emissions per kilogram of meat or milk produced. Judged on the basis of a whole herd, the gains are even larger. Much of the feed consumed or emissions generated by a herd of cows, sheep, or goats is by mothers engaged in producing their young. And some feed is consumed and some emissions produced by animals that die before being slaughtered or finishing their milk production. As animals increase their reproductive rates and as their mortality declines, they will also increase the amount of meat and milk produced per kilogram of feed or per ton of GHGs. Figure 11-1 illustrates the close relationships between production emissions and output per animal in the case of milk.

Each of these efficiency gains reduces both land-use demands and associated GHG emissions, particularly of methane emissions—the dominant form of emissions from ruminant production (excluding land use).⁸⁶

A striking feature of Figure 11-1 is that improving the most inefficient systems generates the largest

marginal returns in the form of reduced emissions. Once milk or meat production is already efficient, additional efficiency measures (e.g., shifting to even more crop-based feed), achieve only modest additional increases in GHG efficiency. Helping inefficient livestock systems—often those of small farmers—to improve therefore provides large opportunities for environmental gains.

Improving inefficient livestock systems also provides large opportunities for improved nutrition and poverty reduction. The vast bulk of the roughly 900 million livestock keepers in sub-Saharan Africa and South Asia work on small, mixed farms.⁸⁷ In India, small and marginal farmers own 60 percent of female cattle and buffaloes. Women farmers play a particularly prominent role.⁸⁸ Systematic government investment and supportive policies led India to become the world's largest dairy producer, with heavy participation by small farmers.⁸⁹ Not only can efficiency gains in developing countries by definition lead to more milk and meat while using fewer resources, but efficiency gains by small farmers will be critical to their continued ability to enhance their incomes through farming.

<figure><img src="image_103.png" /><figcaption>Figure 11-1 | More efficient milk production reduces greenhouse gas emissions dramatically</figcaption></figure>

Note: Dots represent country averages.

Source: Gerber et al. (2013).
## Technical options

The wide range in beef and dairy production efficiencies across production systems and regions indicates that high technical potential exists for improvement. According to FAO data, in 2006, the yield of meat per beef carcass was 166 kg (carcass weight) in developing countries compared to 271 kg in developed countries.⁹⁰ The quantity of feed required per kilogram of beef is four times greater in Africa than in Europe.⁹¹ In fact, variations between the most feed-efficient beef systems in Europe and North America and the least efficient systems in Africa and South Asia vary by a factor of 20, and dairy system efficiencies vary by a factor of 10.⁹² Land-use requirements are calculated differently by different studies, but as estimated by Herrero et al. (2013), land-use requirements vary by a factor of 100.

GHG emissions generated per kilogram of beef or dairy protein also vary widely—even without counting emissions from land-use change. One study's findings show ranges of a factor of 30 (Figure 11-2).⁹³ A study by FAO in 2010 found that, on average, GHG emissions per liter of milk produced in Africa were five times those of North America.⁹⁴

Fortunately, dairy and meat production in the developing world does not need to employ concentrated feedlots to become more efficient. Even today, Indian dairy production emits only half as many GHGs per liter of milk as African dairy production, according to the same 2010 FAO study.⁹⁵ The principal opportunities for improvement are well known, and can also build resilience to climate change. They fall into three basic categories: better feeding, better health care and overall animal management, and better breeding.

### Better feeding

Improved feeding strategies fall into several categories, including the use of improved forages and better grazing, supplemental feeds, and more digestible crop residues.

**Improved grasses and use of legumes and trees.** Planting pastures with “improved” grasses (grasses bred for higher yields) and using adequate amounts of fertilizer produces larger amounts of more digestible forage. Adding legumes can reduce the need for fertilizer and increase the protein content of forage, but ruminants may selectively graze out the legumes. Rotating animals periodically through different parts of a field, or different

<figure><img src="image_104.png" /><figcaption>Figure 11-2 | Inefficient beef production systems result in far higher greenhouse gas emissions per unit of meat output</figcaption></figure>

Note: Maps are for illustrative purposes and do not imply the expression of any opinion on the part of WRI concerning the legal status of any country or territory, or concerning the delimitation of frontiers or boundaries.

Source: Herrero et al. (2013).
fields, often by moving electric fences, also leads animals to consume more of the available forage while it is most nutritious and tends to maximize grass growth by keeping grasses at optimal growing heights. (There is a scientific debate about whether very well-managed, continuous stocking can achieve the same gains.) In some areas, mixing cattle with sheep or goats—animals that graze differently from bovines—improves the efficient use of the whole pasture and can reduce worms and other pest problems.⁹⁶ In parts of Africa and Asia where “cut and carry” systems of forage predominate, large potential also exists to improve the production of more digestible and protein-rich forage crops, including both grasses and high-protein shrubs.

**Supplemental feeds.** Nearly all of the world's grazing lands have seasons when rainfall is too low or temperatures too cold to produce abundant and high-quality grass. Animals can lose much weight in these seasons. The need to keep animal numbers down so that they do not starve results in stocking densities (animals per hectare) that are too low to fully exploit available grass in the rainy season. Supplements can include crops or silage, which is a crop (often maize) harvested with both stovers and grains, chopped up and preserved, or hay harvested and preserved in the wet season.

Some supplemental feeding of animals with feed grains or oilseed cakes, which are highly digestible and some of which have high protein content, typically leads to substantial production gains and reductions in emissions per kilogram of milk or meat.⁹⁷ Industrial by-products like brewers' yeast and the leaves of some shrubs (such as *Leucaena* and *Calliandra*) can also provide highly nutritious supplements.

At very high levels of use, reliance on crops will often continue to increase production, but it may not continue to decrease GHG emissions—at least when compared to intensive pasturing. For example, U.S. dairy production, which relies heavily on grains, produces more milk per cow but has higher production emissions than European dairy.⁹⁸ This is because the higher GHG emissions from producing crops (rather than pasture) begin to cancel out the yield benefits of more milk per cow. In fact, factoring in land use can more clearly show

the advantages of highly intensive grazing. One study found that soil carbon losses from converting intensive pasture in the Netherlands to maize to supply dairy feed would lead to net increases in atmospheric carbon for at least 60 years, despite the reductions in methane from cow digestion due to the higher-quality feed.⁹⁹

**More digestible crop residues.** Ruminant animals can only eat so much food at any one time. The more digestible the food, the more energy animals derive from each kilogram of feed; and the more rapidly animals digest this feed, the more they can consume.

Roughly 16–19 percent of the world’s beef and dairy feeds are crop residues,¹⁰⁰ but most have low digestibility, and reliance on their use is heavily concentrated in poorer countries. But opportunities exist to introduce crop varieties with more digestible residues. Farmers in India, for example, have adopted such sorghum varieties, which does much to explain why India’s higher dairy production is more efficient than Africa’s.¹⁰¹ In contrast, few African farmers have adopted crop varieties with more digestible residues, although doing so should greatly improve both milk output and GHG emissions efficiency.¹⁰² For African farmers to fully exploit this opportunity, grain varieties with more digestible residues will need to be adopted into local breeding programs. Other technical opportunities have long existed to improve stover digestibility by treatment with urea. Agricultural development programs have initiated many pilot efforts, but cumbersome labor requirements or the costs of urea have hindered adoption.¹⁰³

### Improved health care and overall animal management

Livestock health problems—from ticks to viral and bacterial infections that reduce growth and milk production—suppress fertility and increase mortality. Basic veterinary services, including vaccines and tick control, therefore would increase production. Other management techniques are also available that enable animals to have babies more frequently, and help the young animals grow better. Timing breeding so that young animals are born before the start of wet, forage-abundant seasons rather than dry, hungry seasons can also have a large impact.¹⁰⁴
Better breeding

Some livestock breeds grow faster and produce more milk than others. Improved feeding in general should make possible more widespread use of high-yielding breeds, although some native breeds are better able to handle heat stress and do better when feeds are less nutritious. Regardless of breed, farms that keep track of their animals' production and use the highest producing animals to breed new cows can steadily increase their productivity over time.

In the developed world, the opportunities for efficiency gains among ruminants largely depend on new breeding. For decades, the focus of breeding has mostly been production per animal, leading to breeds of animals that can consume vast quantities of feed and put on weight or produce milk in high amounts. Coincidentally, this breeding has led to overall efficiency gains because more of the energy in feed goes into production of meat or milk rather than maintenance of the animal.

An alternative breeding strategy might focus explicitly on breeding animals for their efficiency in converting feed into milk or weight gain. That is, the same or increased meat or milk production would be achieved with little or no increase in feed volumes. The opportunity appears substantial—and should also have benefits in developing countries—because different individual animals appear to have a substantial range of efficiencies. However, the field of breeding deliberately for feed efficiency is in its infancy, and there can be economic trade-offs between maximizing how much milk or meat a single animal produces versus how much milk or meat a kilogram of feed produces, so the potential benefits at this time are uncertain.¹⁰⁵

Using these different ways of improving efficiency, many farms have shown high potential for efficiency gains in developing countries, even in a changing climate. The following provide some examples:

**Silvopastoral systems in Colombia.** On roughly 4,000 ha in Colombia, farmers have developed intensive silvopastoral systems that provide a highly productive and environmentally efficient method of producing milk or beef. Farmers plant many separate layers of vegetation: a layer of highly productive grasses dominated by stargrass complemented by three rows of shrubs or trees. According to researchers at the country's Centro de

investigación en sistemas sostenibles de producción agropecuaria (Center for Research on Sustainable Agricultural Production Systems),¹⁰⁶ *Leucaena* shrubs play a particularly critical role. These shrubs fix nitrogen, which fertilizes the grasses, and create protein-rich leaves for the animals. The shrubs grow fast, and when cows bend the branches to eat the leaves, the branches do not break but rather bounce back. The tree layer increases humidity under the canopy, which promotes grass growth and provides shade to reduce heat stress on animals.

Compared to extensive grazing, farms adopting intensive silvopastoral systems have generated several times the milk per hectare and better resist drought.¹⁰⁷ Production of milk can even be 70 percent higher than otherwise well-managed and fertilized pasture. Silvopastoral areas also have enhanced carbon stocks and biodiversity, including a reported 71 percent increase in bird abundance and diversity compared to standard extensive grazing.¹⁰⁸ These systems require a high up-front investment and complicated management but have proved highly profitable where developed.¹⁰⁹ Although the Colombian systems represent perhaps the most intensive form of silvopastoralism, a wide range of silvopastoral systems exists across different continents and biomes.¹¹⁰

**Improved grazing systems in the Cerrado of Brazil.** Over the past several decades, Brazil has cleared millions of hectares of the Amazon rainforest, the Atlantic Coastal rainforest, and the diverse, woody savanna known as the Cerrado for grazing. Around two-thirds of the resulting 175 Mha of pasture are planted in *Brachiaria*, an adapted African grass. If supported with lime and fertilizers and other good grazing management, *Brachiaria* has the potential to produce as much as 140 kg of beef per hectare and more than 200 if combined with legumes or some crops in the final months of finishing.¹¹¹ But when *Brachiaria* is not fertilized, it becomes increasingly unproductive and productivity can fall below 30 kg of beef per hectare per year, comparable to other common and poorly managed systems.¹¹²

A variety of forms of improved management can provide increasingly large gains in production and reductions in GHG emissions per kilogram of beef in the Cerrado. In a recent analysis, the combina-
tion of adding fertilizer and lime every 10 years, supplying basic mineral licks, and making efforts to breed more productive cattle more than doubled production per hectare from unmanaged pasture and reduced production emissions by 30 percent per kilogram of beef. The same study found possible a fourfold increase in production per hectare and a 50 percent drop in production emissions per kilogram of beef through addition of legumes in the pasture area, a schedule of fertilizing pasture every five years, additional control of parasites, some crop supplements during animal finishing, and greater attention to the timing of breeding, so that calves are born at the start of the wet season.¹¹³

**Dairy farms in Kenya.** In sub-Saharan Africa, mixed crop-livestock systems produce the vast majority of milk and meat. Farmers maintain cows in stalls and feed them mostly a combination of crop residues and forage grasses that are either cut from wild growth or from deliberately raised forage grasses. Historically, milk production has been very low. Overall, production from sub-Saharan African herds is only around 1 liter per cow per day, compared to more than 16 liters per cow from Western European herds.¹¹⁴

There are many examples of improvements. One from Heifer International describes a small farmer who boosted production 350 percent through more regular tick control and deworming, increased use of dried napier grass and green maize stalks, and haying of wild grasses during wet seasons to feed during dry seasons.¹¹⁵ Overall, although many farmers in East Africa have made large gains by adopting napier grass, a highly productive and nutritious grass,¹¹⁶ great potential exists to expand and improve napier production through more precise matching of grass varieties to environments, improved application of fertilizers, and closer integration into cropping systems.¹¹⁷ Thousands of farmers in East Africa have also adopted high-protein shrubs, such as *Calliandra*. One study estimated that each kilogram of *Calliandra* leaves fed to cows will increase milk production by roughly one-third of a liter per day.¹¹⁸ Because this kind of shrub fixes nitrogen, intercropping also boosts yields both by improving soil productivity and by attracting stem borers—a problematic pest—away from maize.¹¹⁹

Another analysis in Kenya found that changes in feeding systems led to fivefold differences in methane emissions per liter of milk among seven districts, while a mere 10 percent increase in the digestibility of feed led to emissions reductions of almost 60 percent per liter of milk.¹²⁰ Additional research suggests the potential in much of sub-Saharan Africa to improve feed digestibility by roughly 10 percent through a range of measures including more digestible stovers or an increase in the use of concentrated grains to 2 kg per day. This study estimated that either intervention could reduce methane emissions per kilogram of milk or meat by two-thirds or more.¹²¹

## Overall potential for improvement

To entirely avoid any expansion of grazing lands by 2050, assuming no reductions in demand from our baseline, beef production per hectare of grazing land would have to increase by 82 percent instead of the 62 percent in our baseline, dairy production by 67 percent instead of the 53 percent in our baseline, and sheep and goat meat by 106 percent instead of the 71 percent in our baseline. Because we build large increases in productivity into our baseline, we are reluctant to hypothesize much larger increases. However, we imagine scenarios with larger or smaller increases in productivity per ha, achieved through greater increases in the efficiency of feed (the quantity of output per kilogram of feed measured in dry matter). Table 11-1 shows the scenario results. In our increased productivity scenario, pasture expansion falls from 401 to 291 Mha. However, if productivity were to grow at a rate 25 percent slower than in our baseline, pasture expansion would increase from 401 to 523 Mha.

Data and methodological challenges have so far prevented us from developing what we consider to be economically valid projections of livestock improvement potential. Analyses often suggest that improvements should be economical. Henderson et al. (2016), for example, analyzed different farms using the same basic production systems in Africa and found that some farmers could produce twice as much output per dollar of input. Yet no one has come up with a good way of estimating the cost of overcoming the various obstacles that stand in the way of these improvements.
<table><caption>**Table 11-1** | Global effects of 2050 livestock efficiency change scenarios on agricultural land use and greenhouse gas emissions</caption><thead><tr><th rowspan="2">SCENARIO</th><th colspan="3">CHANGE IN AGRICULTURAL AREA, 2010-50 (MHA)</th><th colspan="3">ANNUAL GHG EMISSIONS, 2050 (GT CO<sub>2</sub>E)</th><th rowspan="2">GHG MITIGATION GAP (GT CO<sub>2</sub>E)</th></tr><tr><th>Pastureland</th><th>Cropland</th><th>Total</th><th>Agricultural production</th><th>Land-use change</th><th>Total</th></tr></thead><tbody><tr><td>No change in livestock efficiencies between 2010 and 2050</td><td>2,199 (+1,798)</td><td>256 (+64)</td><td>2,455 (+1,861)</td><td>10.6</td><td>20.6</td><td>31.2</td><td>27.2 (+16.1)</td></tr><tr><td>2050 BASELINE and Coordinated Effort (pasture output grows by 53-71%<sup>a</sup> between 2010-50)</td><td>401</td><td>192</td><td>593</td><td>9.0</td><td>6.0</td><td>15.1</td><td>11.1</td></tr><tr><td>Less optimistic: 25% slower rate of ruminant feed efficiency gains</td><td>523 (+121)</td><td>203 (+11)</td><td>726 (+132)</td><td>9.2</td><td>7.1</td><td>16.3</td><td>12.3 (+1.3)</td></tr><tr><td>More optimistic: 25% faster rate of ruminant feed efficiency gains (Highly Ambitious and Breakthrough Technologies)</td><td>291 (-110)</td><td>182 (-10)</td><td>473 (-121)</td><td>8.8</td><td>5.1</td><td>13.9</td><td>9.9 (-1.1)</td></tr></tbody></table>

Notes: a. Pasture output growth (per hectare) between 2010 and 2050 is 62% for beef, 53% for dairy, and 71% for small ruminants.

"Cropland" includes cropland plus aquaculture ponds. Numbers not summed correctly are due to rounding. Numbers shown in parentheses are changes relative to 2050 baseline. Coordinated Effort scenario assumes same rates of growth as projected in the 2050 baseline.

Source: GlobAgri-WRR model.

One way of appreciating the challenge is to look more closely at Latin America. We project increased production of beef in Latin America between 2010 and 2050 to be 92 percent. That level of production increase would require comparable percentage rate gains in output per hectare of grazing land to avoid additional land conversion to pasture. In fact, our 2050 baseline projects very large-scale intensification in the region, with an increase in beef per hectare of 74 percent in Brazil and 78 percent in the rest of Latin America. But given the gap between demand growth and pasture efficiency, we still project 123 Mha of pasture expansion in Latin America.¹²²

What would it take for Latin America to produce these increased volumes of beef and dairy without expanding agricultural land? Of Latin America's roughly 400 Mha of grazing land devoted to beef production (by our calculation), roughly 100 Mha

are arid. The arid lands have substantially less potential for intensification without heavy reliance on crop-based feeds. To achieve our estimated 2050 production in Latin America without clearing additional pasture and while intensifying only the 300 Mha of wetter pasture lands, production on those wetter areas would have to more than triple, to around 162 kg/ha. But of these 300 Mha of wetter pasture, some grow native grasses, whose conversion to improved grasses would have adverse consequences for biodiversity—consequences that do not fit our criteria for a sustainable food future.¹²³ Other hectares are steeply sloped or in remote areas to which supplying inputs is difficult. Assuming intensification occurred on two-thirds of the wetter pastures (200 Mha), production per hectare would have to grow to around 215 kg/ha.¹²⁴ According to Cardoso et al. (2016), such increases are possible in the Cerrado, but only under the
most efficient present forms of management on some farms. This most efficient management would include fertilizing, plowing, and replanting grasses every five years, and either some substantial reliance on crops for feed in the last 90 days before slaughter or the successful introduction of legumes into pastures (which is usually challenging because animals selectively graze them).

The suggestion from this Cerrado analysis is that every wetter, feasible, and appropriate hectare of land in Latin America would have to intensify production to a maximum level to meet rising beef needs without expanding into forests and natural savannas.

## Recommended Strategies

Improvements in pasture receive a fraction of the global attention directed to improvements in cropland, but a sustainable food future will require a new level of global commitment. We offer four recommendations to address the most serious obstacles facing livestock farmers.

**Establish national and international goals for livestock efficiency gains—particularly ruminant systems—and develop technical programs to implement them.** Because the importance of sustainable livestock intensification is underappreciated, the establishment of specific national and international goals could help focus efforts. Efficiency can be measured by all the basic metrics discussed in this section: output per animal, per ha, and per kilogram of feed. But output per kilogram of GHG emissions does an excellent job of reflecting them all. Efficiency goals should reflect the carbon costs of land-use change and should recognize that different groups of farmers start from different levels of efficiency; targets should encourage improvement of each group.

**Develop analytical systems to track and plan ruminant efficiency gains.** Data about different farms and their intensification potential are limited in most countries, particularly those using diverse feeds. Modeling systems at the national or international level today are probably meaningful enough to identify large-scale potential for improvement. However, they must make a large number of assumptions because of the lack of data, and such models cannot be used to plan improvements at the level of individual farms or groups of farms.

To pursue efficiency goals, countries should develop data and monitoring systems that characterize their livestock production systems, estimate their productivity and emissions, and examine opportunities for improvement. Such systems should work at the farm level and scale up to the national level, and easily incorporate new information. Governments should institutionalize them in policymaking and nurture their development with the involvement of private research organizations.

Data and monitoring systems should also guide research with an enhanced commitment to filling in the many gaps in knowledge about livestock systems. For example, even though *Leucaena* shrubs achieved a breakthrough in Colombia's intensive silvopastoral systems by providing a fast-growing, flexible source of protein and soil nitrogen, *Leucaena* does not grow well in highly acidic soils. For Colombia's silvopastoral system to work in these soils, *Leucaena* will need to be adapted, or an alternative legume must be bred to perform the same functions. In much of Africa and Asia, livestock improvements rely on improved production of cut-and-carry forage grasses, and enormous potential exists to improve understanding of how these grasses are produced today and how they can be improved. In more advanced systems, advances in GPS technology make it easier to better analyze the management and consumption of existing natural grasslands¹²⁵ so forage can be exploited at the optimum state of maturity.¹²⁶

**Protect natural landscapes.** Even though pasture intensification will be economical in many locations, without efforts to protect natural landscapes, expansion of pasture will still occur wherever it is cheaper than intensification. Analysis by Embrapa, the Brazilian agricultural research agency, has shown that expanding pasture into forest can be cheaper than rehabilitating pasture.¹²⁷ One study in the early 2000s showed that a modest form of intensification, fertilizing degraded pasture, was cost-effective in the western Amazon but that a more intensive form, using some supplemental feeds, was not.¹²⁸ Another more recent study in the state of Mato Grosso estimated that intensive cattle raising in itself is not profitable unless it is particularly well-managed.¹²⁹ A modeling analysis of Brazilian pasture intensification published in 2015 found that intensification was strongly tied to higher land prices and lower transportation costs,
themselves related to market centers, and that on average, the intensification options were more expensive than expansion options by $80 per ha.<sup>130</sup> Not surprisingly, Brazil has tended to intensify cattle production in some locations while expanding cattle pastures in others. Between 2000 and 2006, for example, even as cattle density in the Brazilian Amazon greatly increased, the pasture area there still increased by roughly 25 percent.<sup>131</sup>

There are compelling ecological reasons to protect natural landscapes. In addition, intensification strategies may prove economically beneficial for a country in the long run because they stimulate the development of more sophisticated agricultural support services and because they may allow governments to better target regional infrastructure and support services. Intensely managed livestock also require more employment to substitute labor for land. But in the short run, some individual ranchers will still tend to prefer pasture expansion if it is allowed and if they are not required to pay the environmental costs of converting forests.

Countries with natural forests or other natural ecosystems that could be converted to grazing lands will need to enact policies to protect that land from conversion. Likewise, companies seeking the same outcome will need to incorporate avoided deforestation considerations into their purchasing decisions. Such actions must make the political, legal, market, and/or reputational cost of conversion higher than the near-term financial benefit of conversion. We discuss how this can be done in more detail in the final section of this report, "Cross-Cutting Policies for a Sustainable Food Future."

**Integrate programs to support intensification with a greater focus on feed quality.**

Livestock farmers face many obstacles to taking full advantage of intensification opportunities, includ-

ing lack of formal and secure tenure over land, high cost of inputs, and limited access to relevant technical information.<sup>132</sup> The evidence shows that market access also has a major impact on intensification. For example, farmers have little incentive to increase milk production beyond subsistence levels if they cannot easily sell their milk.<sup>133</sup>

The potential interventions to address these challenges are known, and include programs to strengthen tenure, create cooperative marketing efforts, improve transportation or retail networks to lower input costs, introduce social insurance to reduce food security risks, enhance education services provided by extension agents, create farmer-to-farmer networks, and form cooperatives. We discuss these issues in "Cross-Cutting Policies for a Sustainable Food Future." Many countries have programs targeting one or more of these issues, though they are often inadequately funded.

Often, however, farmers will need to overcome all these challenges simultaneously to be able to intensify. One option would be to create systems that target a variety of programs for a group of farmers committed to working together for sustainable intensification. In areas where forests or other natural ecosystems are at risk of conversion, or where grazing land has little intensification potential but could be restored as forests, these programs could support efforts to combine intensification with forest protection or restoration. For example, governments might allow these groups of farmers to compete against each other with initial proposals for improvement and commit resources to the most promising groups with the most ambitious forest protection commitments. Combining such efforts into programs that generate measurable reductions in emissions per kilogram of beef or milk and spare natural ecosystems should also increase the capacity of such projects to attract international funding as "climate-smart agriculture."
<figure><img src="image_105.png" /></figure>

Yuca Invito

CEC XXX-3

<figure><img src="image_106.png" /></figure>
# MENU ITEM: IMPROVE CROP BREEDING TO BOOST YIELDS

Because crop breeding has driven much of the world's previous yield gains, this menu item involves advancing crop breeding. Great promise exists both in boosting regular efforts to "incrementally" breed better crops, and in taking advantage of rapid progress in techniques of molecular biology.
## The Challenge

Breeding new crop varieties can increase yields in a number of ways. Breeding can result in plants that grow more densely, that direct more of their energy into the edible portions of plants, or that more efficiently use water and nutrients. New crop varieties can better exploit local day lengths and soil conditions, resist disease or pests, or cope with dry periods and other stresses. Breeding can increase the maximum yield that crops achieve under ideal, fully watered conditions, which is called the “yield potential.” Breeding can also help farmers achieve yields that are closer to the potential in real-world conditions, thanks to characteristics that better resist disease, periods of drought or flooding, or other sources of stress.

Although farmers increase crop yields in part by using better seeds and in part through better management (especially increased fertilizer use), disentangling the contribution of each is difficult. Green Revolution crops, for example, produced higher yields mainly when combined with fertilizer application and irrigation. Despite this challenge, typical estimates claim that, since the Green Revolution, breeding has been responsible for roughly half of all crop yield gains.¹³⁴ In the future, crop breeding will probably have to bear more of the burden because, as discussed, except in sub-Saharan Africa agriculture has already exploited most of the “easy” potential ways of increasing yields: adding more water, using chemical inputs, and introducing basic machinery.¹³⁵

To provide continuing yield gains, breeding will need to become more nuanced. In the past, much yield gain in the major cereals wheat and rice resulted from shifting biomass from vegetative parts to seeds and shortening and stiffening the stems so they could support more grain (resulting from higher fertilizer application) without falling over. These traits, which were largely responsible for the Green Revolution, are in some cases reaching their biological limits; crops can only grow so close to one another before they have no more space, and crops can only direct so much of their growth into edible portions before they will no longer stand upright. These limits, plus the need to boost crop yields even faster than in historical trends, present the crop breeding challenge.

## The Opportunity

Four major related opportunities exist to increase crop yields through improved breeding: speed-ing up crop breeding cycles, marker-assisted and genomics-assisted breeding, improvement of “orphan” crops, and genetic modification.

### Speeding up "incremental" breeding cycles in developing countries

Although there is a continuum of breeding efforts, it is helpful to think of breeding as focused on big “step-changes” in varieties, achieved through major changes in traits, on the one hand, and small continuous improvements, on the other. Major step changes in yield, disease resistance, or stress tolerance are often the result of incorporating rare genes with large and visible effects, or of changing from open-pollinated to hybrid crops. Such major changes involve concentrated efforts by researchers to develop new varieties. Famous historical examples include the creation of successful hybrid maize seeds in the United States in the 1930s; dwarf wheat and rice in the 1960s, which allowed crops to produce more seeds without stems breaking under the weight; and new breeds of *Brachiaria*—an African grass—developed in the 1980s and 1990s, which allowed the pasture grass to thrive in Brazil’s highly acidic soils. By contrast, continuous incremental improvements result from the steady accumulation of thousands of favorable genes with small effects. Incremental improvements result from a continuous process of selecting higher-yielding individual crops and breeding them.

Commercial breeding of maize in the United States sets the standard for the continuous incremental improvement that results from modern crop breeding. A few major seed companies follow a series of steps to regularly improve their varieties.¹³⁶ They create new in-bred lines of maize to assure genetic consistency, cross-breed these new lines to create new “hybrid” varieties (crosses of two lines), test the new results for performance and select new commercial varieties from the best performers, set out test strips widely across the corn (maize) belt of the United States every year, examine yields and other characteristics and select desirable performers, and finally leverage an extensive seed network so farmers quickly adopt the new commercial varieties.
Faced with competitive pressures, these seed companies have new breeding cycles that require only four to five years from one generation of products (hybrids) to the next. This timeline contrasts with public breeding programs in developing countries, which often take 10 years or more to develop a new generation of seeds, plus many more years to disseminate them. By overlapping their efforts, U.S. seed companies are releasing improved varieties of major cereals every two or three years. As a result, studies find that the average hybrid maize seed used in the United States is only 3 years old, compared to 17 years for maize in Kenya, and 13 years for wheat, and 28 years for rainfed rice in India.¹³⁷

A variety of techniques are available to speed up breeding. For example, breeding outside of the main crop-growing season (such as the winter or dry season) can double rates of improvement and in some tropical countries requires irrigation only of test fields.¹³⁸ Doubled haploid breeding can accelerate the breeding process by inducing plants to produce identically matching chromosomes in each pair within only two seasons, a process that normally takes six or seven generations. This technique makes it possible to purify strains of plants with desirable traits, which can then either be released as true-breeding varieties (in rice or wheat, for example) or crossed with other, similarly purified plants to form hybrids (as is common with maize).¹³⁹

Virtually every country in the world has some basic set of institutions for national crop breeding that receives financial support from the national government and technical support from international networks. But funding levels often vary from year to year. Breeding is a multiyear effort and requires well-trained breeders who develop knowledge over years of experience. Ultimately, funding that is both adequate and consistent is the key to successful crop breeding.

It is also difficult to get improved seed varieties rapidly into circulation. Although many analyses assume that farmers in developing countries reuse their own seeds from year to year, in many cases smallholder farmers purchase a significant proportion of their seeds from local markets or from fellow

farmers.¹⁴⁰ Yet only about 2.4 percent are “certified seeds” from private sector companies. Commercial farmers who have the funding to buy private sector seeds and can evaluate them are far more likely to buy these seeds more frequently, and to test new varieties offered by their seed supplier. Competition among seed companies in the United States and Europe also fosters sales efforts that lead to more rapid adoption. Government seed companies often lack these incentives. Because purchasing commercially provided seeds creates markets for more rapid variety development (by providing seed companies with a steady revenue stream), there are synergistic benefits between fostering improved distribution systems and more rapid adoption rates.

## The potential of marker-assisted and genomics-assisted breeding

Crop breeding has primarily improved crops by crossing different individual members of the same plant species, different varieties of the same plant species, or sometimes assisting self-pollination by the same plant to achieve consistent traits. To generate new genetic diversity with which to experiment, breeders occasionally have used mechanisms such as radiation to create new plant mutations. They then test to determine whether any mutations are favorable and, if so, spread them through conventional cross-breeding.

Until recently, breeders primarily bred new crops by crossing two individual members of the same species, which they select for breeding based on how those crop varieties performed in the field—while occasionally using estimates of whether they contained certain gene types (alleles). Breeders then repeatedly select offspring with the most desired traits for dissemination or for subsequent crossing. Even with the advent of genetic modification discussed below, conventional breeding has driven yield gains in part because most traits that lead to higher crop yields result from many genes and their interactions with environmental factors.¹⁴¹ Conventional breeding provides the means by which breeders can affect large numbers of genes (even without knowing precisely which genes or their genetic codes).
Even as breeding has continued in this way, molecular biologists have developed dramatically faster and cheaper methods of analyzing deoxyribonucleic acid (DNA), providing new mechanisms to accelerate and enhance crop breeding. One mechanism, called marker-assisted breeding, allows breeders to map and label portions of DNA associated with agronomically useful traits.

With these techniques, even without growing crops, breeders can identify those seedlings from among a large population that are most promising for further breeding. This approach reduces the time required to develop new crop varieties because breeders need not sow millions of plants or wait for individual plants to grow to determine which individuals to cross.¹⁴² Thus, while “low-tech” conventional breeding may require a minimum of 7 to 17 generations of crops to produce a new cultivar, marker-assisted breeding can cut this breeding cycle down to just a few generations.¹⁴³ The International Rice Research Institute (IRRI) demonstrated the potential of this approach in 2009 by introducing a rice variety that could survive submersion under water for up to two weeks. IRRI developed this variety in just three years after it identified the relevant genetic marker for flood tolerance, a trait found in a traditional variety grown in a flood-prone part of India. Since then, IRRI has delivered 10 additional varieties that are resistant to flash flooding in South and Southeast Asia.¹⁴⁴

Like genetic engineering, marker-assisted breeding by itself is primarily of value for simple traits determined by a single gene. But within the past decade, improvements in “genomics” have created opportunities to increase and accelerate yield improvements by analyzing groups of genes. Genomics applies DNA sequencing methods and genetic mapping to analyze the function and structure of whole (or large portions of) genomes—the complete set of DNA within a single cell of an organism.¹⁴⁵ Genomics also includes the evaluation of the large portions of DNA that do not “code” for new proteins but rather play critical roles by determining when genes are turned on and off.¹⁴⁶ A breeder that desires to breed-in many traits now may be able to predict through a combination of a DNA map and statistical analysis whether or not individual plants have all the genes needed to yield the desired traits.¹⁴⁷

Genomics has the potential to make conventional breeding not only faster but also better. Conventional approaches require that breeders use indirect methods to identify seeds with the favorable underlying genes, which they can confirm only once those genes express themselves in beneficial traits in actual plants. The new genomics-assisted techniques allow breeders to identify and breed for promising gene combinations that are predicted to occur when parents with complementary traits are crossed. Breeders can then test for the presence of these genes in offspring and push these combina-

<figure><img src="image_107.png" /></figure>
tions forward through continued breeding even if
the first generations of offspring do not themselves
express favorable traits. That may occur, for exam-
ple, because the trait, such as yield, only becomes
evident in large field plots and cannot be accurately
measured in single plants in a greenhouse.

In general, large commercial seed companies have
extensively incorporated genomic techniques into
their breeding programs.¹⁴⁸ Much crop breeding is
undertaken by the public sector, however, and the
achievements of these techniques are still limited
for several reasons, in part because they are new
and in part because the facilities to use such tech-
niques are less available in developing countries.¹⁴⁹

Although genomics is already speeding up plant
breeding, the extent to which genomics will enable
major new improvements remains unclear. Breed-
ing for complex traits that depend on many genes
and their relation to the local environment is
inherently complicated. Although identifying genes
is becoming easier, knowing what these genes do
and how they respond to a variety of environmental
settings is hard, time-consuming, and complicated.
For complex traits, the size of the crop population
under study must be large, the assessment of traits
should be reliable and replicable, and the popula-
tion of crops studied must be of the same variety.

Fortunately, technological advances are creat-
ing new capacities in techniques known as “high
throughput phenotyping.” They include using
sensing devices to monitor attributes of plant
growth in the field and robotic platforms that can
make reliable measurements of traits that have
been difficult to quantify, such as water use, photo-
synthetic capacity, root architecture, and biomass
production.

The information gathered will be cumulative. As
scientists identify the molecular functions of differ-

ent strands of DNA and their relationship to traits,
they gain increasing ability to predict what combi-
nations of DNA are optimal for specific crop types
and environments. In addition, breeding institu-
tions can share different responsibilities. Globally
oriented research institutions can engage in “pre-
breeding” that uses some of these new techniques
to develop promising plant material while local
institutions can incorporate promising germplasms
into local varieties. This kind of division of respon-
sibilities is occurring in partnerships between U.S.
and European universities, the CGIAR system, and
national organizations.¹⁵⁰

Improvement of orphan crops

The advances in marker-assisted breeding and
genomics create additional potential to breed
improvements into orphan crops.¹⁵¹ The term
*orphan crops* generally refers to crops that have
received relatively little research attention, often
because they are little traded on global markets.
Yet they are important for food security in many
regions.¹⁵² Orphan crops include sorghum, millet,
potatoes, peas, cassava, and beans. By one defini-
tion, 22 orphan crops occupied almost 300 Mha
in 2017 (Figure 12-1). Because of their importance
to poor smallholder farmers, improving orphan
crop yields to even half of their maximum potential
would have greater benefits for food security in
regions such as sub-Saharan Africa than improve-
ments in any other crops.¹⁵³

Marker-assisted selection and genomics should
make it easier to achieve quick yield improvements
in these less-studied crops in two ways. First, these
technologies can increase the pace of breeding pro-
grams. Second, these technologies may in the future
enable breeders to understand the gene combina-
tions that have already led to yield gains in more
intensely studied crops, and then select for them in
orphan crops.
<figure><img src="image_108.png" /><figcaption>**Figure 12-1** | Orphan crops occupy nearly 300 million hectares</figcaption></figure>

*Note:* Total harvested area of these 22 crops was 296 million hectares in 2017.

*Source:* FAO (2019a) using definitions of *orphan crops* from Naylor et al. (2004), except teff, for which data on area under cultivation are not available from FAO (2019a).
## Genetic Modification

Genetic modification (GM) typically refers to inserting specific genes—often from a different species—into the genome of a target plant. This approach differs from conventional plant breeding, which selects individual plants with desired traits and sexually crosses whole genomes from the same or very closely related species to produce offspring with random mixes of genes from the parent plants. To date, most GM crop traits have been inserted into just four high-value crops: maize, soybeans, canola, and cotton. Of the 190 Mha annually planted in GM crops—approximately 12 percent of global cropland¹⁵⁴—the vast majority are in the United States, Canada, Brazil, Argentina, India, and China.¹⁵⁵

GM crops overwhelmingly employ one of two basic traits. The first conveys absolute resistance to the herbicide glyphosate. This allows farmers to spray glyphosate—originally effective against virtually all weeds—directly over crops that the herbicide would otherwise kill. This trait is most used for soybeans and maize. The second trait is the production of a natural insecticide from the bacterium *Bacillus thuringiensis* (Bt), which is particularly effective against insect larvae such as those of the corn rootworm and the corn borer. Bt traits are used predominantly in maize and cotton.

## Genetically modified crops: the debate so far

Genetic modification has potential to improve crop breeding and increase yields, but it is the subject of by far the most contentious public policy debate surrounding plant breeding. We believe that the merits of GM technologies lie primarily with traits other than glyphosate resistance or Bt, as we discuss below. But because the public debate about GM crops has focused so heavily on these two traits, we summarize the debate here. The debate also encompasses extending the use of these crops to Africa.

The debate focuses on four issues: food safety and human health, environmental toxicity and pest resistance, crop yield effects, and economic effects on farmers, particularly a shift of profit and control to major corporations. We draw heavily on a 2016 study by the National Academies of Sciences, Engineering, and Medicine, which, based on our own independent review of the evidence, does a careful job of presenting the evidence.

## Food safety and human health

Fear that GM crops are not safe for human consumption drives much of the public opposition to genetically modified organisms (GMOs). At this time, there is no evidence that GM crops have harmed human health.¹⁵⁶ The vast majority of studies have found no adverse health effects.¹⁵⁷ Even GM critics argue that they oppose GM crops mainly because the risks have been insufficiently studied.¹⁵⁸

Much attention has focused on possible links between glyphosate and cancer. Significantly, this debate is not about whether the genetic modification itself causes cancer, but on the toxicity of glyphosate, whose use is enabled by breeding glyphosate resistance into crops. Most studies have found little to no evidence of glyphosate causing cancer in humans.¹⁵⁹ One of the most alarming studies of GM crops claimed to find a large increase in cancers in lab rats. However, the sample involved only 10 rats of each gender, and food safety institutes criticized it for a high likelihood of random error.¹⁶⁰ Over the objections of the authors, the *Journal of Food and Chemical Toxicology* retracted the study.¹⁶¹ A subsequent review of the study by the U.S. National Academies of Science, Engineering, and Medicine was less critical of the study's method but still did not find that it showed statistically significant evidence of concern.¹⁶² In its 2016 assessment, the U.S. National Academies of Science, Engineering, and Medicine found no differences in cancer rate trends of different cancers in the United States and Europe, despite the U.S. embrace of these crops and Europe's resistance.
Although the evidence as a whole does not show health effects, that does not mean glyphosate itself is harmless. Many studies of glyphosate, whether epidemiological or using animals, have suggested pathways through which glyphosate or the chemicals that occur as it is broken down by microorganisms in the environment could cause health effects, possibly even including cancer.¹⁶³ One concern is a potential link between high exposure to glyphosate, generally in farmworkers, and a higher rate of non-Hodgkins lymphoma.¹⁶⁴ As another example, even though the U.S. Environmental Protection Agency found that glyphosate is not an endocrine disrupter through traditional pathways, other researchers identified possible effects through more unusual pathways.¹⁶⁵

The evidence on Bt crops suggests that health effects have probably been positive overall because Bt crops, so far, have enabled many farmers to reduce significantly their overall use of insecticides. These insecticides, particularly those used in China and India, are generally more toxic than Bt.¹⁶⁶ That is true even though in some areas Bt crops have led to an increase in “secondary” pests—pests not controlled by Bt—and reducing the secondary pests can, in turn, require more pesticide use. However, several studies show that Bt crops can also contribute to reductions in secondary pests¹⁶⁷ and, thanks to reduced overall use of insecticides, can even promote beneficial insects that reduce pests on neighboring maize, peanut, and soybean fields.¹⁶⁸ Bt crops have also reduced use of insecticides on non-Bt crops by reducing the presence of major pests, such as corn stem borer.¹⁶⁹

Although neither glyphosate nor Bt is without health concerns, the human health evaluation of Bt and glyphosate-resistant crops depends not on their absolute health risks but on their health risks relative to the alternatives. For most farmers, the alternative means use of other pesticides that raise more concerns than glyphosate and Bt. The scope and increase in use of both glyphosate and Bt crops warrant continued health studies, but the evidence to date is that these GM crops have not increased health risks compared to the alternatives and, in the case of Bt, may be contributing some health benefits.¹⁷⁰

## Environmental toxicity and pest resistance

Much of the environmental criticism of glyphosate-resistant and BT crops acknowledges the advantages of reduced toxicity in the short term but argues that they may lead to greater toxicity in the long term.

Any increased reliance on specific pesticides can lead to more rapid development of resistance to those pesticides in weeds or invertebrate pests so, over a longer period, use of these GM crops could lead to the loss of benefits from the lower toxicity of glyphosate and Bt. There have been examples of crop infestations by insects that are resistant to some Bt proteins. Resistance to the effective proteins in fall armyworm emerged within three years of introducing multiple types of Bt in Brazil.¹⁷¹ In South Africa, the one sub-Saharan African country to use Bt maize, a variety was introduced in 1998 but some resistance evolved in stem borers by 2006. That form of Bt maize was withdrawn from the market in 2013 and replaced by a new variety, to which insects have also started to develop resistance.¹⁷² A 2016 study reported 16 separate cases of Bt resistance, each of which took an average of only five years to evolve.¹⁷³

One strategy to reduce the evolution of resistance has been, where feasible, to introduce crops with multiple Bt proteins. Bt crops can generate a variety of proteins that harm insects, and the types of proteins and level of harm vary. Breeding multiple Bt proteins into crops may reduce the likelihood of resistance developing because even genetic mutations that lead to resistance to one Bt protein will not give pests an advantage if they remain vulnerable to the other Bt proteins.¹⁷⁴ But forms of cross-resistance to multiple Bt proteins can also evolve, although the science is complicated and depends on the proteins.¹⁷⁵ Stacking of Bt proteins may help reduce the rate of evolution of resistance, but it will probably not stop it entirely.

Growing herbicide resistance is a significant concern for glyphosate-resistant crops. Twenty-four weeds in the United States have become resistant, including several that are major problems, particularly for soybean production.¹⁷⁶ Large seed companies have responded by introducing varieties of
soybeans that are also resistant to older herbicides, such as 2,4-Dichlorophenoxyacetic acid (2,4-D) and dicamba. Relative to insecticides, these chemicals (like other herbicides) have lower human toxicity concerns. But these older herbicides pose significant environmental concerns as they are far more toxic to broad-leaved plants and more likely to "drift" on winds from farm fields to adjacent lands and damage nontarget plants.¹⁷⁷

A key strategy to reduce the evolution of pest resistance is for farmers to continue to plant crops without the GM traits on some of their fields, creating pest "refuges" where non-Bt or non-glyphosate-resistant crops can be grown. In these areas, weeds and insects without resistant genes would continue to survive. They can then breed with insects that evolve resistance after exposure to GM crops in other fields and the offspring will die when exposed to Bt plants or glyphosate (so long as whatever resistant gene evolves is recessive). The effectiveness of this pest refuge approach varies with the toxicity of the Bt plant and the size of the refuge, among other factors. In general, countries with larger refuges and well-managed farms tend to delay emergence of resistance, and in some cases have prevented resistance from appearing for roughly 20 years.¹⁷⁸ But farmers do not always follow the practice. Small farmers in particular struggle to set aside and maintain refuges, refuge area requirements are sometimes too small, and resistance can evolve anyway.

The emergence of resistant weeds is one reason why glyphosate-resistant traits have not always led to a reduction in the aggregate use of herbicides. Usage depends on the crop. The total application of herbicide active ingredients to U.S. maize crops declined by 18 percent from 1991 through 1994 even as herbicide use shifted toward glyphosate, a safer product.¹⁷⁹ Yet the overall herbicide application to soybeans in the United States grew by 70 percent over the same period, both because the application rate for glyphosate increased and because glyphosate use did not significantly reduce the application of other herbicides by volume.¹⁸⁰

In addition to risks that glyphosate-resistant crops may not ultimately reduce use of other pesticides, increased application of glyphosate is also a concern. Even if it is less toxic to humans and less likely to drift than some other pesticides, glyphosate still likely has adverse effects on some other organisms. The greatest risk is probably to some aquatic species.¹⁸¹ At least one study raises concern that glyphosate may be harming honey bees,¹⁸² whose hive collapses in the United States have posed major challenges to pollination and agriculture itself. As with other pesticides, these environmental effects are seriously understudied. Because the global use of glyphosate is high and continues to expand, continued research into both human and environmental effects of glyphosate remains appropriate.

## Crop yield effects

Whether glyphosate-resistant and Bt crops have led to yield gains is open to some debate. Neither trait by itself was designed to boost the yield potential of these crops, as opponents of GMOs point out. In addition, the introduction of a new gene often leads to "yield drag" because conventional versions of those crops continue to improve during the time it takes breeders to integrate the new gene into local crops. Yields eventually catch up for a particular GM gene,¹⁸³ but the insertion of new genes will repeat the drag effect in the future, although more rapid breeding techniques generally should reduce this drag.

Yields improve not only when maximum yield potential increases but also when farmers are better able to control stresses, such as pests, on their crops. Easier weed management enabled by use of glyphosate-resistant crops, or greater control of insects that attack crop roots enabled by Bt, could in theory boost yields. In addition, greater profitability thanks to reduced losses caused by pests could lead farmers to make other investments that improve overall yields. Therefore, the question is what net effects on yields GM crops have produced in the real world.
<figure><img src="image_109.png" /></figure>

A huge number of studies, using almost as many different approaches, have tried to answer this question. They have offered a range of answers, but fundamental methodological challenges make it difficult to get a definitive answer. Studies that compare test plots of well-managed GMOs with well-managed alternative plots often find little or no effect on yields, particularly from glyphosate-resistant crops.¹⁸⁴ However, their methods make them less likely to recognize the potential for real-world gains from the greater ease of pest management that GM crops may allow because, for example, Bt reduces the need to apply pesticides at all and it is easier to apply glyphosate on top of crops rather than carefully around them. Conversely, comparisons of real-world yields obtained by farmers who adopt and farmers who do not adopt GM crops are confounded by the fact that early adopters tend to be farmers already achieving higher yields. Also, farmers who pay more for GM seeds are likely to plant them on better fields and pay more attention to them.¹⁸⁵ Similarly, studies based on country comparisons tend to ignore the fact that countries adopting GM crops already had high and rising yields.¹⁸⁶

In 2016, the National Academies of Science, Engineering, and Medicine produced a particularly careful review of the evidence from the United States, building on a report by the National Research Council in 2010. The bulk of the evidence shows different results for glyphosate-resistant crops and Bt crops.

The net effect on yields of glyphosate-resistant crops has probably been either zero or very small.¹⁸⁷ There are wide differences in study results, and substantial uncertainties because of methodological differences between studies, but this is the most reasonable conclusion that can be drawn from the evidence to date. By contrast, the evidence tends to show some yield gains from Bt crops. The 2010 study concluded that Bt had led to 5–10 percent yield gains for cotton¹⁸⁸ and perhaps smaller gains for maize.¹⁸⁹ The 2016 study found repeated evidence of gains of this size in both maize and cotton, based on studies of direct plot comparisons; some studies showed larger gains.¹⁹⁰ Yet the 2016 report found that despite this evidence from farm-level studies, U.S. yields in the major GM crops had not grown any more rapidly after the introduction of
GM varieties than they had before. The authors also found no reason to believe that yields might not have grown as fast without the advent of GM crops. The report plausibly concluded, “Although the sum of experimental evidence indicates that GE [genetic engineering] traits are contributing to actual yield increases, there is no evidence from USDA data that they have substantially increased the rate at which U.S. agriculture is increasing yields.”¹⁹¹

In developing countries, the evidence for yield gain is stronger and intuitively more likely, both because many farmers will have less access to other pesticides and because pests tend to flourish more in warmer environments, which are more common in developing countries. Most of the studies have focused in particular on Bt cotton and have found increases in yields.¹⁹² The largest apparent success occurred in India, which experienced yield gains in cotton of 56 percent between 2002 and 2011, which corresponded to the introduction of Bt cotton. Doubters properly point out that nearly all of this rise occurred from 2002 to 2005, when official Bt cotton adoption rates were only 6 percent.¹⁹³ Yet other researchers noted that even in this period, some farmers were unofficially adopting the seeds, suggesting that the 6 percent adoption rate was an underestimate and pointing to a significant role of Bt cotton in yield gains.¹⁹⁴ Although improved management of cotton overall probably played an even larger role, the evidence tends to justify claims that Bt cotton helped significantly increase yields, particularly in locations where pests addressed by Bt were most prevalent.¹⁹⁵

Overall, the weight of the evidence supports the proposition that GMOs to date have led to meaningful but not large yield gains on average for Bt crops. Nonetheless, precise data are lacking.

## Effects on costs, labor productivity, and equity

A fourth concern with genetic engineering is the expense and control of GM crops. Most farmers need to buy new seeds annually and GM seeds cost more than traditional varieties. The concern is that private seed companies will extract more of the income generated by farming, leaving farmers with less.

Although seeds cost more, they also bring economic benefits. In addition to yield gains, particularly for Bt crops, both major types of GM crops reduce

the work and expense of pest control. Studies have generally found sizable savings from reduced labor and, in the case of Bt, from the costs of alternative insecticides, which explains the high rates of adoption of these seeds in countries like Brazil, the United States, and Argentina.¹⁹⁶

The question is whether these economic benefits outweigh the higher seed costs and improve overall profitability. The answer is largely determined by the pricing policies of companies, which naturally seek to profit to the extent they can from their seeds, and from the level of competition among companies. But farmers generally will not buy the seeds unless they make their farms more profitable. Not surprisingly, studies have generally found that those farmers who purchased seeds found them profitable.¹⁹⁷ The evidence suggests that GM seeds may not be profitable in years or locations with low pest pressures.

The evidence has been more mixed where small farmers are concerned, although many studies have found substantial benefits for small farmers.¹⁹⁸ There are prominent examples of farmers in parts of India and West Africa beginning then abandoning the use of Bt cotton.¹⁹⁹ Higher seed costs, even if more than compensated for by increased yields, can be more of a burden for small farmers than large farmers because they are often less able to raise the initial capital needed to purchase seeds and other inputs. Higher input costs also increase the risks associated with bad weather and crop failure. Small farmers may be less able to balance these added losses in bad years with the greater benefits in good and average years. This is the case even though small farms can be as productive as large farms, or more so, in many farming systems.²⁰⁰ The availability of credit to small farmers is one important determinant of whether they can benefit from GM crops.

Despite this mixed record, the evidence that GM crops could enable small farmers to farm better is strong. The impediment appears to be the price. If good seeds could be provided at low cost or without a premium, the benefits could be compelling. For example, maize farmers in Africa face substantial challenges from insects such as stem borers that can be controlled with Bt.²⁰¹ They are also facing substantial losses from the fall armyworm, recently arrived from the Americas. With support from
USAID and the Bill & Melinda Gates Foundation, public breeding institutions are working to provide Bt maize in Africa that works against such pests without the price premium normally paid elsewhere.

Outstanding challenges to introducing GM crops more widely in Africa are thus both technical—for example, whether a Bt variety can be developed to kill fall armyworm and other threats to maize in Africa without quickly leading to resistance—and economic. Most small farmers in Africa do not purchase seeds annually, either because they cannot afford hybrid seeds or because higher-priced seeds are too risky given weather variations. Introducing genetically engineered crops without addressing these issues is unlikely to contribute to yield increases or socioeconomic development.

## Some conclusions regarding the debate over major GM crops

Although claims both for and against GM technology have often been overstated, the best evidence is that GM technology has already provided some yield gains from Bt crops and has probably reduced toxicity both to humans and the environment, relative to the use of alternative crop varieties that require more pesticide use. For many farmers, both crop traits have led to increased profitability and reduced labor requirements, although the experience of small farmers has been varied. Less positively, both glyphosate itself and Bt, like other pesticides, pose concerns. The big, unknown question is whether or how long these traits can remain functional before being overwhelmed by resistance, and what would replace them if and when resistance undermines their utility.

Although the controversy over today's dominant GM crops has led us to provide this summary, we do not believe that debate over these particular GM traits should dictate policy about the entire technology of genetic engineering. The case for using this technology is compelling when the full range of potential gains and costs is taken into consideration.

Regarding health effects, there is a scientific consensus that food safety does not justify rejecting genetic modification in general. That is the view of such entities as the U.S. National Research Council, the European Joint Research Centre, the American

Medical Association, the American Academy for the Advancement of Science, and the combined National Academies of Sciences, Engineering, and Medicine.²⁰² There is also a general consensus that while GM technology enables a range of crop modifications, some of which should appropriately require significant safety testing, the basis for regulation should generally be the types of changes in a crop rather than the method for generating them.²⁰³ Even conventional breeding techniques can include such methods as using radiation to generate more mutations.²⁰⁴ GM technology is probably more capable of altering plants in ways that raise new risks, but many uses of GM technology are unlikely to pose any more significant risk than conventional crop breeding.²⁰⁵

In addition, while the market power granted by patents to private companies can raise equity and even efficiency concerns in any industry, patents play an important role in the seed industry that is broader than their application to GM technology. And GM technology does not always involve private patents. The public sector can also be a source of GM innovation, with the technology then licensed freely.

## Use of genetic modification to resist diseases

One important reason not to allow the debate over Bt and glyphosate-resistant crops to dictate GM policy is the potential uses of GM technology to breed pest-resistant traits into crops under serious pest attack. In Hawaii, for example, papayas would probably have been wiped out without the benefits of GM technology. Hawaiian papayas faced a virulent virus but were protected by insertion of genes from the virus itself into the papaya, generating a kind of plant immune response.²⁰⁶ Because of public resistance to GMOs, this variety has not spread much to the developing world.²⁰⁷ Likewise, current work demonstrates the potential for controlling potato late blight worldwide with GM technology.²⁰⁸ Transgenic potato varieties under trial in Uganda are unaffected by this pathogen.²⁰⁹ GM soybeans with resistance to Asian soy rust are under development by a major seed company: this disease causes annual losses of around $2 billion in Brazil, and the chemical sprays used for disease control are losing their efficacy.²¹⁰ GM tomatoes have demonstrated resistance to bacterial spot in successive years of field trials in Florida, where the disease has been
the number one endemic disease problem affecting
tomatoes for over 40 years.²¹¹

Although data sets are incomplete,²¹² studies
estimate that various diseases, animals, and weeds
cause yield losses of 20 percent to 40 percent of
global agricultural production.²¹³ Crop diseases can
originate from many different sources, including
viruses, bacteria, fungi, oomycetes, nematodes, and
parasitic plants. Scientists have started to under-
stand that, like animals, plants can respond to and
defend themselves against infections and parasites.
Although plants, unlike animals, do not have
mobile defender cells such as antibodies, each cell
relies on its own immunity and responds to sys-
temic signals emanating from infection sites.²¹⁴ The
plant has proteins that detect pathogens and trigger
immunity responses, including signals for a cell to
die to prevent further spread of the disease.²¹⁵

When selecting for disease-resistant crop variet-
ies, breeders are essentially selecting for genes
that will code more effective detector chemicals.²¹⁶
But using conventional breeding takes time, and
pathogens are often able to overcome resistance
conferred by a single, major gene.²¹⁷ By identifying
the genes that promote pathogen susceptibility and
removing them, or by identifying the genes that
promote pathogen immunity and adding them, GM
plant breeding can limit plant vulnerabilities and
enhance resilience.

The world's crops are likely to become increasingly
exposed to a greater variety of diseases because
the expansion of trade and travel makes it easier
for disease pathogens to move around and because
warmer, wetter weather overall makes it easier for
pests to thrive. In addition, any yield breakthroughs
by particular crop varieties encourage other farm-
ers to use the same varieties. Broad adoption of the
same or similar varieties increases resistance devel-
opment in the disease organism, and major crops
may become more susceptible to global diseases.²¹⁸
Genetic techniques do not displace conventional
breeding but allow for more varied and faster
responses to diseases in some cases.

Emerging new techniques of genetic identification
and modification

When deliberate genetic modification of DNA to
improve seeds first began, the primary technique
involved a kind of “gun” that injected hundreds
of copies of a gene into a cell in the hope that the
gene would attach itself somewhere and express
itself. Only by growing the offspring could scien-
tists determine whether the new genes were doing
anything. The technique was essentially a time-
consuming form of trial and error, which greatly
favored large companies because only they could
afford the scale of effort. Over time, biologists have
developed a variety of alternative techniques that
could deliver genes more precisely, in less time-
consuming and expensive ways.

In 2013, scientists reported dramatic progress
with gene editing using an evolving method,
called CRISPR-Cas9 (CRISPR). Although some of
what this method allows can be achieved by other
methods,²¹⁹ CRISPR is far more agile, inexpensive,
and quick. CRISPR allows biologists to precisely
target genes at any location in strands of DNA to
turn genes on and off at will. It also allows them to
cut and insert new genetic material of their design
in precise locations.²²⁰ Scientists can also insert
genetic switches into plants that will activate genes
only if they are exposed to certain chemicals or
light. Each year since 2013, scientists have been
announcing new variations on the technique that
offer a range of new options. For example, scientists
can now edit individual “base pairs” of DNA rather
than entire genes. Among the other opportuni-
ties provided by CRISPR, scientists can study and
modify the 98 percent of DNA that does not pro-
duce proteins but much of which has other impor-
tant, though little understood, functions.

CRISPR is so new that no one can confidently
predict which advances it will ultimately generate
in crop breeding. Breeders caution that at this time
there is limited knowledge of what the different
parts of plant genomes do. In addition, most crop
yield gains result from multiple gene interactions,
so the process of conventionally breeding desired
plants with each other is likely to continue to drive
the majority of yield gains for the foreseeable
future. Yet CRISPR offers many new opportunities:
* The process enables gene editing to occur with less yield drag. This drag results from taking one crop variety with a desired special trait but not necessarily other high-yielding qualities and cross-breeding it multiple times with elite, high-yielding varieties to generate a high-yielding variety with that same special trait. CRISPR enables breeders to introduce specific traits directly into elite varieties, circumventing the need for cross-breeding multiple times.

* CRISPR makes it easier for plant breeders to turn genes off, breed a variety, and then quickly obtain information about what the gene does. Over time, knowledge of the functions of different parts of the genome should accumulate and enable more deliberate breeding.

* CRISPR enables gene editing to achieve more complex results because sequentially using CRISPR makes it easier for researchers to alter multiple genes in a plant as well as to influence noncoding DNA, which regulates whether genes are expressed.

Combined with improved genomic information, the new potential to deliberately and intelligently edit DNA seems likely to offer high potential for crop yield improvement. The new techniques also make it possible for much smaller research teams to use genetic modification techniques. This could reduce the likelihood that genetic modification will be dominated by a few, large companies. But it is also possible that small companies will help develop new traits then sell them to larger companies to get the new traits through expensive regulatory processes. In addition, CRISPR is unlikely to alter the fact that large companies dominate sales of some crop seeds, such as maize; the result could be to give large companies ultimate control over the seeds even if traits are developed elsewhere.

At the same time, the ease of the new technique also raises issues of health and environmental safety because the technique is likely to become widespread. Even talented high school students can now learn to do genetic modifications. How the world will manage these new techniques raises questions that go far beyond crop breeding.

## Recommended Strategies

The combination of the great need for yield gains and new technologies to map or edit DNA makes a strong case for increased dedication to crop breeding. We offer four recommendations:

### Boost breeding budgets

Substantial investments by a wide range of institutions will be required to improve breeding where it is now slow and take advantage of new technologies.²²¹ The challenge is particularly acute in developing countries, where these innovative approaches to plant breeding are still essentially out of reach for most public-sector researchers. Developing countries need more scientists trained in modern breeding technologies, more transfer of these technologies from developed countries, and new data management systems and computational tools to support market-assisted and genomics-assisted breeding. Reports of agricultural research spending do not separate out crop breeding and are incomplete, but, overall, the world probably devotes only around 1.4–1.7 percent of agricultural GDP to agricultural research and development (R&D), which is less than the rate of total research spending relative to the total global economy (2.1 percent).²²²

Limited R&D funding is compounded by the high volatility of funding in the world's poorest countries, which in part depend on—and therefore respond to—the interests of international donors.²²³ But crop breeding requires stable funding because breeding is inherently a gradual and cumulative process. A good example is the funding for the CGIAR network of agricultural research institutions, which were set up in the 1960s as part of the Green Revolution effort. After many years of stagnation, CGIAR's budget grew rapidly after the food crises in 2008–11, from $707 million in 2011 to $1,067 million in 2014. However, its budget declined again to $848 million in 2017.²²⁴ The need for increased and consistent agricultural R&D is discussed in more detail in the final section of this report, “Cross-Cutting Policies for a Sustainable Food Future.”
## Share genomic advances

Public and private sector researchers can accelerate yield enhancements by developing and publicizing basic genomic data and methods. The Genomes Online Database (GOLD)²²⁵ is designed for such a purpose. Likewise, Mars Incorporated paid for the genetic sequencing of a common variety of cocoa and then publicly released it without patent in 2010 to speed up research on improving yields for the plant.²²⁶ In general, mapped genomes of major row crops are now being shared. Going forward, sharing information as it is discovered about what different DNA sequences actually do will be equally important.

## Leverage new technologies

Crop breeding programs should take full advantage of advances in new technologies. This lesson means that conventional breeding should embrace marker-assisted and genomics-assisted breeding, supported by better data management, sensors, and other tools to more quickly and cheaply identify the functions of different genes.

Whatever the debate about Bt and glyphosate-resistant crops, they represent only a few of GM technology's potential uses. Breeding disease-resistant traits into crops under serious threat is a problem to which genetic engineering may, in some cases, be the only solution if the crop is to be saved. CRISPR opens up a wide range of additional possibilities to increase yields in subtle ways, sometimes by adding new genes, and sometimes by influencing when genes turn on and off. These techniques also hold out promise for improving the environmental performance of crops, as we discuss in Chapters 27 and 28, by reducing emissions from nitrogen fertilizer use and rice cultivation. Although new regulatory systems will be needed to address the broad ease-of-use of these technologies (with implications that go far beyond crop breeding), the techniques offer too much opportunity for crop breeding to ignore them.

## Increase research on orphan crops

Researchers at universities, agriculture agencies, and agricultural companies should broaden their scope beyond the most intensely researched crops—maize, wheat, rice, and soybeans—to give increased attention and funding to orphan crops. Advanced plant breeding tools like CRISPR may help quickly improve orphan crops, which often have intractable breeding improvement challenges. Sorghum is a good example, with many quality and productivity problems, especially in the numerous varieties cultivated in Africa. As genes of interest are identified and linked to important phenotypes, in a wide variety of ways CRISPR holds potential to improve orphan crops more quickly, and breeders are already reporting a variety of rapid improvements.²²⁷

Some movement in this direction is under way. In 2003, CGIAR launched its 10-year Generation Challenge Programme to improve crops in drought-prone and harsh environments through genetic diversity and advanced plant science. From 2009 to 2014, the program focused on drought tolerance for nine crops, six of which are orphans: beans, cassava, chickpeas, cowpeas, groundnuts, and sorghum.²²⁸ In addition, CGIAR has launched a research partnership initiative on grain legumes. Furthermore, the African Orphan Crops Consortium²²⁹—consisting of companies, nongovernmental organizations, and international institutes—is undertaking an effort to sequence the genomes of 100 little-studied food crops in Africa. Although promising, the research dollars involved are still small. By 2014, the consortium had raised $40 million per year from developed countries, with a promise of $100 million more from African countries.²³⁰ Nevertheless, more efforts to improve orphan crops and research funding are needed.

For more detail about this menu item, see "Crop Breeding: Renewing the Global Commitment," a working paper supporting this World Resources Report available at www.SustainableFoodFuture.org.
<figure><img src="image_110.png" /></figure>
# MENU ITEM: IMPROVE SOIL AND WATER MANAGEMENT

Many agricultural soils are degraded, and degradation is particularly acute in many areas where yield gains are most needed for food security. This menu item explores the potential to boost yields by restoring these degraded lands through practices such as agroforestry, water harvesting, and fertilizer microdosing.
## The Challenge

Although reliable data are lacking, FAO estimates that 25 percent of all cropland suffers from significant soil degradation.²³¹ Sources of degradation include water and wind erosion, salinization, nutrient depletion (of nitrogen, phosphorus, and potassium), and loss of soil organic carbon.²³² Although protecting and rebuilding agricultural soils is the foundation of agricultural “conservation,” and although many projects have focused on such efforts in Africa, soils there continue to degrade.

Land degradation is of special concern in the world's more arid croplands, often called “drylands,” although we are not referring here to grazing areas too dry for growing crops.²³³ Drylands cover 41 percent of the earth's surface and account for approximately 44 percent of global food production.²³⁴ About 43 percent of Africa is drylands²³⁵ and we focus in this chapter on sub-Saharan Africa. One challenge facing drylands is that rainfall levels often do not permit agricultural production to grow to match high rates of population increase, which can lead to overuse. A 2016 World Bank analysis examined the challenges in African drylands, highlighting population growth as a central stressor. While sub-Saharan drylands are expected to expand by 20 percent in some scenarios, the population in these areas is expected to grow by 58–74 percent by 2030, leading to overuse and land degradation, as well as possible social conflict.²³⁶

Loss of soil organic carbon is a particular challenge. Organic carbon helps soils hold moisture and provides the kinds of chemical bonding that allow nutrients to be stored but also easily exchanged with plants. Soil organic carbon originates from decomposed plants. Because microorganisms in nearly all soils constantly break down soil organic matter and release the carbon into the atmosphere, maintaining soil organic carbon requires continual

replenishment. In the case of cropland, replenishment comes from the decomposition of plant roots and residues, or from the addition of material such as manure. Loss of soil organic carbon is also problematic because organic matter contains virtually all of the potentially plant-available nitrogen and 20–80 percent of the phosphorus in soils.²³⁷ In fact, if cropping removes more nitrogen than it adds through fertilizer or nitrogen fixation, soil organic carbon will decline because the nitrogen must come from the breakdown of existing organic matter.

African soils are not only low in organic matter but have long been losing carbon and nutrients.²³⁸ These losses probably result in part from insufficient replenishment of carbon and in part from insufficient addition of nitrogen.²³⁹ The problem has been exacerbated in sub-Saharan Africa by adverse conditions for carbon and nutrient retention. The combination of old soils and high temperatures creates conditions where thriving microorganisms are able to consume, respire, and therefore transfer the carbon in soils into the air year-round.²⁴⁰ Organic matter's ability to retain water is particularly important in this region because of the highly variable rainfall.²⁴¹ The growing season is also often short, and a relatively small percentage of rainfall is actually used by growing crops.²⁴² Multiple studies have now documented that low organic matter reduces crop response to fertilizer application and makes fertilizer application uneconomical for vast areas of farmland.²⁴³

Overall, the low levels of organic matter in African soils create a vicious circle because they lead to low yields, which in turn lead to less replenishment of soil carbon by crop roots and residues, and thus further losses in soil organic matter. But where crop yields are high, carbon levels not only can be maintained but even increased. Several papers have estimated that this is the case in China.²⁴⁴