# Trade-offs between greenhouse gas abatement and profitability of alternative management practices on Australian grain farms

Elizabeth Meier ±<fnref n="1" />, Peter Thorburn <fnref n="1" />, Marit Kragt <fnref n="2" />, Nikki Dumbrell <fnref n="2" />, Jody Biggs <fnref n="1" /> & Stuart McAlpine <fnref n="3" />

## 1 Introduction

Under proposed Emissions Reduction Fund policy, Australian farm owners can be paid for greenhouse gas (GHG) abatement if they bid the least cost delivery price through an auction process. Farm owners therefore need information about the effect of practices on soil carbon and nitrous oxide (N₂O) emissions, productivity and gross margins. However, practices that sequester carbon - thus reducing carbon dioxide (CO₂) emissions - may increase N₂O emissions. The potential trade-offs between these emissions pathways are not always obvious and may reduce the potential for Australian grains farms to mitigate GHG emissions. In addition, suitable mitigation practices may vary across regions, reduce whole farm profitability or fit poorly within farm management. To better understand all of these trade-offs, we estimated the net, whole-farm GHG mitigation balance and financial impact of various management practices applicable to Australian grains farms.

## 2 Materials and Methods

We established six representative farms in collaboration with farmer groups across Australia. The usual cropping systems and management employed by the farmers formed the baseline for analyses. In this paper, we present results for one of the case study farms. Baseline and alternative practices aimed at abating GHG emissions (Table 1) were simulated over 100 yr with the APSIM model (Holzworth et al., 2014; www.apsim.info). The capacity of APSIM to represent baseline practices was assessed by modelling field experiments near the representative farms. Amounts of carbon sequestered and N₂O emitted were converted to carbon dioxide equivalents (CO₂e; IPCC, 2013) to evaluate the global warming potential (GWP) of different practices. We calculated the financial impacts of different practices before participating in emission reduction schemes through an analysis of the gross margins for each crop and management scenario on the farm. Gross margins were calculated at the paddock scale as income (yield x price) minus variable costs such as fertiliser. Economic data were sourced from the Department of Agriculture and Food Western Australia (DAFWA, 2012).

The case study farm was located at Dalwallinu (30.27°S, 116.66°E) in Western Australia. Average climatic conditions include annual (winter-dominant) rainfall of 310 mm yr⁻<fnref n="1" /> and minimum-maximum temperatures of 18-36°C (January) and 6-17°C (July). The farming system included several rotations of winter crops, but here only the results for one rotation (canola × wheat × wheat × barley) are presented. All crops were fertilised with ~65 kg N ha⁻<fnref n="1" /> crop⁻<fnref n="1" /> in a split application. The most common soil types on the 6,000 ha case study farm were sands and sandy duplexes, so here results are described for a single representative soil (a deep sand; 0.7% carbon in 0.0-0.1 m).

<table><caption>**Table 1.** Baseline practices (Scenario 1) and alternative practices (Scenarios 2-8) modelled for the case study farm</caption><thead><tr><td>Scenario number</td><td>Management practice description</td><td>Name</td></tr></thead><tbody><tr><td>1</td><td>Stubble burnt, 65 kg N ha<sup>-1</sup> fertiliser, bare summer fallow</td><td>Baseline</td></tr><tr><td>2</td><td>Stubble retained</td><td>NoBurn</td></tr><tr><td>3</td><td>Stubble burnt + 25% extra N fertiliser</td><td>Baseline+N</td></tr><tr><td>4</td><td>Stubble burnt - 25% less N fertiliser</td><td>Baseline-N</td></tr><tr><td>5</td><td>Stubble retained + 25% extra N fertiliser</td><td>NoBurn+N</td></tr><tr><td>6</td><td>Stubble retained - 25% less N fertiliser</td><td>NoBurn-N</td></tr><tr><td>7</td><td>Stubble retained + 5 t ha<sup>-1</sup> manure applied every 5 yr</td><td>NoBurn+Manure</td></tr><tr><td>8</td><td>Stubble retained + opportunistic summer cowpea green manure crop</td><td>NoBurn+Cowpea</td></tr></tbody></table>

## 3 Results and Discussion

The median crop yield (Fig. 1a-c) was greater than the baseline for Scenarios 3 and 5 (+25% extra N fertiliser). Scenario 7 (NoBurn+Manure) also increased the median yield or the range of yields in the upper quartile for some crops. Median crop yield was lower than the baseline in Scenarios 4 and 6 (-25% less N fertiliser). The revenue generated from greater yields obtained when extra N fertiliser was applied (Scenarios 3 and 5) offset the additional

<footnote n="1">¹ CSIRO Agriculture Flagship, EcoSciences Precinct, 41 Boggo Road, Dutton Park QLD 4102 Australia</footnote>

<footnote n="2">² School of Agricultural and Resource Economics, University of Western Australia, M089/35 Stirling Highway, Crawley WA 6009 Australia</footnote>

<footnote n="3">³ Liebe Group, 58 Johnston Street, Dalwallinu WA 6609 Australia</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Elizabeth.Meier@csiro.au</footnote>
costs associated with applying the fertiliser, and resulted in the greatest gross margins (Fig. 1d) of the scenarios. The savings in N fertiliser under Scenarios 4 and 6 did not outweigh the loss in revenue from the lower yields and resulted in the lowest gross margins for the scenarios. In Scenario 8, approximately 50% of cowpea crops reached harvest maturity due to low summer rainfall (data not shown) and so the nitrogen benefit from these legumes was limited. Therefore there was no yield benefit from this practice but there were increased costs from sowing the cowpea crop, which resulted in a decrease in the gross margin for this scenario compared to the baseline practice (Scenario 1).

<figure><img src="image_51.png" /><figcaption>**Fig. 1.** (a-c) Crop yield and (d) partial gross margins from scenario practices (described in Table 1). Box plot whiskers in (a-c) represent 1.5 times the interquartile range of the lower and upper quartiles. The horizontal lines are aligned with (a-c) median baseline yields and (d) the baseline gross margin to assist with comparison of scenarios.</figcaption></figure>

Annual changes in soil carbon ranged from gains (sequestration) of ~400 kg C ha⁻¹ yr⁻¹ to losses of ~150 kg C ha⁻¹ yr⁻¹ in different scenarios (data not shown). Annual emissions of N₂O-N typically ranged from 0.06 to 0.11 kg N₂O-N ha⁻¹ yr⁻¹ (data not shown), consistent with measured values for the region (Barton *et al.*, 2013). The cumulative CO₂e emissions (from combined changes in soil carbon and emitted N₂O; Fig. 2) indicated that all scenarios with stubble retained (Scenarios 2, 5-8) provided GHG abatement for the duration of the 100 yr simulation period compared to the baseline. Scenarios in which stubble was retained and additional organic matter inputs were applied from manure or summer crops (Scenarios 7 and 8) delivered the greatest GHG abatement.

<figure><img src="image_52.png" /><figcaption>**Fig. 2.** Cumulative 100 yr GWP of emissions from practices relative to Scenario 1 baseline (described in Table 1)</figcaption></figure>

## 4 Conclusions

Stubble retention at increased nitrogen fertiliser rates (Scenario 5) resulted in win-win outcomes for yield, GHG abatement and gross margins. Stubble retention alone (Scenario 2) or with added manure (Scenario 7) increased GHG abatement without reducing profitability. By comparison, reducing the amount of N fertiliser applied together with burning stubble (Scenario 4) resulted in lower yields and profitability while increasing GHG emissions. The greatest trade off between profitability and GHG abatement occurred when a cowpea crop was grown (Scenario 8), which provided some of the greatest abatement but lowered gross margins.

*Acknowledgements. Funding for this project was provided from the Australian Commonwealth Government and the Grains Research and Development Corporation and is gratefully acknowledged. Collaboration with the Liebe Group (www.liebegroup.org.au) is greatly appreciated.*

## References

- Barton, L., Murphy, D.V., Butterbach-Bahl, K., 2013. Influence of crop rotation and liming on greenhouse gas emissions from a semi-arid soil. Agr. Ecosyst. Environ. 167, 23-32.

- DAFWA, 2012: Gross margins by region, Perth, Department of Agriculture and Food Western Australia.

- Holzworth, D.P., et al., 2014. APSIM-Evolution towards a new generation of agricultural systems simulation, Environ. Modell Softw. 62, 327-350.
- IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.
# Yield gap analysis and crop insurance

Alba Castañeda-Vera ±1,2, Alberto Garrido 1 & M. Inés Míguez 1,2

## 1 Introduction

Yield gap analyses have been widely implemented over the last decades to evaluate the agronomic room for improvement of crop productivity (e.g. Affholder et al., 2012; Lobell et al., 2009; van Wart et al., 2013). It has been proved to be a valuable concept for assessing and understanding the possibilities to meet food demand for an increasing population (van Ittersum et al., 2013). Nevertheless, the use of this concept is not limited to the biophysical aspects of agronomy, but may be extrapolated to support research on socioeconomic aspects of agricultural production.

In this work, the use of yield gap analysis within a crop insurance context is explored: (i) by introducing the insurance parameter —maximum insurable yield— in a yield gap analysis and comparing it the actual observed yields; and (ii) by evaluating the possible use of crop model simulated yields to calibrate insurance parameters.

## 2 Materials and Methods

The analysis uses four yield levels: (i) actual yields, (ii) expected yields, (iii) insurable yields, and (iv) simulated water-limited-yields. Data on actual yields (Ya), those achieved by farmers, were available from ESYRCE database (Encuestasobre Superficies y Rendimientos de Cultivos-Ministry of Agriculture, Food, and Environment. MAGRAMA). Expected yields (Yexp), those predicted previous to sowing, were estimated for each county from the detrendedYa series, using Stata v12. Data from all municipalities in Castilla y León (CyL) region (northern central Spain) with at least one insured farmer in 2011 or 2012 was made available by ENESA (Spanish Agency of Agricultural Insurance). The zonal maximum insurable yield (YinsZ), for wheat grown under rainfed conditions for each municipality was obtained from the Spanish Official Gazette (BOE) No 217, 02.09.2013, at the Order AAA/1629/2013. Individual maximum insurable yields are adapted through a bonus-malus system based on the individual farmers' insurance history. Lastly, water limited yields (Yw) were simulated using the crop model CERES-Wheat (Godwin et al., 1989). CERES-Wheat was calibrated and validated using published data from the trials conducted by the Agricultural Research Program of CyL between 2004 and 2010 for wheat (Triticumaestivum, L.) CV_Marius'. The experimental sites are located across the grain production areas and are representative of the variability of its climate and soil. Crop development data such as emergence, anthesis and physiological maturity dates, and yield were available, as were sowing dates and plant density for all the sites. All yields are given per county.

Two yield gaps (GapZ, GapW) were defined by the following yield differences:

$$G = ins - ep \quad [Eq 1]$$

$$G = - ep \quad [Eq 2]$$

## 3 Discussion

The kernel density estimation of the zonal maximum insurable yield (YinsZ), the expected yield (Yexp), and the water limited yields (Yw) are presented in Fig. 1-A while the corresponding yield gaps, GapZ and GapW, are presented in Fig. 1-B. The lowest yields were YinsZ, followed by Yexp and lastly, Yw, with means of 1.96, 2.77 and 3.78 Mg ha⁻<fnref n="1" />, respectively. The largest variability was found for Yw, followed by Yexp and lastly YinsZ (Figure 5-A). GapZ had the mean of -0.85 Mg ha⁻<fnref n="1" /> and a low variability (percentiles 10 and 90, being -1.45 and -0.37 Mg ha⁻<fnref n="1" />, respectively), while GapW had a mean of 1.01 Mg ha⁻<fnref n="1" /> and a high variability (percentiles 10 and 90, -0.36 and 2.13 Mg ha⁻<fnref n="1" />, respectively).

Zonal maximum insurable yields (YinsZ) were 0.85 Mg ha⁻<fnref n="1" /> lower than the expected yields (GapZ) revealing that the parameter YinsZ is underestimated. This might explain why bonus-malus systems have been introduced to adapt individual maximum insurable yield based on the individual farmers' insurance history.

GapW was the highest yield gap found among the gaps included in this analysis (Fig. 1) and therefore, if used as expected yields for designing new insurance coverage, unbalances would be found as farmer's yields would be overestimated. In that case, yield gaps should be estimated and considered before implementing them.

To evaluate the potential use of simulated yields to calibrate insurance parameters, first the uncertainty associated to the use of crop models must be considered. In Castañeda-Vera et al. (2015) it is suggested that winter wheat yields simulated with the crop models Aquacrop (Raes et al., 2009; Steduto et al., 2012), CERES-Wheat, CropSyst (Stöckle et

<footnote n="1">¹ Research Centre for the Management of Agricultural and Environmental Risks (CEIGRAM), Technical University of Madrid (UPM), Spain</footnote>

<footnote n="2">² Agricultural Systems Group, Dpto. Producción Agraria, E.T.S. Ingenieros Agrónomos, Technical University of Madrid (UPM)</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: alba.castaneda@upm.es</footnote>
al., 2003) and WOFOST (Boogaard et al., 2011; Supit et al., 1994) are comparable when water is not limiting, but differences are larger when simulating under rainfed conditions and that could be due to the substantial differences they found in soil water simulations.

<figure><img src="image_53.png" /><figcaption>Fig. 1. Kernel density estimation of yields and yield gaps.</figcaption></figure>

The mean simulated value of Ywat 3.78 Mg ha⁻¹ (Fig. 1) diverges from that reported in Boogaard et al., (2013), within an analysis for the whole of Europe using the crop model WOFOST. Those authors calculated Yw between 5 and 6 Mg ha⁻¹, and GapW between 3 and 4 Mg ha⁻¹ in CyL region, so that Yw was more than 2 Mg ha⁻¹ greater and Yexp about 1 Mg ha⁻¹ smaller than reported here. These differences in the simulated Yw could be related to the accuracy in calibration and the scale of application. Deficiencies in the accuracy of the models to simulate soil water content or certain physiological processes subject the quality of models performance. Moreover, the orography and soil variability can hardly be included in the analysis as the high horizontal heterogeneity of the weather and soils or the differences in the cultivars used in different regions might result in significant errors when the scale of application is large. This is the case when calibrating models in a specific region to extrapolate results to a larger area and when available soil maps and weather data grids resolution is low.

4 Conclusions

The use of insurance historical data rather than using crop models is still preferred to evaluate and calibrate insurance parameters when available, otherwise farmer yields would be overestimated. Nevertheless, the use of crop models is useful to design new insurance packages when no historical data is available. In those cases, it is suggested that yield gaps be estimated and subtracted from simulated attainable yields.

*Acknowledgements.* This research was funded by the project MULCLIVAR, from the Spanish Ministry of Economy and Competitivity (MINECO) CGL2012-38923-C02-02, A. Castañeda-Vera has a PhD grant from UPM. We thank ITACyL, ENESA and the Spanish Ministry of Economy of Agriculture, Food, and Environment (MAGRAMA) for meeting our requests to use their data and the clarifying discussions.

References

Affholder, F., Poeydebat, C., Corbeels, M., Scopel, E., Tittonell, P., 2012. The yield gap of major food crops in family agriculture in the tropics: Assessment and analysis through field surveys and modelling. F. Crop. Res. 143, 106–118.

Boogaard, C.A., Van Diepen, R.P., Rötter, J.M.C.A., CabreraH.H., VanLaar, H.L., DeWit, H.L., TeRoller, J.A., 2011. WOFOST Control Centre 1.8 and WOFOST 7.1.3. Wageningen.

Boogaard, H., Wolf, J., Supit, I., Niemeyer, S., van Ittersum, M., 2013. A regional implementation of WOFOST for calculating yield gaps of autumn-sown wheat across the European Union. F. Crop. Res. 143, 130–142.

Castañeda-Vera, A., Leffelaar, P., Álvaro-Fuentes, J., Cantero-Martínez, C., Mínguez, I., 2015. Comparison of four winter wheat simulation models: modelling approaches and outputs. Eur. J. Agron. (under rev.

Godwin, D., Ritchie, J., Singh, U., Hunt, L., 1989. A User's Guide to CERES Wheat - V2.10. Alabama.

Lobell, D.B., Cassman, K.G., Field, C.B., 2009. Crop Yield Gaps: Their Importance, Magnitudes, and Causes. Annu. Rev. Environ. Resour.

Raes, D., Steduto, P., Hsiao, T.C., Fereres, E., 2009. AquaCropThe FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description. Agron. J. 101, 438–447.

Steduto, P., Hsiao, T.C., Fereres, E., Raes, D., 2012. Crop yield response to water (No. 66), FAO Irrigation and drainage. Rome. Stöckle, C.O., Donatelli, M., Nelson, R., 2003. CropSyst, a cropping systems simulation model. Eur. J. Agron. 18, 289–307.

Supit, I., Hooijer, A.A., van Diepen, C.A., 1994. System description of the WOFOST 6.0 crop simulation model implemented in CGMS. Volume 1: Theory and Algorithms (No. EUR 15956 EN), An Agricultural information system for the European community. Luxemburg.

Van Ittersum, M.K., Cassman, K.G., Grassini, P., Wolf, J., Tittonell, P., Hochman, Z., 2013. Yield gap analysis with local to global relevance—A review. F. Crop. Res. 143, 4–17.

Van Wart, J., Kersebaum, K.C., Peng, S., Milner, M., Cassman, K.G., 2013. Estimating crop yield potential at regional to national scales. F. Crop. Res. 143, 34–43.
# Modeling attainable yield and yield losses due to pests and diseases to compare performances of coffee farming systems

Rolando Cerda ±1,2, Jacques Avelino<fnref n="2" />,<fnref n="3" />,<fnref n="4" />, Esther Lechevallier<fnref n="1" />,<fnref n="5" />, Louise Krolczyk<fnref n="3" />,<fnref n="6" />, Charlie Mathiot<fnref n="2" />,<fnref n="6" />, Eugénie Clément<fnref n="3" />,<fnref n="7" />, Christian Gary<fnref n="8" />, Philippe Tixier <fnref n="2" />,<fnref n="9" />& Clémentine Allinne<fnref n="1" />,<fnref n="2" />

## 1 Introduction

The regulation service of pests and diseases (P&D) can be assessed in terms of avoided crop losses (Avelino et al., 2011). This approach is necessary to compare the performance of agroecosystems with different sets of P&D (injury profiles). For perennial crops, such as coffee, the assessment of yield losses is difficult due to the biennial production cycle, the complexity of agroecosystems where these crops are grown, and the existence of primary losses (in the current year) and secondary losses (losses in the following years due to the physiological damages caused by P&D) (Zadoks & Schein, 1979). Through this work, we contribute to: i) a better understanding of the impact of P&D on primary and secondary coffee yield losses; and ii) build a conceptual model to identify the main factors that determine coffee yield losses.

## 2 Materials and Methods

**Trial:** coffee at full sun exposure (5000 plants ha⁻<fnref n="1" />) where six treatments with different sequences of fungicide applications are compared, with a duration of three years (Fig 1). In each of four replicates (40 m<fnref n="2" />), six coffee plants were marked for measurements: fruiting nodes, fruits per node, dead branches, P&D incidences and severity, and yields.

**Studied variables:** standardized area under the disease progress curve (sAUDPC) and severity of P&D, dead branches, and yield losses. Depending on the treatment, at the end of each year we can obtain attainable yields (Yatt) and actual yields (Yac), whose differences represent primary or secondary losses (Fig 1). *Yield loss (%) = ((Yatt - Yac)/Yatt) * 100*

**Statistical analysis:** i) sAUDPC, severity, dead branches and yields were compared among treatments trough analyses of variance, with fruit load of the previous year and soil acidity as covariates (they characterize the production conditions of each plot), and LSD (Fisher) with p<0.05; ii) correlations (Spearman) between measured and studied variables of 2013 and 2014 to support the relationships presented in the conceptual model.

## 3 Results – Discussion

**Diseases and yield losses.** We observed significant effects of the treatments on several diseases in both years (Table 1). In the first year (2013), no difference of yield between treatments was found and therefore no yield loss was calculated. In the second year (2014), significant differences between treatments were observed for sAUDPC of coffee leaf rust and dead branches; and the yield of the treatments conducting to attainable yield (T1&T5) was different to others, making possible to calculate yield losses. The negative impacts of abandon fungicide application from one year to another (T4) can be worse than no fungicide application in two consecutive years (T2&T6), reflected by higher dead branches and similar yields. Both primary and secondary losses were high, showing a severe impact of diseases (Table 1). Although we applied fungicides, there was presence of diseases, which indicates that yield losses could be even higher.

**Correlations and conceptual model.** Several significant correlations were found between variables from one year to another and within the same year. The fruit load in 2013 influenced negatively the fruit load in 2014; higher yield components (fruits per node, fruiting nodes, fruit load) caused higher sAUDPC and severities of diseases in most cases; dead branches had positive correlations specially with severity of diseases, and dead branches of 2013 influenced negatively the yield components in 2014 (Fig. 2). These findings indicate that physiological aspects and impacts of diseases lead to yield losses in a given year and in next years. Based on that, we present a conceptual model, showing how different factors can influence the components of attainable yield, and how this last one can be reduced to actual yield, due to primary and secondary yield losses of coffee (Fig. 3).

<footnote n="1">¹ CIRAD, UMR System, SupAgro Montpellier bat 27, 2 place Viala, 34060 Montpellier, France</footnote>

<footnote n="2">² CATIE, Program of Sustainable Agriculture and Agroforestry, 7170 Turrialba, Costa Rica</footnote>

<footnote n="3">³ CIRAD, UR Bioagresseurs, TA A-106 - Avenue Agropolis, 34398, Montpellier, France</footnote>

<footnote n="4">⁴ IICA-PROMECAFE, AP 55, 2200 Coronado, San José, Costa Rica</footnote>

<footnote n="5">5 ENSAT-INP, F-31326 Castanet Tolosan, France</footnote>

<footnote n="6">6 AgroParisTech, F-75231 Paris, France</footnote>

<footnote n="7">⁷ INSA Toulouse 135, Avenue de Rangueil - 31077 Toulouse- France</footnote>

<footnote n="8">⁸ INRA, UMR System, SupAgro Montpellier bat 27, 2 place Viala, 34060 Montpellier, France</footnote>

<footnote n="9">9 CIRAD, UPR GECO, TA B-26 / PS4 - Boulevard de la Lironde - 34398 Montpellier, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: rcerda@catie.ac.cr</footnote>
<figure><img src="image_54.png" /></figure>

Yatt: attainable yield; YL: yield losses; PY: primitive yield

<figure><img src="image_55.png" /><figcaption>**Fig. 2.** Matrix of correlations (Spearman) among variables that determine yields.</figcaption></figure>

<figure><img src="image_56.png" /><figcaption>**Fig. 3.** Conceptual model to assess primary and secondary yield losses of coffee.</figcaption></figure>

## 4 Conclusions

The negative impacts of diseases in coffee are not limited only to the year where they have developed. The high estimated secondary losses indicate that economic and technical measures to help coffee farmers to face phytosanitary issues (as coffee rust epidemic in 2012-13, Avelino *et al.*, 2015) need to be continued on several years.

The proposed conceptual model shows the main factors that should be taken into account to assess primary and secondary yield losses of coffee. Based on this model, statistical models could be developed (to be finalized in 2015) to estimate attainable yields and yield losses, in order to assess the performance of different coffee farming systems.

*Acknowledgments.* We thank the INRA Smatch Metaprogramme that funds this experiment through the DAMAGE action.

## References

Avelino, J., ten Hoopen, M., &DeClerck, F. (2011). Ecological mechanisms for pest and disease control in coffee and cacao agroecosystems of the Neotropics. In: DeClerck, F., Le Coq, J.F., Beer, J. (Eds.), Ecosystem services from agriculture and agroforestry measurement and payment. Earthscan, 91-117. London.

Avelino J, Cristancho M, Georgiou S, Imbach P, Aguilar L, Bornemann G, *et al.* (2015). The coffee rust crises in Colombia and Central America (2008-2013): impacts, plausible causes and proposed solutions. Food Security. 2015;DOI 10.1007/s12571-015-0446-9.

Zadoks, J.C. & Schein, R.D. (1979). Epidemiology and plant disease management. New York

<table><caption>**Table 1.** Effects of treatments on P&D and yield losses.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="3">Year 2013</th><th colspan="4">Year 2014</th></tr><tr><th>T1&T4&T5</th><th>T2&T3&T6</th><th>T1&T5</th><th>T2&T6</th><th>T3</th><th>T4</th></tr></thead><tbody><tr><td>sAUDPC_R</td><td>24 a</td><td>34 b</td><td>34 a</td><td>41 b</td><td>40 ab</td><td>31 a</td></tr><tr><td>sAUDPC_C</td><td>24 a</td><td>29 b</td><td>22 a</td><td>24 a</td><td>24 a</td><td>27 a</td></tr><tr><td>sAUDPC_A</td><td>6 a</td><td>5 a</td><td>8 a</td><td>4 b</td><td>6 ab</td><td>8 a</td></tr><tr><td>Max_Sev</td><td>3.1 a</td><td>3.2 a</td><td>3.5 a</td><td>3.4 a</td><td>3.2 a</td><td>3.6 a</td></tr><tr><td>DeadB</td><td>37 a</td><td>54 b</td><td>28 ab</td><td>29 ab</td><td>13 a</td><td>44 b</td></tr><tr><td>Yield</td><td>2077 a</td><td>2267 a</td><td>3397 a</td><td>1823 b</td><td>2125 b</td><td>1931 b</td></tr></tbody><tfoot><tr><td colspan="7">Estimated Primary Losses = ((3397-1931)/3397)*100 = 43%</td></tr><tr><td colspan="7">Estimated Secondary Losses = ((3397-2125)/3397)*100 = 37%</td></tr><tr><td colspan="7">Estimated Primary and Secondary Losses = ((3397-1823)/3397)*100 = 46%</td></tr></tfoot></table>

*See interpretation of Fig. 2. for the meaning of variables and their units*

**Interpretation.** Below the diagonal there are the Spearman coefficients; above the diagonal: the darker and bigger the circles, the stronger the correlation (symmetric matrix). Blue color indicates positive correlation; orange color indicates negative correlation. Cells with white color indicate that there is no significant correlation.

NFNPlant: Number of fruiting nodes per plant
NFNode: Number of fruits per node

FL: Fruit load (=NFNPlant x NFNode) PDeadB: Percentage of dead branches

DeadB: Number of dead branches

Yield: Coffee yield (grams of coffee cherries per plant)
Max_Sev: Maximum of severity of diseases in leaves (scale 0-6)

sAUDPC_R: standardized Area Under the Disease Progress Curve of coffee leaf rust (Hemileiavastarix) (% day⁻¹)

sAUDPC_C: of cercospora (Cercosporacoffeicola) (% day⁻¹)
sAUDPC_A: of anthracnoses (Colletotrichumspp.) (% day⁻¹)

Numbers 13 and 14 represents the variables in 2013 and 2014, respectively

Note: we constructed the accumulated AreasUnder the Disease Progress Curve, but, since there were differences in the total time of incidence measurements between 2013 and 2014, we standardized them per day.
T4. What's new with bioeconomic models for the analysis and design of agricultural systems?

Chair: Francois Affholder, CIRAD

Co-chair: Hatem Belhouchette, IAM.M
# DAHBSIM, a Dynamic Agricultural Household Bio-Economic Model

Guillermo Flichman<fnref n="1" />, Hatem Belhouchette<fnref n="1" />, Maria Blanco<fnref n="2" />, Roza Chenoune<fnref n="1" />, Sophie Drogué<fnref n="3" />, Adam Komarek<fnref n="4" />, James Hawkins<fnref n="4" />, Wajid Nasim<fnref n="1" /> & Siwa Msangi<fnref n="4" />

## 1 Introduction

The development of bio-economic models responds to the need of tools able to represent in a consistent way different dimensions of Agricultural Systems, integrating different scale levels as well as impacts concerning agro-ecologic issues, socio-economic ones and indirect effects of other nature, such as global environmental impacts.

Most bio-economic models use outputs of bio-physical models (Flichman, 2011) to generate part of the parameters of the model. A small number of models including the bio-physical processes in the same code, usually apply empirical bio-physical relations, adapted only to the specific case that is studied. When bio-physical models are used to generate data to be introduced in the bio-economic model, it is not possible to fully simulate the dynamic interactions between the economic and the bio-physical dimensions of the whole model. In the majority of cases, the bio-economic models of this type are static, creating to some extent a lack of consistency.

On the other side, when only a simplified empirical approach is representing the bio-physical issues, the model cannot be easily adapted to a different context (Holden *et al.* 2004).

The development of DAHBSIM uses an accumulated experience in this type of models, and intends to introduce some specific improvements in relation with previous work in this field, essentially through its dynamic character and the possibility of application in different contexts, as it is developed in a generic way using a modular structure.

DAHBSIM is designed to capture some key features of developing countries rural areas. Some aspects of this work present a particular interest from a methodological perspective:

The fact that DAHBSIM is built in a modular and generic manner allows applying it in different contexts. The potential user will be able to use all or part of the modules. The architecture of the model may include new modules if it appears necessary to do so. The development of this model is done in the context of collaboration between IFPRI and CIHEAM-IAMM.

## 2 Materials and Methods

DAHBSIM is a model applying a Dynamic-recursive optimization approach: an inter-temporal optimization is performed, the results of the first period are observed and recursive equations (Summary biophysical model) are introduced before the second optimization, for taking into account the effect on resources of the production choices of the previous year. This procedure is repeated for all periods. There is a moving horizon then, the model takes into account future but reinitializes at each iteration the initial conditions, changed as a function of the previous choice. Inside each step, the inter-temporal optimization does not take into account the changes in the state variables (yield and externalities per activity) defined by the previous use of the soil. These changes are introduced in the following step, when state variables (water and nitrogen content of the soil) are reinitialized. Only the first year results of each iteration are considered at the end.

The model simulates farm household level decisions; market prices are defined exogenously.

Model features are coded in a generic and modular way to ease application to different farming systems and household types. The core modules comprise the common features of the model: (1) objective function; (2) cropping module; (3) biophysical module; (4) farm module and (5) household module.

Apart from these core modules, all other modules can be activated or deactivated depending on the application at hand. Presently, a livestock module is also available. Modules for perennial crops will also be developed.

Thanks to its modularity, DAHBSIM can be easily extended and adapted to answer new policy questions.

The main outputs generated from DAHBSIM are land use, production activity levels, input use, farm income, time allocation decisions, household food and non-food consumption, and environmental externalities (as joint products). These outputs are translated into indicators to measure the impact of policies.

DAHBSIM can be applied to individual households (either real households or household types) or spatial regions. Results are provided at the household level or aggregated to higher spatial scales (department, region...).

<footnote n="1">¹ CIHEAM-Institut Agronomique Méditerranéen de Montpellier,</footnote>

<footnote n="2">² ETSA, Universidad Politécnica de Madrid</footnote>

<footnote n="3">³ INRA, UMR MOISA,</footnote>

<footnote n="4">⁴ CGIAR, IFPRI This model is being developed as part of the IFPRI BioSight project, coordinated by Siwa Msangi, in collaboration with CIHEAM-IAMM.</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: flichman@iamm.fr</footnote>
The Dahbsim model has a modular structure.

* The biophysical module computes in its first part the water stress coefficient in year 1. And in its 2nd part it computes the water stress coefficient for the next years. A similar procedure is used concerning nitrogen.

* The crop module contains the equations describing the cropland allocation, the labor use, the rotation constraints, etc.

* The farm module contains the equations defining the resources constraints.

* The household module contains the equations defining household demand and time allocation

* A livestock module computes the feed requirements of different type of animals and has consistent feed-backs with the rest of the model, taking into account balances of feed consumption as well as manure for crop fertilization.

## 3 Results

Actually, there are not still results of any application; the results are the current development of the model. The following scheme shows the principal flows of the model, only considering cropping activities.

<figure><img src="image_57.png" /><figcaption>Simplified DAHBSIM scheme for cropping activities</figcaption></figure>

## References

* Flichman G., editor (2011). Bio-economic models applied to Agricultural Systems. Springer.

* Holden S., Bekele S., Pender J. (2004). Policy Analysis for Sustainable Land Management and Food Security in Ethiopia. A Bioeconomic Model with Market Imperfections. Research Report 140. IFPRI.
# Is « bio-economic » farm modelling of any help for farming system design?

François Affholder*±<fnref n="1" />, Damien Jourdain<fnref n="2" />, Marc Corbeels<fnref n="1" />, Véronique Alary<fnref n="2" />, Krishna Naudin<fnref n="1" />, Philippe Bonnal<fnref n="2" />, Eric Scopel<fnref n="1" />, Françoise Gerard<fnref n="2" />, Philippe Quirion<fnref n="3" /> & Hatem Belhouchette<fnref n="4" />

## 1 Introduction

Many innovations at cropping system level impact the whole farm system by changing the flow of farm resources across farm activities. Using farm models has increasingly been proposed for assessing the feasibility of prototypes of cropping systems at farm level, and evaluating their impacts on household food production, farm income, and the environment. Such models are often called bio-economic model as a way to stress the mixing of knowledge about the biophysical and economic aspects of farming (Janssen & van Ittersum, 2007; Thornton & Herrero, 2001). *A priori*, one should not expect accurate predictions from these coupled bio-economic models, because of their complexity and the difficulty of measuring certain key input variables, such as labour availability for the different farm and off-farm activities, family income, and intra-farm consumption of agricultural products. This type of models can rather be used to explore ‘what if’ scenarios and to understand their outcomes including their inevitable uncertainty. In this paper, we review our experience with bio-economic farm models as virtual test benches for evaluating “cropping systems ideotypes” (CSI), i.e. idealized cropping systems proposed as alternatives to existing cropping systems, for increasing farm income and production or for reducing negative impacts of farming on the environment. Six published case studies are used (list of papers available at http://agents.cirad.fr/index.php/Krishna+NAUDIN/bio_economicFarmModelling). The objective is to examine to what extent the models developed fulfilled their purpose of evaluating prototypes of cropping systems for farming systems design.

## 2. Materials and Methods

In 3 out of 6 case studies (‘Madagascar’, ‘Brazil-CA’, and ‘Vietnam’) the CSI were conservation agriculture options designed for low income family farms. In two other case studies, ‘Senegal’ and ‘Brazil-Conv’, the CSI were conventionally intensive cropping systems as an alternative to low yielding current practices. In the 6th case, ‘France’, CSI were options designed to reduce ground water pollution by lixiviated N. In all the case studies, we used farm models based on the ‘Optimization Under Multiple Constraints (OUMC)’ approach, in which an optimization procedure is used to find the set of crop, livestock and off-farm activities that best fits the objective of getting the highest farm income subject to constraints relative to seasonality of activities, the necessity to satisfy the family’s basic needs (in food, clothes, health and education) throughout the time period covered by the simulation, as well as constraints relative to the farm’s resources in land, labour force, equipments, and cash money. In each case study, a regional assessment of the diversity of farms was first established based on relatively large surveys. Table 1 shows the differences between cases regarding farm structure.

<table><caption>Table 1. Main characteristics of farms modelled in the case studies</caption><thead><tr><td>Case studies</td><td>Predominant Farming system</td><td>Farm size (ha)</td><td>Number of family members</td><td>Number of workers</td><td>Total household income (€/capita/year)</td><td>Off-farm income (€/capita/year)</td></tr></thead><tbody><tr><td>Brazil-Conv</td><td>Mixed crop livestock in transition</td><td>5-120</td><td>2-10</td><td>1.5-3.75</td><td>40-8000</td><td>0</td></tr><tr><td>Brazil-CA</td><td>toward intensive dairy farms</td><td>16-51</td><td>2-4</td><td>2-3.8</td><td>800-6000</td><td>0-560</td></tr><tr><td>Vietnam</td><td>Mixed crop livestock systems in</td><td>0.7-4.7</td><td>5-12</td><td>2-7</td><td>20-400</td><td>0-200</td></tr><tr><td>Madagascar</td><td>transition from subsistence to</td><td>2.5-3.5</td><td>5</td><td>6</td><td>5-800</td><td>0</td></tr><tr><td>Senegal</td><td>market oriented farms</td><td>3.5-16</td><td>12-25</td><td>4-10</td><td>100-300</td><td>0</td></tr><tr><td>France</td><td>Mixed arable farms</td><td>100-120</td><td>3-5</td><td>0.25-1.1</td><td>25000</td><td>0</td></tr></tbody></table>

In 4 out the 6 studies, two typical farms were then selected within each of the farm category identified, and they were modelled thanks to a more detailed questionnaire applied to each individual farm. In the two other studies (‘Senegal’ and ‘France’), each farm category was modelled using data of the survey averaged over the category. In all the case studies, model calibration was carried out to ensure that the model reproduced well the observed farm plans when using ‘baseline simulations’ in which the studied CSI were not incorporated into the list of options available to the simulated farm. Then, various scenarios were simulated in which CSI were included into the list of options, and changes in the economic environment of the farms were explored, such as changes in the prices of input or output, in the availability of

<footnote n="1">¹ Performance des Systèmes de Production et de Transformation Tropicaux, CIRAD</footnote>

<footnote n="2">² Environnement et Société, CIRAD</footnote>

<footnote n="3">³ CIRED</footnote>

<footnote n="4">⁴IAMM</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Affholder@cirad.fr</footnote>
credit or other financial tools, or introduction of subsidies of several kinds. Differences between studies in term of methodology were limited to the extent to which biophysical models were used to produce data relative to cropping systems and to minor differences in the optimization algorithm used. However, the level of complexity of the model varied greatly among cases, depending on the need to account for crop-livestock relationships, for the dynamics of farm performances over time, or for risks related to yield or price variability.

## 3 Results – Discussion

The main results are summarized in table 2. For several case studies, we found that even relatively uncertain model parameters or a highly simplified model structure allowed to draw robust conclusions on the feasibility of the locally studied CSI. The robustness of the conclusion was less dependent on the case study than on farm type within case studies. Typically, simulated farm plans were particularly robust for farms with strong labour constraints. In such case, the model prediction about rejection or adoption of CSI is not to be taken as an anticipation of what will occur in the real world. Rather, it provides insight on the economic relevance of the CS at farm level. When the model predicts rejection for farms with very low income, however, it is very likely that the technique will not be adopted by real farmers, who are not expected to make decisions putting at risk the daily subsistence of their family. When model outputs were less robust (i.e more sensitive to uncertainties on key inputs), they were still useful for qualitatively identifying the main factors at farm and field level determining the economic relevance of adopting the studied CSI. They also helped in identifying knowledge gaps that should be addressed for improving the reliability of quantitative assessment of the feasibility at farm level of studied CSI. Often, these gaps relate to the availability of data that quantify the agronomic performances of CSIs in the biophysical and socio-economic environment of the case study.

<table><caption>Table 2. Summary of results per case study.</caption><thead><tr><th></th><th>CSI tested</th><th>Model complexity (*)</th><th>Data on CSI performances</th><th>Typical Result in terms of FSD</th><th>Typical gaps in knowledge identified</th></tr></thead><tbody><tr><td>Brazil-Conv</td><td>More intensive maize systems (cultivar, fertilizer, mechanization)</td><td>2/4 :S-R-LB</td><td>Ad hoc crop model calibrated and validated on site</td><td>Identification of soil constraints making dairy specialization too risky for certain farms under current economic environment</td><td></td></tr><tr><td>Brazil-CA</td><td>Conservation agriculture</td><td>4/4 :D-R-LC</td><td>On-farm and on-site trials</td><td>CA ideotypes refined per farm types</td><td>Agronomic / environmental performances of CA</td></tr><tr><td>Vietnam</td><td>Conservation Agriculture</td><td>1/4 : S-NR-LB</td><td>On-farm and on-site trials</td><td>CA not appropriate for most farm types, subject to further studies for others</td><td>Long term Agronomic / environmental performances of CA</td></tr><tr><td>Madagascar</td><td>Conservation agriculture</td><td>3/4 : D-NR-LC</td><td>On farm surveys and trials</td><td>CA systems with fodder crop beneficial for dairy cow farmers. Fraction of biomass used for soil protection against erosion likely to decrease when price of milk increases.</td><td>Agronomic / environmental performances of CA</td></tr><tr><td>Senegal</td><td>More intensive cereal cropping systems (fertilization)</td><td>2/4 : S-R-LB</td><td>Ad hoc crop model calibrated and validated on site</td><td>Drought insurance may entail crop intensification but subsidies to insurance are less effective than subsidies to credit or than direct cash transfers to farmers for increasing the simulated farm income</td><td>Agronomic / environmental performances of crops under highly variable rainfall. Nutrient fluxes between livestock and crops.</td></tr><tr><td>France</td><td>Lower N lixiviation systems</td><td>1/4 : S-R-L0</td><td>Ad hoc crop model calibrated and validated on site</td><td>The cross-compliance restriction associated to nitrate directive needs to be high to incite farmers to adopt CSI</td><td>Suitability of bio-economic modelling for co-assessment and co-design of cropping systems.</td></tr></tbody></table>

## 4 Conclusions

Bioeconomic farm models should not be seen as a way to predict adoption or rejection of innovative cropping systems by real farms, but rather as a way to better identify (i) gaps in knowledge about the agro-environmental performances of such innovative cropping systems that are critical for comparing them with current practice from the point of view of a farm, and (ii) the key factors determining the feasibility of such systems at that scale. In that sense, bioeconomic farm models are a very effective way to assemble available knowledge at field and farm scale for identifying the conditions at which strong changes in cropping systems may take place.

## References

* Janssen, S., van Ittersum, M.K. (2007). Assessing farm innovations and responses to policies: A review of bio-economic farm models. Agricultural Systems, **94**, 622-636.

* Thornton, P.K., Herrero, M. (2001). Integrated crop-livestock simulation models for scenario analysis and impact assessment, Agricultural Systems **70**, 581-602.

<footnote>(*) Model complexity is described using a 1 to 4 scale and a string chain accounting for Dynamic (D, 1) or Static (S, 0) approaches, plus the integration of Risk (R, 1) or not (NR, 0), plus the level of details of the relation between livestock and cropping systems (LC: detailed, 2; LB: basic, 1, L0: none, 0)</footnote>
# Trade-offs in soil fertility management on arable farms

Jules Bos<fnref n="1" />, Hein ten Berge<fnref n="1" />, Jan Verhagen<fnref n="1" /> & Martin van Ittersum*±<fnref n="2" />

## 1 Introduction

Crop production and soil fertility management implies a multitude of decisions and activities on crop choice, rotation design and nutrient supply. Within each of these management categories, many options are usually available to farmers. In practice, the choices to be made and the resulting outcomes are subject to a wide range of objectives and constraints. Objectives are economic as well as environmental, for instance sequestering carbon in agricultural soils or reducing nitrogen losses. Constraints originate from biophysical and institutional conditions that may restrict the possibilities for choosing crops or the use of specific cultivation and fertilization practices. Finding ways to maintain farm profitability while increasing carbon sequestration or reducing undesirable emissions is complicated by many interactions and feedbacks among agricultural practices (e.g. Powlson *et al.*, 2011; Alluvione *et al.*, 2010).

## 2 Materials and Methods

To explore farming options related to nutrient and soil fertility management, we developed the mixed integer linear programming model NutMatch. The novelty of the model is the coherent description of mutual interdependencies amongst a broad range of sustainability indicators related to soil fertility management in arable cropping, enabling the quantification of synergies and trade-offs between objectives. In NutMatch, farming systems are viewed as being composed of cropping and fertiliser activities. Cropping activities refer to the growing of crops at predefined nutrient (NPK) requirements. These requirements have to be covered by nutrients applied in fertiliser activities. Each activity is characterised by design criteria and quantified via so-called input-output coefficients derived from the literature and secondary data. Input-output coefficients are strongly linked to the goals pursued with nutrient management in crop rotations (e.g. food production, income generation, maintenance of soil fertility, minimising environmental impact) and describe inputs (e.g. organic fertilisers) and desired and undesired outputs (e.g. crop yields, nitrous oxide emission). Each activity not only contributes to any of the objective variables, but also claims limited ‘resources’. In NutMatch, the sum of activities’ claims is subject to a series of constraints, dictated by a priori set values. For example, phosphorus surplus at rotational level should be between -5 and +5 kg P₂O₅ kg per ha.

We applied NutMatch to two crop rotations, differing in crop areas and cultivation of green manure crops. ROT1 is a ‘standard’ 1:4 rotation with four crops: winter wheat, ware potato, silage maize and sugar beet. In ROT2+, the winter wheat area is doubled at the expense of silage maize, with fertilised green manure crops after both winter wheat crops. Winter wheat straw is left in the field in both rotations. In model optimizations, rotations can be combined with four fertiliser strategies differing in the use of organic fertilisers. In one strategy, nutrient supply to crops is based on mineral fertilisers only, in three others mineral fertilisers are combined with either cattle slurry, pig slurry or compost. The two rotations and four fertiliser strategies each contribute differently to desirable and undesirable objective variables, due to differences in financial returns, emissions and organic matter inputs into the soil via crop residues and organic fertilisers. In the case study presented here, for each combination of two rotation types and four fertiliser strategies, trade-off curves were calculated of newly built soil-C via additions of organic fertilisers and crop residues on the one hand (kg C ha⁻<fnref n="1" /> yr⁻<fnref n="1" />), and nitrogen losses (NO₃-N, N₂O-N; kg ha⁻<fnref n="1" /> yr⁻<fnref n="1" />) on the other. End points of each trade-off curve are defined by the maximum values of newly built soil-C and minimum values for nitrogen losses. Intermediate points are calculated by maximising newly built soil-C while gradually tightening the restriction on either NO₃-N leaching or N₂O-N emission.

## 3 Results – Discussion

Trade-off curves of newly built soil-C and nitrogen losses are shown in Fig. 1. Maximum newly built soil-C ranges from 325 to 880 kg C per ha per year. Compared to using mineral fertilisers only, the use of cattle slurry and especially compost results in large increases of newly built soil-C, while pig slurry hardly does so. Considerable differences in the contribution of organic fertilisers to soil C built-up are explained by differences in their organic matter contents. Per kg P applied in organic fertilisers, the supply of organic matter is relatively large when using cattle slurry or compost and low when using pig slurry. Compared to using mineral fertilisers only, the use of cattle slurry and compost in both rotations adds ca. 240 and 390 kg newly built soil-C per ha per year, while pig slurry adds only 26 kg C per ha per year.

<footnote n="1">¹ Agrosystems research, Wageningen University & Research centre, P.O. Box 16, 6700 AA Wageningen</footnote>

<footnote n="2">² Plant Production Systems, Wageningen University & Research centre, P.O. Box 430, 6700 AK Wageningen</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Martin.vanIttersum@wur.nl</footnote>
In ROT2+, maize is substituted by winter wheat, and includes green manure crops after both winter wheat crops. In all fertiliser scenarios, these changes added ca. 160 kg C per ha per year (Fig. 1), but reduced financial return by ca. 340 euro per ha. The extra built-up of soil-C was mainly due to the increased winter wheat area (with straw left in the field), while the green manure crops only had a modest effect (data not shown).

Near-maximum values for newly built soil-C were attained when organic fertilisers were used at maximum allowable levels (as defined by maximum allowable phosphorus surplus at rotational level), and were hence associated with higher nitrogen loss. Relationships between newly built soil-C and nitrous oxide emission were fairly linear before plateauing, suggesting that there is a strong relationship between the build-up of soil-C and these losses. Nitrogen leaching loss, however, could be considerably reduced without dramatically affecting soil-C built up. When maximizing newly built soil-C while tightening the restriction on leaching loss, NutMatch maintains the supply of organic fertilisers as high as possible, while reducing mineral N supply to crops. This strategy initially only affected the return of carbon to the soil through crop residues, but left the more significant supply of carbon via organic fertilisers unaffected.

<figure><img src="image_58.png" /><figcaption>**Fig. 1.** Trade-off curves of newly-built soil-C and nitrate concentration in ground water (left) and nitrous oxide emission (right) in ROT1 and ROT2+ under four different fertiliser scenarios. End points of each curve correspond with maximum newly built soil-C and minimum N loss, respectively.</figcaption></figure>

The aggregated effect of newly built soil-C and nitrous oxide emissions in terms of greenhouse gas (GHG) emissions in different rotations and fertilizer scenarios can be assessed by expressing both in CO2-equivalents (1 kg newly built-up soil-C = 3.7 kg CO2-eqs.; 1 kg N2O-N = 487 kg CO2-eqs.). For example, when maximizing newly built soil-C in ROT1 (i.e. right end points in right graph of Fig. 1), it can be calculated that additional soil-C built-up resulting from the use of cattle and pig slurry did not compensate for additional emissions of nitrous oxide, so that the net GHG emission effect of using these organic fertilisers is negative. Only the use of compost resulted in a net GHG emission benefit in the sense that additional soil-C built-up outweighed additional nitrous oxide emission. The net GHG emission effect of the introduction of (fertilised) green manures and increased winter wheat area in ROT2+ was positive. This positive effect was exclusively due to the increased winter wheat area.

## 4 Conclusions

Trade-off curves generated with NutMatch show that carbon sequestration conflicts with reducing nitrogen losses. From a greenhouse gas emission perspective (CO2-equivalents), soil-C gained via the use of animal slurries is entirely offset by increased N2O emissions. Using compost or adapting crop choice to increase crop residue return to soils resulted in a positive GHG emission effect, but was associated with lower farm profitability. Due to the many data used from a large variety of sources, our trade-off curves are uncertain. However, these trade-offs are very relevant and urgently require further study. Our analysis is partial, as we did not consider GHG emissions from mineral or organic fertilizer production. Our model-based explorations provide insight in options for soil carbon sequestration and their limitations vis-a-vis other objectives.

## References

* Alluvione, F., Bertora, C., Zavattaro, L., Grignani, C. (2010). Nitrous oxide and carbon dioxide emissions following green manure and compost fertilization in corn. Soil Science Society of America Journal **74**, 384-395.

* Powlson, D.S., Gregory, P.J., Whalley, W.R., Quinton, J.N., Hopkins, D.W., Whitmore, A.P., Hirsch, P.R. & Goulding, K.W.T. (2011). Soil management in relation to sustainable agriculture and ecosystem services. Food Policy **36**, S72-S87.

*This publication has been partly funded under the EU CATCH-C project (Grant Agreement N° 289782) within the 7th Framework Programme for RTD.*
# A SPATIALLY-EXPLICIT MULTI-SCALE BIOECONOMIC MODEL TO DESIGN AND ASSESS AGRICULTURAL LANDSCAPES

Pierre Chopin *±1, Jean-Marc Blazy <fnref n="1" />, Loïc Guindé <fnref n="1" />& Thierry Doré <fnref n="2" />,<fnref n="3" />

## 1 Introduction

Agricultural landscapes drive the provision of several ecosystem services. These landscapes are the partial results of farmer cropping system choices at the field and farm levels. Cropping system choice is driven by a range of parameters that act at the field, farm and regional scales such as biophysical parameters, farm structure and regional quotas. Bioeconomic models can assess *ex ante* the impacts of policies on landscapes resulting from the modification of farmer choices. They have scarcely been used for assessing new spatially-explicit agricultural landscapes with the integration of the field, farm and regional scales (Delmotte *et al.*, 2013). To assess the effects of policies on the landscapes change and the regional sustainable development, we built a regional, spatially explicit, multi-scale bioeconomic model of farmer cropping system choices. The model is tested in Guadeloupe, a French archipelago in the Caribbean islands.

## 2 Materials and Methods

The Multi-scale model of the crOpping Systems Arrangement and Its Contribution to sustAinable development MOSAICA produces new agricultural landscapes at the regional scale by optimising the allocation of cropping systems regionally at the field scale. It accounts for the constraints and opportunities at the field level (e.g. soil types), the farm level (e.g. the availability of production factors), and the regional level (e.g. the policies implemented). The inputs of MOSAICA are i) the geographic database of fields, ii) the database of activities that describe the cropping systems that can be allocated to fields and iii) a farm typology.

The geographic database is composed of fields represented by polygons with relevant information for the cropping system allocation defined with geographical information systems, statistical data or farm surveys. The activities are cropping systems defined with technical coefficients and externalities derived from previously published papers, relevant documentation or expertise from local advisers. The farm typology is built with a classification tree that can help observe farming system changes across scenarios. For each farm type a risk aversion coefficient is allocated and used as the calibrating parameter of the model. The model optimizes the overall farmer's utilities, which includes the revenue and the risk aversion coefficient which is the calibrating parameter, similar for all the farms initially classified in the same type. The allocation of cropping systems is modelled through a set of equations that model the choice of cropping systems by farmers at different scales, namely the field, farm, sub-regional and regional scales. The prediction capacity of the model was assessed in Guadeloupe at field and regional scales by comparing the initial crop areas and the simulated ones and the initial and observed farm types at farm scale. The outputs of the model are the new agricultural landscape and the calculation of 19 sustainability indicators. These indicators assess the impact of agriculture on society and environment at a regional scale by accounting for cropping system externalities at plot scale and the location of these cropping systems throughout the region by using scale change methods.

As an illustration of its possible interest, we used the model to assess the consequences of three scenarios accounting for expected future modifications of subsidies. In the “area reallocation” scenario, the subsidies are reallocated to each crop with an amount of 1768€.ha⁻<fnref n="1" />. The “workforce reallocation” is the reallocation of subsidies per unit of workforce with an amount of 15569€ per worker. The “decoupling” scenario is the decoupling of subsidies from production.

## 3 Results – Discussion

Results from the evaluation were good at all the scale tested (similarity of 91% at regional scale, 80% at farm scale and 75% at field scale) which means that the model can be used for scenario analysis. The scenario modified the repartition of cropping systems in landscapes in a spatially explicit way. The results are summarized in this abstract at the regional scale (Fig. 1). The general trends observed throughout the three scenarios demonstrate a sharp decrease in sugarcane and banana production over the island with their total disappearance in the "area reallocation" scenario and in the "decoupling" scenario only for banana farms. By contrast, the areas devoted to pasture and fallow fields increase and the area devoted to crop gardening and orchards increase more progressively, as well. The consequences of these new landscapes are assessed based on spatially explicit indicators at the regional scale (Table 1).

<footnote n="1">¹INRA, UR1321 ASTRO Agrosystèmes Tropicaux, Department, Institute, Faculty, University, F-97170 Petit-Bourg (Guadeloupe), France</footnote>

<footnote n="2">² AgroParisTech, UMR 211 Agronomie, F-78850 Thiverval-Grignon, France</footnote>

<footnote n="3">³ INRA, UMR 211 Agronomie, F-78850 Thiverval-Grignon, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: pierre.chopin@antilles.inra.fr</footnote>
<figure><img src="image_59.png" /><figcaption>Fig. 1. Evolution of crop acreage from scenarios</figcaption></figure>

<table><caption>Table 1. Spatially explicit assessment of agricultural landscapes from scenarios</caption><thead><tr><th>Indicators</th><th>Base year</th><th>"Area reallocation"</th><th>"Workforce reallocation"</th><th>"Decoupling"</th></tr></thead><tbody><tr><td>Agricultural added value (M€.yr<sup>-1</sup>)</td><td>96</td><td>138</td><td>125</td><td>162</td></tr><tr><td>Total amount of subsidies (M€.yr<sup>-1</sup>)</td><td>75</td><td>60</td><td>62</td><td>72</td></tr><tr><td>Ratio of nutrients produced over needs</td><td>15%</td><td>20%</td><td>23%</td><td>23%</td></tr><tr><td>Potential electricity production with crops (MW.yr<sup>-1</sup>)</td><td>33</td><td>16</td><td>24</td><td>0</td></tr><tr><td>Total needs of workforce (persons)</td><td>3105</td><td>2566</td><td>2928</td><td>2772</td></tr><tr><td>Area of risk of contamination of food crops (ha)</td><td>1170</td><td>2013</td><td>1529</td><td>1843</td></tr><tr><td>Ratio of water bodies potentially polluted</td><td>35%</td><td>14%</td><td>27%</td><td>18%</td></tr><tr><td>Amount of water needed for irrigation (m3.yr<sup>-1</sup>)</td><td>17.7</td><td>14.7</td><td>19.6</td><td>15.1</td></tr><tr><td>CO2 emissions from farming (kT eq CO2.yr<sup>-1</sup>)</td><td>158</td><td>142</td><td>149</td><td>135</td></tr><tr><td>Diversity of crops across landscape (Simpson's Index)</td><td>3.0</td><td>3.1</td><td>3.4</td><td>2.7</td></tr></tbody></table>

In terms of economic sustainability, these three developed scenarios were relevant because they performed better than the base year in terms of economic sustainability especially the “area reallocation” scenario with the increase of the agricultural added value to 138M€.yr⁻¹ and the decrease of subsidies to 60M€.yr⁻¹. For social sustainability, the scenarios performed better in terms of food self-sufficiency, especially scenario 3, with an increase of nearly 50% in food-self-sufficiency. In focusing on environmental sustainability, the pressure on biodiversity and water resources decreased over the three scenarios.

The modelling of farmers cropping system choices and the creation of agricultural landscapes resulting from the modification of a set of rules at different spatial scales is useful for the prototyping of landscapes. This multi-scale modelling is especially important for addressing sustainability issues, such as food security or biodiversity preservation that require multi-scale strategies for resolution. Our set of indicators can even assess the trade-offs in the provision of services by displaying the direction of change in the indicator value between the base year and the scenarios.

## 4 Conclusions

The bioeconomic model MOSAICA integrates the different scales involved in the decision-making process of farmers and policy makers, *id est* the plot, farm, sub-regional and regional scales. This integration of agricultural systems allows for the testing of the multi-scale policy and parameter changes that are involved in agricultural production and the assessment of these changes on the provision of ecosystem services with spatially explicit indicators.

Our model could be relevant for testing spatially targeted policies aimed at improving the contribution of agriculture to sustainable development.

## References

* Chopin, P., Blazy, J.-M. & Doré, T. (2015). A new method to assess farming system evolution at the landscape scale. Agronomy for Sustainable Development **35**, 325-337.

* Delmotte, S., Lopez-Ridaura, S., Barbier, J.-M. & Wery, J. (2013). Prospective and participatory integrated assessment of agricultural systems from farm to regional scales: Comparison of three modeling approaches. Journal of Environmental Management **129**, 493-502.
# Linking modelling with optimization techniques to maximise farming system productivity - Preliminary assessment in an experimental field study

Subash Nataraja Pillai *±<fnref n="1" />, Jagpal Singh <fnref n="1" /> & Babooji Gangwar <fnref n="1" />

## 1 Introduction

Majority of farmers in India practise subsistence farming. It is characterised by small and scattered land holdings, the whole family works on the farm, most of the work is done manually and most of the farm produce is consumed by the family. The types of farming systems practiced by the small holders in India vary across space and time. The interaction of biophysical, technological, institutional and socio-economic conditions leading to temporal and spatial variability among the farming systems. However, the enterprises of the farming system depends largely on location specific and farmer centric nature. The farming system for Nutrition (FSN) model protocol demonstrate the feasibility of a wide-ranging and sustainable nutrition-sensitive agricultural intervention (Das *et al*., 2014). It tries to capture the extent of productivity and profitability enhancement in the farming system contributing to enhanced spending by the household towards balanced diet. In another approach used a whole farm model - APSFarm in a participatory modeling approach to examine the sensitivity of four contrasting case study farms to a likely climate change scenario (Rodriguez et al, 2014). A review on farm household modeling showed that there are enough techniques for integrated assessments of farm systems in relation to climate change, adaptation and mitigation, but they have not yet been combined in a way that is meaningful to farm level decision makers (van Wijk *et al*., 2012). However, there is a need to include the resource availability of the farmer, and their capability to sustain under risky weather, particularly unseasonal weather aberrations. It is very complex to model the entire farming system in a single tool. If you succeed in linking modeling with optimization techniques, then it will be highly useful for the small holder farming community to select the farming enterprises based on their resource availability and agro-climatologically suitability. With this aspect in mind, we have initiated a study to link modeling with optimization techniques to maximise farming system productivity along with the food habit/nutritional security of the small farmers. The methodological framework for the region was developed and monitored one year data from the field experiment which consists of different enterprises crop sequences (rice, wheat, maize etc), livestock and fish pond.

## 2 Materials and Methods

The study area is located in Meerut (29°4' N, 77° 46' E, 237 m), part of the Upper Gangetic agro-climatic region of the Indo Gangetic Plains (IGP), India (Fig. 1). The climate is semi-arid subtropical, with dry hot summers and cold winters. Meteorological data were collected from the agro-meteorological observatory located at the Project Directorate of Farming Systems Research, Modipuram, near to the experimental site, during the period 1992–2010. Data collected include daily maximum and minimum temperatures, rainfall, and sunshine hours. The solar radiation was estimated based on sunshine hours, by using the Angstrom equation (Angstrom, 1924; Medugu and Yakubu, 2011).

<figure><img src="image_60.png" /><figcaption>**Fig. 1.** The Indo-Gangetic Plains in India, indicating agro-climatic zones and the location of the study site in Meerut District, Uttar Pradesh, India.</figcaption></figure>

<figure><img src="image_61.png" /><figcaption>**Fig. 2.** Typical farming system followed in the Meerut district of northwest India.</figcaption></figure>

<footnote n="1">¹ICAR-Indian Institute of Farming Systems Research, Modipuram-250110, Meerut, Uttar Pradesh, India.</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: nsubashpdfsr@gmail.com</footnote>
The climate data for the period 1980–1991 were created using the bias-corrected AgMERRA (Ruane *et al.*, 2014) satellite-derived data. The soil of the experimental site is a sandy loam (18% clay, 19.5% silt, and 62.5% sand) of Gangetic alluvial origin. It is very deep (>2 m), well

drained, flat (about 1% slope), and representative of an extensive soil series, i.e., the Sobhapur series of northwest India. Rice-wheat and sugarcane-wheat are the predominant cropping systems in the area with livestock being an integrated part of the farming system of sample households. However, in this present study we have considered only the rice-wheat farms of each households. Livestock holding is generally proportional to land holding but majority of the farmers, even with tiny land holdings, keep at least one milch animal (indigenous or crossbred cow and/or buffalo). The study site has excellent irrigation facilities with almost 99% cultivable area being irrigated. The typical farming system followed in this region is depicted in Fig.2. The methodological framework used for the study is given in Fig 3.

<figure><img src="image_62.png" /><figcaption>**Fig. 3.** Methodological framework of FSDST (Farming System Design Support Tool)
We have used APSIM7.5 to build the simulation set up of major crops. Since we have grown the other crops under controlled environment, we have used the yield obtained as the potential yield.</figcaption></figure>

**3 Results - Discussion**

Based on the experiments conducted at the same study site during a period of six year (2004-2010), with a farmer having 1.5 hectare irrigated land, besides fulfilling all the requirement of 7 members household food and fodder demand (animals) inclusive cost of production, could create an additional average annual saving, which will be helpful to meet the farmer for health, education and social customs and thereby improved the livelihoods (Singh *et al.*, 2011). During the year 2013-14, we have completed the recording of crop phenology, crop growth, yield and yield attributes for two crop seasons - kharif and rabi for all important crop enterprises. The potential yields of rice, wheat, maize and sugarcane were simulated through APSIM7.5. Some enterprises, crop calibrations are not available and hence we have used maximum yield of other experiments as the potential yield. As far as livestock component is concerned, we have used the secondary data for estimating the household potential yield. The fish yield for one year was also monitored and taken this as the potential yield because it is located in the research farm and followed all the recommended management practices. To compare the productivity of all farming system enterprises, converted the productivity into one major crop ie., rice equivalent yield. LINGO14.0 was used for optimization of farming system enterprises.

**4 Conclusions**

The initial results indicated that linking of modeling with optimization tool provide an integrated approach to pinpoint the environmentally location specific enterprises of farming system based farmer centric resources. More research and fine tuning of methodology is needed for developing an integrated FSDST (Farming System Decision Support Tool).

***Acknowledgements.*** The authors are thankful to Indian Council of Agricultural Research for providing necessary funds to take up this study. The authors are also thankful to anonymous reviewers for their patience and time to go through the technical aspects of this study during the abstract submission stage.

**References**

Ångstrom, A. (1924). Solar and terrestrial radiation. Quarterly Journal of Royal Meteorological Society **50**, 121-126.

Medugu, D.W., Yakubu, D. (2011). Estimation of mean monthly global solar radiation in Yola-Nigeria using angstrom model. Advances in Science and Research **2**(2), 414-421.

M.T. van Wijk, M.C. Rufino, D. Enahoro, D. Parsons, S. Silvestri, R.O. Valdivia, M. Herrero. 2012. A review on farm household modelling with a focus on climate change adaptation and mitigation. Working Paper No. XX, Kopenhagen.

Rodriguez, D., Cox, H., de Voil, P. and Power, B. 2014. A participatory whole farm modelling approach to understand impacts and increase preparedness to climate change in Australia. Agricultural Systems **126**, 50-61.

Ruane, A.C., Goldberg, R. and Chryssanthacopoulos, J. (2014). AgMIP Climate forcing datasets for agriucultural modeling: Merged products for gap-filling and historical climate series estimation. Agricultural Forest Meteorology **200**, 233-248. doi. 10.1016/j.agr.formet.2014.09.016.

Singh, J.P., Gangwar, B., Pandey, D.K. and Kochewad, S.A. 2011. Integrated farming system model for small farm holders of western plain zone of Uttar Pradesh. PDFSR Bulletin No. 05. PP.58, Project Directorate for Farming Systems Research, Modipuram, Meerut, India.
# Characterization of farming systems in drylands of South Asia for assessing resource constraints and technologies

Thiagarajah Ramilan<sup>±1,2</sup>, Shalander Kumar<sup>1</sup>, Nageswara Rao<sup>1</sup> & Anthony Whitebread

## 1 Introduction

Millions of smallholder farmers in South Asia derive their livelihoods from dryland agricultural systems. These agricultural production systems are characterized by low productivity, low farm incomes and natural resource degradation. There is a growing emphasis on sustainable intensification of these systems for improving farmer livelihoods. The CGIAR research program on Dryland Systems has adopted a systems approach to enhance agricultural productivity through technological interventions in South Asia. It is important to identify options that are manageable within the context of the farmer's resource base and the household's objectives that could improve farm household well-being. This study focuses on the development of representative farming systems for subsequent development of bio-economic farm model. Representative farming systems are developed using objective approach. We adopted an objective approach to determine the number of representative farming systems by using Model-based clustering method based on finite mixtures (Fraley & Raftery, 2002).

## 2 Materials and Methods

Farm level data is captured using, crop simulation modeling and on farm trials. The typology of representative farms is created through multivariate analysis of 780 observations from pooled cross section data from 2009 to 2012, obtained from Village Dynamics in South Asia (VDSA) farm household survey conducted by International Crop Research Institute for Semiarid Tropics (ICRISAT, 2014). Multivariate outliers are eliminated based on Mahalanobis' distance to enable robust estimation (Todoro et al., 2011). Then forty two socio economic and bio physical variables are identified (key variables are listed in the Table 1), then we applied principal component analysis to create latent variables. The typology of representative systems is created through cluster analysis on latent variables. We adopted an objective approach to determine the number of clusters by using Model-based clustering method based on finite mixtures (Fraley & Raftery, 2002). In this method, number of clusters is determined statistically contrary to conventional methods such as hierarchical or kmeans method, which depends on subjective judgment.

## 3 Results - Discussion

Optimal number of clusters is identified based on Bayesian Information Criterion (BIC). In this case, the best model according to BIC is a variable-covariance model (MClust VVV) with 5 components or clusters (Fig. 1).

<figure><img src="image_63.png" /><figcaption>Fig. 1. BIC values across the clusters under different covariance structure</figcaption></figure>

<footnote n="1">¹ International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502324 India</footnote>

<footnote n="2">² Honorary Fellow, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Victoria, Australia</footnote>

<footnote>* Speaker</footnote>

<footnote>‡ Corresponding author: t.ramilan@cgiar.org</footnote>
<table><caption>Table 1. Descriptive statics of farm clusters</caption><thead><tr><th>Farming Systems<br>Variables</th><th>Type1</th><th>Type 2</th><th>Type 3</th><th>Type4</th><th>Type 5</th></tr></thead><tbody><tr><td>Adult mean education (years)</td><td>2.6</td><td>6.0</td><td>3.5</td><td>7.2</td><td>5.4</td></tr><tr><td>Farm size (ha)</td><td>1.8</td><td>5.6</td><td>0.0</td><td>1.8</td><td>2.5</td></tr><tr><td>Irrigated area (ha)</td><td>0.0</td><td>1.9</td><td>0.0</td><td>0.2</td><td>1.2</td></tr><tr><td>Good_soil_area</td><td>0.6</td><td>2.1</td><td>0.0</td><td>1.1</td><td>1.1</td></tr><tr><td>Drought tolerant crop area (ha)</td><td>0.8</td><td>1.8</td><td>0.0</td><td>0.6</td><td>0.8</td></tr><tr><td>Value of crop produce marketed (USS)</td><td>157</td><td>1645</td><td></td><td>1467</td><td>244</td></tr><tr><td>value of livestock product marketed (USS)</td><td></td><td>39351</td><td></td><td>1</td><td>0223</td></tr><tr><td>Cash crop area (ha)</td><td>0.1</td><td>1.3</td><td>0.0</td><td>0.2</td><td>0.7</td></tr><tr><td>Tropical Livestock Units (TLU)</td><td>0.5</td><td>1.0</td><td>0.1</td><td>0.0</td><td>0.8</td></tr><tr><td>Fertiliser use (kg/ha)</td><td></td><td>44165</td><td></td><td>062</td><td>232</td></tr><tr><td>Purchased feed (US$)</td><td></td><td>14115</td><td></td><td>0</td><td>064</td></tr><tr><td>Crop gross margin (US$)</td><td>119</td><td>2315</td><td></td><td>0270</td><td>848</td></tr><tr><td>Livestock gross margin (US$)</td><td>212</td><td>826</td><td>38</td><td>17</td><td>504</td></tr><tr><td>Nonfarm Income (US$)</td><td>504</td><td>1165</td><td>991</td><td>1695</td><td>812</td></tr><tr><td>Off farm income (US$)</td><td>701</td><td>167</td><td>482</td><td>193</td><td>341</td></tr><tr><td>Farm Machinery (US$)</td><td></td><td>83966</td><td>43</td><td>171</td><td>362</td></tr><tr><td>Value of durable goods (US$)</td><td>887</td><td>3641</td><td>1006</td><td>2001</td><td>1617</td></tr><tr><td>Building assets (US$)</td><td>1628</td><td>3739</td><td>1802</td><td>3031</td><td>1838</td></tr><tr><td>FamilyREA.hours.sum</td><td>201</td><td>844</td><td></td><td>0228</td><td>533</td></tr><tr><td>Hired.REA.hours.sum</td><td>108</td><td>1007</td><td></td><td>0237</td><td>579</td></tr></tbody></table>

Table 1 reveals the existence of wider heterogeneity in farm types. It ranges from land less households to irrigated-livestock cropping systems. The drivers for the heterogeneity are varying access to irrigation, livestock units, non-farm employment and land size The farm types are validated through informal interviews, key informant interviews and focus group discussions. We found potential interactions among different farm types such as landless farmers provide labour as well as linking with markets for trading livestock and restock inputs with farm based systems.

## 4 Conclusions

Synergies and tradeoffs on range of scenarios on technological interventions and resource constraints will be assessed, relating to changes in current enterprise mixes, potential for intensification and environmental impact. Indicators generated from the model are useful for effective farming system design and up scaling to larger areas, when linked to the typology.

Acknowledgements : Authors are greatfull to National Agricultural Research system Partners

## References

* Fraley, C., & Raftery, A. E. (2002). Model-based clustering, discriminant analysis and density estimation. *Journal of the American Statistical Association*, 97, 611–631.

* ICRISAT. (2014). Village Dynamics in South Asia (VDSA) database. Retrieved from http://vdsa.icrisat.ac.in/

* Todorov, V., Templ, M., & Filzmoser, P. (2011). Detection of multivariate outliers in business survey data with incomplete information. *Advances in Data Analysis and Classification*, 5(1), 37–56.
# Identifying sustainable farms under diverse agro-ecological conditions and livelihood strategies in southern Africa: An interdisciplinary simulation-based approach

David Berre *±<fnref n="1" />, Munyaradzi Mutenje <fnref n="1" />, Marc Corbeels <fnref n="2" />, Leonard Rusinamhodzi <fnref n="3" />, Christian Thierfelder <fnref n="1" /> & Santiago Lopez-Ridaura <fnref n="4" />

## 1 Introduction

Agricultural systems face the challenge to feed a growing population while at the same time reducing their environmental impact in a changing world subjected to shocks, such as extreme weather and economic volatility (Godfray *et al.*, 2010). In this global context, southern Africa appears as a particularly sensible region due to the fact that more than 60 % of the livelihoods rely on rain-fed agriculture and have low adaptation capacity (Zinyengere *et al.*, 2014). The diversity of biophysical and socio-economic situations in this region requires prudence in the promotion of "good practices", and a good understanding of local farming systems complexity and their current level of inefficiency. In order to identify the most efficient farms (i.e. minimizing their inputs and simultaneously maximizing their outputs), we implemented the Data Envelopment Analysis (DEA, Charnes *et al.* (1978)) method, which is the most commonly used non-parametric frontier efficiency approach. Using results from farm household surveys conducted in Zambia and Malawi, we modelled the efficiency frontier based on observed best practices in 2012 and identified potential progress margin of efficiency. Even if those results are themselves significant outcomes as efficiency analysis in southern Africa are very rare (Chiona *et al.*, 2014), the originality of this research is the combination of DEA with APSIM (Agricultural Production Systems Simulator) crop model to assess the evolution of this multi-dimensional efficiency frontier. The crop growth model APSIM was used to simulate the performance of a wide range of maize-based cropping systems under different agro-ecological conditions and future climatic scenario, for several types of farming systems identified on structural data basis (cattle, adult equivalent, income, etc.). With this approach we aim to identify cropping systems and farms in southern Africa that are most efficient under future climate.

## 2 Materials and Methods

<figure><img src="image_64.png" /><figcaption>**Fig.1.** Data overview in the study region: From field scale (long-term agronomic trials) to farm scale (farm household surveys)</figcaption></figure>

<footnote n="1">¹ International Maize and Wheat Improvement Centre (CIMMYT) - P.O. Box MP 163, Mount Pleasant, Harare, Zimbabwe</footnote>

<footnote n="2">² Centre de Cooperation Internationale en Recherche Agronomique pour le Développement (CIRAD) - C/O Embrapa-Cerrados, BR 020 - Rodovia Brasilia/Fortaleza, Planaltina, DF, Brazil, Brazil</footnote>

<footnote n="3">³ Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - C/O CIMMYT - P.O. Box MP 163, Mount Pleasant, Harare, Zimbabwe</footnote>

<footnote n="4">⁴ International Maize and Wheat Improvement Centre (CIMMYT) -CCAFS, Texcoco, Mexico.</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: d.berre@cgiar.org</footnote>
Figure 1 shows the location of the long-term agronomic trials used for APSIM calibration and the number of farm households investigated in each district where trial data were available. At the field scale, APSIM (v. 7.6) was calibrated using long-term trial data and future climate was generated with 17 General circulation models (GCM) for two extreme emission scenarios (RCP 2.6, RCP 8.5) (Rusinamhodzi et al., 2015). At the farm scale, farming typology has been implemented with a classic Principal Component Analysis - Ascendant Hierarchical Classification in R 3.0.0. Compared to data displayed in Figure 1, the final typology includes only half of the data available as some survey was incomplete and/or data inconsistent. The efficiency frontier analysis was implemented in GAMS 23.4. The chosen model assumed variable return to scale, i.e. marginal cost differs for each level of inputs and outputs, which implies that each farm can only be compared with farms of similar size. Undesirable outputs are included in the model using the weak disposability assumption (Färe et al., 1989), i.e. to connect undesirable production to the desirable output, while the first one is minimized and the later maximized.

**3 Discussion**

<figure><img src="image_65.png" /><figcaption>**Fig.2.** Combining efficiency frontier analysis with APSIM for ex-ante assessment of climate-smart practices</figcaption></figure>

Figure 2 represents the modelling framework implemented to identify future climate smart practices in southern Africa. In this simulation-based optimization modelling approach, APSIM allows simulating the levels of inputs and outputs for several combinations of climate change scenarios, agronomic practices and agro-ecological conditions, for each types of farming system identified. DEA, represents the optimization model which allows assessing on a multidimensional plan the most appropriate climate-smart practices, for every level of inputs, outputs, and undesirable outputs. Compared to classical approach where farm and crop models are combined in a complex integrated framework, our model represents a “loose coupling approach” as promoted by Van Wijk *et al*. (2012) in their review of farm household modelling in climate change context.

**4 Conclusions**

The diversity of agro-ecologic and socio-economic situations in southern Africa must be tackled with a site and farm-specific calibration of crop models and ad-hoc farm models taking into account farm constraints and farmers’ objectives. Frontier efficiency analysis appears as very promising approach compared to other optimization model (e.g. multi objective optimization, multicriteria decision making, etc.) as it allows identifying a whole set of efficient solutions (instead of few Pareto ones) and an interdisciplinary interpretation including microeconomic theory (marginal cost, allocative inefficiency).

*Acknowledgements.* Authors want to thanks The International Fund for Agricultural Development (IFAD) and the Climate Change Agriculture and Food Security program (CCAFS- CGIAR/CRP Twin Post-Doc) who funded this research.

**References**

Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units, European Journal of Operational Research, 2, pp. 29-44.

Chiona, S., Kalinda, T., Tembo, G., 2014. Stochastic frontier analysis of the technical efficiency of smallholder maize farmers in Central Province, Zambia. Journal of Agricultural Science, 6, 10, 108-118.

Fare, R., Grosskopf, S., Knox Lovell, C.A., Pasurka, C., 1989. Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach, The review of economics and statistics, 71, pp. 90-98.

Godfray, H.C.J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., Pretty, J., Robinson, S., Thomas, S.M., Toulmin, C., 2010. Food security: The challenge of feeding 9 billion people, Science, 327, pp. 812-818.

Van Wijk MT, Rufino MC, Enahoro D, Parsons D, Silvestri S, Valdivia RO, Herrero M. 2012. A review on farm household modelling with a focus on climate change adaptation and mitigation. Working Paper No. 20. CGIAR/CCAFS. Copenhagen, Denmark. Available online at: www.ccafs.cgiar.org

Zinyengere, N., Crespo, O., Hachigonta, S., 2013. Crop response to climate change in southern Africa: A comprehensive review. Global and Planetary Change, Elsevier, 2013, 111, pp.118-126.
# Dynamic Crop Production Responses to Realizations of Weather and Production with Application in Jordan

David Boussios *±1*, Paul V. Preckel 2, Yigezu Yigezu 3 & Prakash N. Dixit 3

## 1 Introduction

Farming decisions across the globe are defined by the uncertainty of output quantities and prices, and this axiom holds true in the semi-arid areas of Jordan, where farmers must adapt throughout the calendar year to manage the variability of production due to the magnitude and timing of the onset of rains. In order to understand and model the response farming practiced by Jordanian farmers, a dynamic and stochastic modelling approach is required.

Agricultural production decisions are shaped by the stochastic interactions of crop growth, weather, and financial outcomes. Incorporation of the biological responses with economic incentives, while intuitive, presents many challenges in modelling. Optimizing agents respond to current biological developments, with only distributional knowledge of future outcomes (as opposed to perfect foresight). The selection and timing of production decisions are important for determining outcomes both in the short-run (e.g. yields, revenues, etc.), as well as in the long-run (e.g. soil moisture and soil organic matter). Producers choose which crops to plant, how much and when to apply inputs, and when and what to harvest. These choices influence the profitability of production each year within a stochastic environment, and when incorporated in a single year model can easily be optimized. However, agricultural production decisions also influence future profitability through their effect on soil properties, such as soil moisture or organic carbon, which are important for both the long term sustainability of the household, and avoiding environmental degradation. Optimization models analyzing intra-seasonal stochastic production and dynamic inter-seasonal resource management present a modeling challenge due to the often encountered curse of dimensionality, where model size increases rapidly exponentially with the number of time periods and stochastic events.

The agricultural systems model presented here overcomes these issues by addressing this problem with a combined Dynamic Programming (DP) model and a portfolio model. The model developed here values the production trade-offs of short- and long-run outcomes, within a stochastic choice model. This methodology allows for the dynamic testing and valuation of various production technologies, including conservation agriculture and crop varieties.

## 2 Materials and Methods

The approach is illustrated with a model of representative agricultural households in Jordan, specifically within the Karak Governorate. Model data is obtained from crop and weather simulators and household surveys. As the data required for a dynamic bio-economic model are intensive and unavailable through historical records, the Agricultural Production Systems Simulator (APSIM) is calibrated at the local level to approximate crop yields given various weather and managerial choices (Keating et al. 2003). To generate a rich distribution of outcomes conditional on production choices and weather conditions, necessary for the stochastic model, a simulated distribution of weather years using LARS-WG 5.5 (Semenov 2010) was generated to serve as inputs to APSIM.

Data were collected at the household level by the International Center for Agricultural Development in the Dry Areas (ICARDA) to define farm household typologies for testing the sensitivity of results and households to differences in various endowments. Households are small-holder farms in the semi-arid region of Central-Western Jordan, typically relegated to barley and wheat production with no access to irrigation for field crops.

A two-part dynamic stochastic model was created to efficiently model conditional production decisions and short- and long-run economic incentives for the farming system. While a single Discrete Stochastic Program (DSP) might be preferred, dependence of transition probabilities for soil attributes (i.e. moisture and organic carbon) across years on managerial choices is not consistent with the DSP approach. For this reason, the soil attribute management problem is addressed in part one of the modeling system using a stochastic DP model over a ten year horizon using discrete choices of crop/conditional management strategies to derive the long-run value of soil attributes. The DP models an optimizing agent maximizing time-discounted returns within and across years given stochastic outcomes and conditional production choices. The resulting dynamic solution, provides estimates of the long-term marginal value of carry-over soil attributes.

Part two of the dynamic stochastic model is based on a single year portfolio model. The choices available can be thought of as investment assets which are defined by set crop/conditional management strategies that fully specify the

<footnote n="1">¹Department of Agricultural Economics, Graduate Research Assistant, Purdue University</footnote>

<footnote n="2">²Department of Agricultural Economics, Professor, Purdue University</footnote>

<footnote n="3">³International Center for Agricultural Development in the Dry Areas (ICARDA), Amman, Jordan</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: dboussio@purdue.edu</footnote>
management practices for the crop given weather outcomes. The distribution of returns to these assets is evaluated for each of the simulated weather years and reflects both the value of crop products (grain and fodder) and a term for valuing the ending stocks of soil attributes derived from the DP model of part one. Choices in the portfolio model are continuous allowing for diversification across crops and input levels to allow for less computationally restrictive risk management strategies. While the portfolio model may seem static at first blush, the incorporation of management choices that depend upon weather outcomes (e.g. timing of planting conditioned on rainfall, weather conditions that trigger fertilizer applications, etc.) make this model effectively dynamic as well as stochastic. The incorporation of a valuation of soil attributes as it affects future production possibilities can be intuitively analogous to the co-state variable in a dynamic Hamiltonian or the expected future returns in a Bellman's equation, where decisions are optimized for a single period given the conditionally optimized future periods.

Combining these two dynamic models builds upon the strengths of each to produce an efficient and tractable system for modeling inter- and intra-seasonal choices. The stochastic process in both models is driven by the combined weather and crop simulation models to provide a large enough sample to generate rich distributions of the returns to crops and conditional managerial strategies. The stochastic portfolio model can be considered similar to a DSP, however, the model defines choices through thresholds and ranges in each time period, as opposed to simplified discrete states. The threshold and range modelling approach does not require state specific assumptive transitional functions, thus maintaining the observed biological process of crop production. The combined DP and portfolio approach capture the short- and long-run benefits and costs of agricultural production in such a way that allows the imposition of constraints in a household modeling framework, such as limits on labor and liquidity, consumption requirements, etc. These household specific features with the combined approach allow for testable hypotheses to the effects of household endowments on choices and outcomes.

## 3 Results – Discussion

The results of the model will show both the importance of dynamic intra-seasonal management responses, as well as the impact of long-run soil valuations on production choices. Optimizing agents balance the trade-offs of short-run income and financial constraints/preferences with the incentives to manage soil quality. With the incorporation of the valuation of soil parameters, optimizing agents, both risk-neutral and risk-averse, will adopt crop rotations and diversified land allocations. The impact of taking a myopic view that does not place a value on soil attributes is also assessed.

The model is also used to assess the long-term benefits of innovations such as zero-tillage for increasing the sustainability of the farming system by better management of soil attributes, showing the importance of incorporating the financial incentives with the biological trade-offs. As planting strategies influence current year outcomes, they also directly influence the distributions of the end-of-year soil attributes, which determines future production potential. The results illustrate the dynamic trade-off, through the valuation and incorporation of soil attributes, of zero- versus conventional tillage.

## 4 Conclusions

We develop a two-part dynamic stochastic model that integrates the stochastic biological process with the financial and economic incentives of crop production. Land use and input decisions are based on short- and long-term trade-offs of current and future returns which includes the impact of weather-related risks. By analyzing the long-run horizon through a dynamic programming model that ignores short-run constraints on household resources, we derive a dynamic valuation of soil attributes. This valuation is then used in a short-term dynamic, stochastic model of the household that focuses on issues of crop mix and the diversification of conditional management strategies for the purpose of measuring the sustainability of the household and their resource management strategies. This hybrid approach results in a tractable model which can be used to optimize production behavior over both the short- and long-run.

## References

* Keating, Brian A, Peter S Carberry, Graeme L Hammer, Mervyn E Probert, Michael J Robertson, D Holzworth, Neil I Huth, John NG Hargreaves, Holger Meinke, and Zvi Hochman. 2003. "An overview of APSIM, a model designed for farming systems simulation." *European Journal of Agronomy* no. 18 (3):267-288.

* Semenov, Mikhail. 2010. "The Use of Multi-Model Ensembles from Global Climate Models for Impact Assessments of Climate Change." *Climate Research* no. 41:1-14.
# Strategic and tactical Urban Farm design, a conception and sizing problem

Nicolas Brulard *±1,2*, Van-Dat Cung 2 & Nicolas Catusse 2

## 1 Introduction

During the last decades, urban agriculture have been brought up to date in developed countries. Adapting existing peri-urban farms and creating new urban farms with robust and sustainable business models are major issues to enable this new development. Indeed, urban farm systems are more constrained than rural ones by infrastructures and practices restrictions and higher costs (investments, logistics). But urban farms can easily integrate multi-functionality, combining production, leisure activities, environmental or logistic services (Mougeot, 2000 ; Ogier et al., 2013).

Many works have been made on Sustainable Food Supply Chain and Agri-Food Supply chain since the 2000s. Ahumada and Villalobos (2009) developed production-delivery tactical models to maximize the producer income. Other works concern the storage of perishable products (Costa, 2011) but they often focus on an industrial fresh products buyer point of view (Tan & Çömden, 2012), with a few variety of products. Multiproduct market gardening has been less studied than specialized systems like dairy or grain systems.

We develop a strategic and tactical model to integrate and optimize both fruit and vegetable production and logistics in urban sites selection and multifunctional farming system design (goods and services production).

## 2 Materials and Methods

We use mix integer linear programming to establish a model for fresh fruit and vegetable production. We aim at maximizing the annual farm result, answering a fixed daily demand. Our main variables are the area $a_i$ to grow using management practices $i$, the beginning date of each plot cultivation $d_i$ and the workers to hire to produce $mod_i$. We introduce other variables to consider the perishability of the products in different ways. As Ahumada and Villalobos (2009), we use a storage cold room loss function and cold room limited capacity. But we also allow a standing storage on the plot, associated to another loss function: the crops can be harvested some days after the ideal harvest time. The selling price is lowered according to storage time, both on the plot and in the cold room. We force the unsold products left on the plots (losses) to be harvested before a management practice dependent deadline, to limit diseases and insects proliferation.

Considering short time perishability (a few days) make us work on a tow-day step. It enables a good combination of different crop management practices on the farm to have the best labor affectation. Perishability, considered at a tactical level, has major consequences on the strategic farming system sizing. Climate is not taken into account as we do not need operational outputs, but management practices beginning can only vary in a small time period, to guarantee realistic solutions. Treasury and water consumption can be used as constraints according to the contexts.

This deterministic problem integrates lot sizing problems. Dependences between areas, harvest and delivery dates of several products, growing tasks and labor make this problem highly combinatorial and hard to solve on big data sets.

## 3 Results - Discussion

Following results are obtained using Cplex 12.6 on Intel Core i7-4600U 2.10GHz processor and 8.0 Go of RAM. Given a certain demand, the model affects areas to the plots cultivated with different management practices, which are represented by a production curve ($x \text{ kg.m}^{-2}$ reaching maturity on day $d$), a set of cultivation tasks and some parameters such as the range of areas in which the management practices are defined. With our data set, the best profitability is obtained combining 5 management practices, (i4, i5) for product 1, (i6, i8, i9) for product 2 (Fig. 1). 90% of the available area is used.

<figure><img src="image_66.png" /><figcaption>Fig. 1. Plot areas obtained by the model</figcaption></figure>

<footnote n="1">¹ Jardins de Gally, Ferme de Vauluceau, 78870 Bailly</footnote>

<footnote n="2">² Univ. Grenoble Alpes, G-SCOP, F-38000 Grenoble, France / CNRS, G-SCOP, F-38000 Grenoble, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author:nbrulard@gally.com</footnote>
<figure><img src="image_67.png" /><figcaption>**Fig. 2.** Demand (line), quantities harvested each day (dark bars) and on-plot available quantities (light areas)</figcaption></figure>

<figure><img src="image_68.png" /><figcaption>**Fig. 3.** Labor to hire (line) to harvest (dark bars) and cultivate (light bars), including idle time (stripes)</figcaption></figure>

To answer the demand, 1 permanent farmer and 3 seasonal workers are needed (Fig. 3). Operations planning takes into account the daily demand (quantities and prices) of two clients to affect the workers to the harvest or to malleable cultivation tasks, such as weeding or trimming. Fig. 2 shows two major phases in April and August when too much products are available on the farm compared to the demand and products are stored several days in cold room (not depicted here). On this test data set, the model enable to deliver 81% of product 1 demand and 88% of product 2 demand, with 9% of losses over the year, to reach an result of 281 k€.

According to the data set parameters, the optimal solution is reach in 2 to 50 minutes, as no work has been not to improve the resolution process yet. Realistic farm designs have been obtained with real data set, producing different varieties of strawberries, tomatoes and lettuce to deliver 2 to 4 clients.

## 4 Conclusions

Using mix integer linear programming, we manage to model the tactical combination of different fruit and vegetable productions and its impact on the strategic sizing of urban farms. The model gives good results on real data set and are currently compared with field experimentation.

Besides the resolution process improvement, next steps will consist to integrate logistics (supply and delivery) in the model to select the best farm locations in a city, and services to get relevant tactical and strategic models of multifunctional urban farms. Variability and robustness will also be integrated.

*Acknowledgements.* This work is part of a PhD thesis “Decision support tool for the design of urban agricultural production systems optimized for the sale of goods and services in local supply chain” supported by Les Jardins de Gally as part of a ANRT funding.

## References

* Ahumada, O., & Villalobos, J. R. (2009). A tactical model for planning the production and distribution of fresh produce. Annals of Operations Research, 190(1), 339–358.

* Costa, A. M., dos Santos, L. M. R., Alem, D. J., & Santos, R. H. S. (2011). Sustainable vegetable crop supply problem with perishable stocks. Annals of Operations Research, 219, 265–283.

* Martin, G., Martin-Clouaire, R., & Duru, M. (2013). Farming system design to feed the changing world. A review. Agronomy for Sustainable Development, 33(2013), 131–149.

* Mougeot, L. (2000). Urban agriculture: definitions, presence, potentials and risks. Growing Cities, Growing Foods: Urban Agriculture on the Policy Agenda, 1–42.

* Ogier M., Cung V.-D., Boissière J. (2013), "Service network design in short and local fresh food supply chain," RAIRO - Operations Research, 47(4). 445-464, 2013.

* Tan, B., & Çömden, N. (2012). Agricultural planning of annual plants under demand, maturation, harvest, and yield risk. European Journal of Operational Research, 220(2), 539–549.
# A household model to assess consumption-production-resources nexus in West Africa: The rice based farming systems in Sierra Leone

Roza Chenoune *<fnref n="1" />, Thomas Allen <fnref n="2" />, Guillermo Flichman <fnref n="3" />, Yigezu Yigezu <fnref n="4" />, Alain Capillon <fnref n="5" />, Hatem Belhouchette <fnref n="1" />

## 1 Introduction

In West Africa, several programmes have been established in order to boost rice production and consumption in those countries. However, these actions are often carried out at a national level without real consideration for the highly contrasting needs of agricultural households across a territory. This also implies the use of more specific approaches and methods that can quantify agricultural production while taking into account the diversity of cropping systems and of household food needs (Herrero *et al.*, 2014). The aim of this paper is to present a non-linear optimization model that highlighting the production strategies of rice farming households with non-separability between production, consumption, and available resources. The model is applied for rice production in the north of Sierra-Leona, which is known for its significant rice production, but also for its low and variable yields and consumption levels, which are among the lowest in the world. It also defines and assesses political alternatives encouraging agricultural production, and therefore the improvement of food consumption.

## 2 Materials and Methods

The methodology is composed from 3 steps:

- **Selection of representative rice farming household:** Based on clustering analysis, only four rice farming households were selected and studied by taking into account structural, resources, production and consumption criteria (table 1, for more details see Chenoune *et al.*, 2014).

<table><caption>**Table 1.** Farm types by considering structural, production and consumption criteria. The intensification level is determined based on seeds and labour amounts and the denomination of lowland or upland is expressed based on the percentage of each ecosystem by farm type (for more details see Chenoune *et al.*, 2014).</caption><thead><tr><th rowspan="3"></th><th colspan="4">Class 1: high rice consumption household</th><th colspan="4">Class 2: low rice consumption household</th></tr><tr><th colspan="2">upland<sub>intensive</sub></th><th colspan="2">lowland<sub>intensive</sub></th><th colspan="2">upland<sub>extensive</sub></th><th colspan="2">lowland<sub>extensive</sub></th></tr><tr><th>Mean</th><th>Std.deviation</th><th>Mean</th><th>Std.deviation</th><th>Mean</th><th>Std.deviation</th><th>Mean</th><th>Std.deviation</th></tr></thead><tbody><tr><td>Total farm area (ha)</td><td>3.7</td><td>0.87</td><td>1.92</td><td>0.65</td><td>3.14</td><td>0.63</td><td>0.94</td><td>0.35</td></tr><tr><td>Rice seed density (t/ha)</td><td>0.05</td><td>0.03</td><td>0.09</td><td>0.03</td><td>0.02</td><td>0.01</td><td>0.02</td><td>0.02</td></tr><tr><td>Rice production (t/farm)</td><td>1.18</td><td>0.3</td><td>0.8</td><td>0.28</td><td>0.3</td><td>0.19</td><td>0.2</td><td>0.12</td></tr><tr><td>Rice total labour (d/ha)</td><td>145</td><td>39</td><td>121</td><td>67</td><td>99</td><td>30</td><td>59</td><td>30</td></tr><tr><td>Size of family (member)</td><td>10.2</td><td>5</td><td>5.6</td><td>1</td><td>8.3</td><td>4</td><td>6.8</td><td>4</td></tr><tr><td>Rice consumption (t/capita/year)</td><td>0.075</td><td>0.013</td><td>0.094</td><td>0.012</td><td>0.057</td><td>0.008</td><td>0.054</td><td>0.005</td></tr><tr><td>Total calorie /cap/day (kcal)</td><td>1338</td><td>180</td><td>1739</td><td>850</td><td>900</td><td>169</td><td>843</td><td>695</td></tr></tbody></table>

- **The farm household model specification:** The household model developed in this study is a static annual model with a utility based on the full income approach, which includes both consumption and farm income (Strauss, 1984). The Utility function is described as following:

$$ Max U = \left( \sum_{pr} R_{pr}^{v} + \sum_{pr} A_{pr} P_{pr}^{a} - \sum_{mope} Q_{mope} P_{m0}^{l} - \sum_{cst,t} C_{cst,t} X_{cst,t} \right) - \phi \partial $$

Where U is the value of the utility function, P is the matrix of agricultural revenue by products (pr) sold and price v; A is the matrix of the amounts of products consumed (self-consumption), Pa are the shadow prices of agricultural products kept for self-consumption, Q is, for each activity, the total labour cost expressed by gender (mo) and period (pe), i.e. sowing, weeding and harvesting, Pl are the daily labour prices expressed by gender, C are the total input costs (other

<footnote n="1">¹ CIHEAM-IAMM-UMR-System</footnote>

<footnote n="2">² OCDE</footnote>

<footnote n="3">³ CIHEAM-IAMM</footnote>

<footnote n="4">⁴ ICRADA</footnote>

<footnote n="5">⁵ UMR-System (SupAgro)</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: chenoue@iamm.fr</footnote>
than labour) expressed by crop (c), ecosystem type (s), intensification level (t) and fallow duration (j), Ø risk aversion coefficient and standard deviation of farm income by considering market price and yield variability in the study area.

- **Scenario specifications:** Table 2 summarizes the 4 tested scenarios targeting to promote the rice production and consumption in the Bombali District.

<table><caption>**Table 2.** Scenarios definition.</caption><thead><tr><th colspan="2">Scenario definition</th></tr></thead><tbody><tr><td>S<sub>baseline</sub></td><td>Baseline scenario. This scenario represents the current situation and should be serving as a reference to evaluate the impacts of below incentive scenarios in terms of rice production and consumption.</td></tr><tr><td>S<sub>seeds</sub> scenario</td><td>Subsidizing the purchase of rice seeds. It seeks to assess the impact of a gradual covering of rice seed purchase cost per hectare, varying its value from 0% to 100%, with a 20% pitch</td></tr><tr><td>S<sub>op</sub> scenario</td><td>Subsidizing the plantation of oil palm scenario as a cash crop. Several levels of subsidization are applied to cover the cost of oil palm plantation per hectare gradually, varying from 0% to 100% (with a 20% pitch)</td></tr><tr><td>S<sub>rice</sub> scenario</td><td>Subsidizing rice plantation in lowland ecosystems. Several levels of subsidization are tested, in relation to the gradual covering (from 0 to 100% with a 20% pitch) of the cost per hectare of lowland ecosystem conversion to rice production</td></tr></tbody></table>

## 3 Results and discussion

The scenario for the subsidization of rice plantation in lowland ecosystems seems to be the most relevant in the current context of Sierra Leone (Fig. 1). In fact, only this scenario has induced a significant rise in rice consumption to reach the same level as that of the two bordering countries: Guinea and Liberia, but far away from the consumption planning hoped for by the National Sustainable Agriculture Development Plan 2010-2030 (fig. 1). In terms of calories, the Lowland<sub>intensive</sub> households alone are at the FAO's recommended minimum level but far from Guinea which shows an average number of calorie intakes of 2500 capita/day (fig. 1). The other households, especially extensive ones, show very low caloric intakes. Similarly, regardless of the type of household, the scenarios have only had a slight effect on the number of calories consumed per day and per capita. This study also highlights the fact that lowland-based households with low intensification means (Lowland<sub>extensive</sub>) and rice consumed per capita barely respond to the scenarios.

<figure><img src="image_69.png" /><figcaption>**Fig. 1.** Variation of rice consumption (a) and total calorie (b) for the 4 farm types, Guinea (average national values), Liberia (average national values) and the targets set by the Sierra Leone Government for the National Agricultural Strategy (2010-2030) and the FAO organization.</figcaption></figure>

## 4 Conclusion

The scenario that contemplated subsidizing rice plantation in lowland ecosystems has been identified as the most efficient, which is relatively coherent with the numerous initiatives that contemplated this type of investment (SLIEPA, 2012). Nevertheless, this scenario has also generated a rice-cash crop specialization (data not shown) which could raise questions as to the resilience of such systems. Besides, the effects of these scenarios remain below the expectations of public authorities. The structure of the current model based on the notion of activity would make it possible without any major changes to test other scenarios strongly contemplated by several initiatives such as the introduction of irrigation, of fertilization, and intensification of others food crops.

## References

* Chen J., et al. 2011. Quantifying economically and ecologically optimum nitrogen rates for rice production in south-eastern China. *Agriculture, Ecosystems & Environment*, vol. 142, n. 3-4, p. 195-204.

* Chenoune et al., 2014. What levers to promote rice production and consumption in Sierra Leone? From characterization to simulation of rice farming households' performance. *PhD Thesis*. 169 pp.

* Herrero M., et al. 2012. Sierra Leone investment outreach campaign: Opportunities for investors in the oil palm sector. *Sierra Leone Investment and Export Promotion Agency*. 40 p.
# T5. Design Climate Smart Agricultural Systems: methods, scales, results and challenges

Chair: Olaf Christen, Agronomy and Organic Farming University of Halle

Co-chair: Jacques-Eric Bergez, INRA
# Farm4Prophet - Managing whole farm business risk in Australia

Harm van Rees *<fnref n="1" />, Tim Hutchings <fnref n="2" />, Zvi Hochman <fnref n="3" />, Tim McClelland <fnref n="1" /> & Robbie Scott <fnref n="4" />

## 1 Introduction

Australia's rain-fed mixed farming region (49M ha) has a highly variable climate, which results in a level of financial risk three to ten times higher than that experienced by its major competitors (OECD-FAO, 2011). Australian farmers receive minimal Government support and businesses thrive or fail depending on their ability to manage risk. There are currently few tools which enable farmers and other stakeholders to quantify the risks resulting from a strategic change in management. Farm4Prophet is a new whole-farm business management tool developed by the BCG (Birchip Cropping Group) in association with leading farmers, CSIRO and farm business consultants. Long term pasture and crop production modelling, using APSIM (Holzworth et al., 2014), is integrated with long-term, whole farm financial projections. This enables users to assess production and financial performance and run scenario analysis to predict impacts on profitability from changes in farm management practices.

## 2 Method

Farm4Prophet uses the SMA (Sequential Multi-variable Analysis) methodology developed by Hutchings and Nordblom (2011), which prepares risk profiles for individual farms derived from multiple iterations of long-term, whole-farm financial simulations. Farm4Prophet is an extension of this concept. It is being developed as a web-based product with three functional components:

1. APSIM is used to prepare crop and pasture production simulations for a period of up to 50 years, based on long-term climate and soil data for any individual farm and enterprise combination.

2. A development of the Farmplan Budget Planner software enables farm specific financial measures to be calculated (eg. Cashflow, Profit&Loss, Balance sheets).

3. A Monte-Carlo routine, randomly selects sequences of production data for a chosen period from the 50 year APSIM production data for each commodity. The routine is then used to build cumulative probability functions (CDFs) based on the cash margins (closing minus opening financial balance) for each iteration.

This process allows the user to test the long-term effects on cashflow of various management scenarios. Cashflow was chosen as the benchmark, for two reasons. First, cashflow is the only common indicator which contains all costs. Secondly, the change in the net cash balance drives the change in the farm debt, which often constrains both the ability and the desire to invest in growth (Bamberry et al., 1997).

## 3 Results

A prototype model has been used as proof of concept and a range of outputs have been prepared and verified on a farm near Wagga Wagga in the Southwest Slopes region of New South Wales in Australia (Lat -34.817, Long 147.198, Alt 247m). This owner-operated, 1,000 ha farm is typical for the region. The farming system is based on 5 years of annual crops - canola /wheat /barley /canola

/wheat under-sown with lucerne/clover pasture, followed by four years pasture, grazed by a self-replacing flock of Merino sheep for prime lamb production. The farm has a current financial equity of 80%. Under the existing management system with current prices the farm is currently not viable, having an 85% probability of operating at a loss (Fig. 1).

Farm4Prophet was used to test alternative management scenarios on profitability, such as leasing an additional 500 ha of adjoining farmland, where the farming practice on the leased land was either (i) maintained with the current rotation and grazing practices; or (ii) the additional land was continuously cropped (no grazing). In each case the model adjusts the variable costs, the variable components of fixed costs (labour, fuel, maintenance and rental) and the capital costs (additional sheep and machinery) for the increasing scale.

Scenario (i): the additional 500ha maintained in the current farming system reduces the risk of loss to 62%, which is a considerable improvement, but still unlikely to be viable.

Scenario (ii): the additional 500ha of cropping only increased the percentage cropped from 60% to 73%. All cost components were adjusted for this change, which reduced the risk of loss to less than 30%, indicating that this is likely to be a viable strategy.

<footnote n="1">¹ BCG (Birchip Cropping Group)</footnote>

<footnote n="2">² Farm Business consultant, Meridian Agriculture</footnote>

<footnote n="3">³ CSIRO</footnote>

<footnote n="4">⁴ Farmplan</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: harm@cropfacts.com.au</footnote>
<figure><img src="image_70.png" /></figure>

## 4 Conclusion

Farm4Prophet sample analysis contains all the complex interactions between farm enterprises at the production, commodity prices, costs and management levels. This complexity is increased by the inclusion of time in the analysis, each iteration is run for a range of historical climate, prices and costs, and includes between-year, as well as within-year interactions. Factors such as nitrogen balance, supplementary feed requirements, and financial and management constraints are modelled, costed and included and compounded over the period.

## References

* Bamberry, G., Dunn, A. & Lamont, A. (1997). Farmer Education and Good Management. Canberra: RIRDC.

* Holzworth et al. (2014). APSIM - evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software 62: 327–350

* Hutchings, T. & Nordblom, T. (2011). A financial analysis of the effect of the mix of crop and sheep enterprises on the risk profile of dryland farms in south-eastern Australia. Australian Journal of Farm Business Management, 8: 19-41.

* OECD-FAO. (2011). Agricultural Outlook 2011.
# Specification of nitrogen use in regional climate impact assessment studies

Heidi Webber *±1, Gang Zhao <fnref n="1" />, Wolfgang Britz <fnref n="2" />, Wim de Vries <fnref n="3" />, Joost Wolf <fnref n="4" />, Thomas Gaiser <fnref n="1" />, Holger Hoffmann <fnref n="1" /> & Frank Ewert <fnref n="1" />

## 1 Introduction

Understanding how crop yields will be impacted by climate change is facilitated by the use of process based crop models. However there is little systematic knowledge across environments about how management (e.g. nitrogen use) interacts with climate change effects to impact crop yields. Specification of management is difficult in large area impact assessment studies due to a lack of data and the need to make assumptions regarding future management. This study investigated whether it is required to include nitrogen limitation in regional climate change impact assessments for Europe.

## 2 Materials and Methods

Water limited and nitrogen-water limited relative yield changes between 2004 and 2050 for SRES scenarios A1B1, B1 and B2 were simulated with SIMPLACE<Lintul5, DRUNIR> (Gaiser et al., 2013) for spring-sown grain maize, silage maize, potato and sugar beet, as well as for winter-sown barley, oilseed rape and wheat across the EU27. Historical yields and nitrogen inputs for NUTSII administrative regions were from the CAPRI database (Fig. 1a). Future nitrogen fertilizer rates were calculated from the fertilization in 2004 multiplied by a factor of how water-limited yields are expected to change to 2050. Climate and soil data were at the level of the simulation units defined by the spatial intersection of eleven environmental zones (Metzger et al., 2005) and NUTSII boundaries (Fig. 1b). Simulations in the baseline period (1982–2006) sometimes had a significant trend due to the use of historical nitrogen inputs and were regressed to obtain yields in 2004. Yields in 2050 are the average yield in the respective scenario period (2041-2064).

<figure><img src="image_71.png" /><figcaption>**Fig. 1.** Observed yields and nitrogen fertilizer rates are from the CAPRI database at the level of (a) NUTSII (n=240). The (b) model simulation units (n=534) are the spatial intersection of the NUTSII zones with environmental zones (Webber *et al.*, in review)</figcaption></figure>

## 3 Results - Discussion

Water-limited and nitrogen-water limited relative yields changes to 2050 are largely similar for spring sown crops but differ for winter sown crops, being more negative when nitrogen limitation is considered (Fig. 2). For the winter crops, particularly in Southern Europe, water stress also results in lower nitrogen rates. The longer growing conditions in the autumn, without accelerated development, lead to more autumn nitrogen use with the result of increased nitrogen stress in the spring. Other options to specify future nitrogen rates (e.g. using farm systems models and including more information on the timing of nitrogen fertilizer) are explored (results not shown).

<footnote n="1">¹ Institute of Crop Science and Resource Conservation (INRES), Katzenburgweg 5, University of Bonn, D-53115 Bonn, Germany</footnote>

<footnote n="2">² Institute for Food and Resource economics, University of Bonn, Nussallee 21, D-53115, Bonn, Germany</footnote>

<footnote n="3">³ Alterra Wageningen UR, P.O. box 47, 6700AA Wageningen, The Netherlands</footnote>

<footnote n="4">⁴ Wageningen University, group Plant Production systems, P.O. Box 430, 6700 AK Wageningen, The Netherlands</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: hwebber@uni-bonn.de</footnote>
<figure><img src="image_72.png" /><figcaption>**Fig. 2.** Agreement between relative yield changes between 2004 and 2050 for water limited (Δ<sub>WL</sub>) and nitrogen-water limited (Δ<sub>NWL</sub>) simulations for the A1B1 scenario across Europe (Webber *et al.*, in review).</figcaption></figure>

**4 Conclusions**

Relative yield changes due to climate change between 2004 and 2050 do not depend on nitrogen limitation for the spring-sown crops simulated in this study. However, for the winter-sown crops, simulated impacts were more negative when nitrogen limitation was considered. These results suggest that additional model complexity (parameterization, input data and assumptions about future use) is not needed for regional scale assessment of climate change impacts for spring-sown crops, but is required for winter-sown crops. Options on how to specify future nitrogen use are presented and the challenges of each explored.

*Acknowledgements*. HW, FE, and TG's contributions were funded by the Federal Ministry of Education and Research (BMBF) through WASCAL (West African Science Service Center on Climate Change and Adapted Land Use). FE also acknowledges support from the German Science Foundation (project EW 119/5-1) and the FACCE JPI MACSUR project (031A103B) through the German Federal Ministry of Education and Research.

**References**

Gaiser, T., Perkons, U., Küpper, P.M., Kautz, T., Uteau-Puschmann, D., Ewert, F., Enders, A., Krauss, G., 2013. Modeling biopore effects on root growth and biomass production on soils with pronounced sub-soil clay accumulation. Ecological modelling 256, 6-15.

Metzger, M., Bunce, R., Jongman, R., Mücher, C., Watkins, J., 2005. A climatic stratification of the environment of Europe. Global Ecology and Biogeography 14, 549-563.
# Re-designing smallholder farming futures for reduced vulnerability to climate change in semi-arid southern Africa

Sabine Homann-Kee Tui<fnref n="1" />, Katrien Descheemaeker*±,<fnref n="2" />, Patricia Masikati<fnref n="3" />, Arthur Chibwana Gama<fnref n="4" />, Olivier Crespo<fnref n="5" />, Lieven Claessens<fnref n="6" /> & Roberto Valdivia<fnref n="7" />

## 1 Introduction

Climate change will impact the productivity of maize-based crop-livestock systems and the food security of smallholders depending on them in semi-arid southern Africa. Earlier results from testing climate change adaptation options showed that incremental improvements in fertilizer application rates, use of adapted maize cultivars or introduction of forage production are insufficient for substantial improvement of smallholder livelihoods (Masikati et al., 2015). In this paper we therefore explored effects of more transformative system re-design on households' vulnerability to climate change, farm net returns and poverty rates. We tested the hypothesis that packages tailored to specific farm situations are more effective than blanket recommendations.

## 2 Materials and Methods

The study was carried out in the Nkayi district of semi-arid Zimbabwe, characterized by average annual rainfall of 650 mm with high interannual variability. Climate change projections agree on an increase in temperature of 2 to 3.5 °C, whereas future rainfall conditions are less certain. Apart from low and erratic rainfall, the poor soil fertility status and limited agricultural input use result in low productivity of the predominantly maize-cattle based systems, with average maize yields below 0.7 t ha⁻<fnref n="1" />, high mortality rates up to 15%, and low milk yields. According to national statistics 76% of all rural households in Zimbabwe are poor and more than 22% are extremely poor (ZimVAC, 2013). Food self-sufficiency varies from 3 to 10 months depending on the annual rainfall, leaving rural households extremely vulnerable to the adverse effects of climate change. Heterogeneity in the farming community is high and three farm types are distinguished, (1) extremely poor households with no cattle, cultivating 1.3 ha on average, (2) poor households with 0 to 8 cattle, cultivating 1.8 ha, (3) better-off households with more than 8 cattle, cultivating 2.5 ha.

We followed the AgMIP Regional Integrated Assessment procedure (Antle et al., 2015) using (i) a multi-modeling framework of climate, crop, livestock and economic simulation models, and (ii) representative agricultural pathways (RAPs) generated with stakeholders to define plausible future socio-economic conditions. System re-design for the three farm types (Table 1) followed the assumption that improved access to inputs, knowledge and markets encourages smallholders to intensify agricultural production, making full use of the cultivated land areas, diversifying production of crops (grain and forage legumes), enhancing mineral and organic fertilizer use and improving livestock management and marketing. Three transformative packages were designed and effects evaluated across the entire farm population. Based on this, a set of tailored packages per farm type was developed to maximize net returns.

<table><caption>Table 1. Transformative packages (Tr) as compared to the current situation for three farm types in Nkayi</caption><thead><tr><th rowspan="2"></th><th colspan="2">0 cattle</th><th colspan="2">0-8 cattle</th><th colspan="2">&gt; 8 cattle</th></tr><tr><th>Current</th><th>Tr 1</th><th>Current</th><th>Tr 2</th><th>Current</th><th>Tr 3</th></tr></thead><tbody><tr><td>Maize (% of cropland)</td><td>75</td><td>25</td><td>70</td><td>25</td><td>70</td><td>20</td></tr><tr><td>Sorghum (% of cropland)</td><td>11</td><td>12.5</td><td>11</td><td>8</td><td>16</td><td>5</td></tr><tr><td>Groundnuts (% of cropland)</td><td>10</td><td>37.5</td><td>10</td><td>33</td><td>10</td><td>25</td></tr><tr><td>Common beans (% of cropland)</td><td></td><td>25</td><td></td><td></td><td></td><td></td></tr><tr><td>Mucuna (% of cropland)</td><td></td><td></td><td></td><td>33</td><td></td><td>25</td></tr><tr><td>Banagrass (% of cropland)</td><td></td><td></td><td></td><td></td><td></td><td>25</td></tr><tr><td>Cereal fertilizer use (kg N / ha)</td><td>3</td><td>20</td><td>3</td><td>20</td><td>3</td><td>20</td></tr><tr><td>Offtake rate (%), cattle/small ruminants</td><td>0 / 17</td><td>0 / 50</td><td>4 / 12</td><td>10 / 50</td><td>7 / 10</td><td>20 / 50</td></tr><tr><td>Milk production (l/day/cow)</td><td>n.a.</td><td>n.a.</td><td>0.8</td><td>1.5</td><td>1.3</td><td>2</td></tr></tbody></table>

<footnote n="1">¹ International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), P O Box 776, Matopos, Bulawayo, Zimbabwe</footnote>

<footnote n="2">² Plant Production Systems, Wageningen University, PO Box 430, 6700 AK Wageningen, The Netherlands</footnote>

<footnote n="3">³ World Agroforestry Centre (ICRAF) Lusaka, Zambia</footnote>

<footnote n="4">⁴ Lilongwe University of Agriculture and Natural Resources, P.O. Box 21,9 Lilongwe, Malawi</footnote>

<footnote n="5">⁵ Climate System Analysis Group, Environmental and Geographical Science Dept., University of Cape Town, Rondebosch, South Africa</footnote>

<footnote n="6">⁶ International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), P.O. Box 39063, 00623 Nairobi, Kenya</footnote>

<footnote n="7">⁷ Department of Applied Economics, Oregon State University</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: katrien.descheemaeker@wur.nl</footnote>
## 3 Results - Discussion

Stakeholders RAPs projected an optimistic future mid-term (2050s) scenario (called RAP1) towards positive economic development, Zimbabwe stepping out of the crisis and massive investments in market-oriented agriculture. Among key drivers they estimated productivity growth rates for maize (40%) and other crops (35%), cattle (30%) and other ruminants (25%). Producer price growth was expected to be limited to 5-20% for the various agricultural commodities. A step further, following improved availability of inputs and markets, farmers would be motivated to increase their cultivated land by 60%; poor farmers would at least double flocks of small ruminants (called RAP 2). Within that socio-economic context a dry climate change scenario would expose about 60% of the farming households to greater vulnerability. The very poor will loose up to 9% of their net returns, others will loose a smaller proportion.

<figure><img src="image_73.png" /><figcaption>**Fig. 1.** Effects of incremental (Incr) and transformative (Tr) adaptations and RAPs on the percentage gainers from adapting (a), increase in net returns (b) and farmers below the poverty line (c) (packages 1, 2 and 3 are explained in Table 1; Tailored package (Tail) is a combination based on highest net returns).</figcaption></figure>

The adaptation packages are likely to reduce vulnerability to climate change for most of the very poor farms. The incremental change will possibly benefit almost all very poor and more than 60% of the less poor (Figure 1a). It will increase their net returns by 50% as compared to < 20% for the less poor (Figure 1b). The magnitude of the benefits will however be small, less than 100 and 500 US$/farm for the very poor and less poor respectively. In comparison, drastic diversification of crop production along with greater participation in livestock markets will lift more than 85% of all farm types to a better economic situation (Figure 1a). The drastic adaptation packages will more than double the net returns of the very poor, and increase those of the poor and better off by 50 to 75% (Figure 1b). In absolute terms those with large cattle herds will benefit more, with on average 1300 US$ higher net returns, as compared to 500 US$ for the very poor, but they also face higher risk. Tailoring the technology packages to farm types increases the net returns for the entire community by 86%, as compared to 72% if technology packages are applied across the entire population as a blanket. Poverty levels might however remain high, even after drastic economic changes and tailored investments, (Figure 1c). Most substantial change in poverty rates will be for those with large cattle herds. The drastic adaptation package will reduce the proportion of these farms below the poverty line to around 35%, as compared to 65% under incremental change. It will reduce poverty levels among those with small cattle herds to 75%. Households without livestock, about 43% of the farm population, will have very little chances to move out of poverty.

## 4 Conclusions

This research illustrates that tailored systems diversification and market orientation can substantially increase farm production, food security and net returns from agriculture. Greater impact on poverty however requires further steps on multiple component innovations, better synchronizing of technologies, markets, policies, on-and off-farm investmens, triggering transformative system change (Geels and Schott, 2007). Incorporating the influence of socio-economic development and institutional and policy improvements defined in the RAPs enabled the comparison with purely climate change effects and is a first step to inform adaptation strategies at farm and larger scales. This approach should be taken further with policy and decision makers, to adjust socio-economic and institutional conditions that would make investments in farming more attractive while considering risk, essential to reduce vulnerability to climate change and enable sustainable futures for smallholders in semi-arid southern Africa.

*Acknowledgements.* This work formed part of the AGMIP (Agricultural Model Inter-Comparison and Improvement Project) regional project, funded by DFID.

## References

* Antle, J.M., Valdivia, R.O., Boote, K.J., Janssen, S., Jones, J.W., Porter, C.H., Rosenzweig, C., Ruane, A.C. & Thorburn, P.J. (2015). AgMIP's Transdisciplinary Agricultural Systems Approach to Regional Integrated Assessment of Climate Impacts, Vulnerability, and Adaptation. In: Rosenzweig and Hillel (Eds), Handbook of Climate Change and Agroecosystems, The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments — Joint Publication with American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, pp. 27–44.

* Geels, F. W., Schot, J. (2007). Typology of sociotechnical transition pathways Research Policy. 36: 3, 399–417

* Masikati, P., Homann-Kee Tui, S., Descheemaeker, K., Crespo, O., Walker, S., Lennard, C.J., Claessens, L., Gama, A.C., Famba, S., van Rooyen, A.F. & Valdivia, R.O. (2015). Crop-Livestock Intensification in the Face of Climate Change: Exploring Opportunities to Reduce Risk and Increase Resilience in Southern Africa by Using an Integrated Multi-modeling Approach. In: Rosenzweig and Hillel (Eds), Handbook of Climate Change and Agroecosystems, The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments — Joint Publication with American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, pp. 159–200

* ZimVAC (2013). Rural livelihoods assessment, Harare
# Resilience of adaptation strategies for small-holder farmers in Cambodian lowland rice ecosystems in response to future climate uncertainty.

Perry L. Poulton *±<fnref n="1" />*, Neal P. Dalgliesh <fnref n="1" />, Seng Vang <fnref n="2" />, Touch Veasna <fnref n="2" /> & Philip Charlesworth <fnref n="3" />

## 1 Introduction

Food security in the face of population growth, urban migration, loss of agricultural land and climate change is a major challenge for Cambodian small holder farmers reliant on rainfed rice-based agriculture. However, by 2050 climate change is predicted to reduce the national rainfed rice cultivation area by 20%. Replacing a traditional low input transplanted rice crop with a 'response farming' approach (Dalgliesh et al., 2015 this edition) has potential to not only mitigate effects of current seasonal variability but enable farmers to transition their farming practices in response to future climate uncertainty. This approach is based on utilising improved, shorter duration varieties, more efficient establishment methods and better agronomic management responsive to timing, intensity and longevity of the monsoon. Well-tested biophysical models have been successfully employed in investigating impacts of projected climate change on food production at global (Rosenzweig and Parry, 1994), regional and country (Ruane et al., 2013) scales and are a cost effective method for evaluating possible adaptation strategies available to farmers in managing current and future climatic risk. The APSIM-Oryza model (Gaydon et al., 2012) has been calibrated and validated for transplanted and direct seeded rice for current climatic conditions (Poulton et al. 2015). This analysis investigates the risk from projected climate change at the IPCCs 2060 time horizon, on rainfed and irrigated rice production in Southern Cambodia.

## 2 Materials and Methods

Baseline climate was generated from local observations (1997-2011) and downscaled GCM data using a 'Linear Mixed-Effect State-Space' model applied to produce effective point scale projections suitable for use in bio-physical modelling (Kokic et al., 2011). APSIM-Oryza was parameterised for a short duration rainfed rice crop and run for incremental changes to CO2 (380-830 ppmv), ambient temperature (28.06-34.06 °C), rainfall (-20% +20%) and in combination of all three climatic factors to evaluate model response for a given set of input parameters. A multi-factor sensitivity analysis was then applied to assess potential variation from current baseline yields for IPCC projected scenarios of 0.7-2.7 °C change in ambient air temperature and a 31.0-64.0% increase in atmospheric CO2 to 2060 for the Cambodian region. Three scenarios compared traditional transplant (MSTR) with a 'response' farming approach using a modern rice variety grown under rainfed (SSDR) and irrigated (SDDI) conditions for a combination of temperature and CO2 within the ranges previously described and ±15% change in annual rainfall. Results are presented as a series of probability of exceedence graphs that highlight both the probability of experiencing a failed crop (< 0.4 t ha⁻<fnref n="1" />) as defined by -100% change on the x axis and as the difference in rice yield from the baseline yield (defined as 0% on the x axis) as a percentage change (%). Climate scenarios projected to 2020, 2040 and 2060 are presented (Fig.1).

## 3 Results and Discussion

Model response to elevated CO2 of 680 ppm for current temperature and rainfall is consistent with the accepted physiological effects of CO2 on C3 crops, simulating a 17.5% yield increase above baseline levels. Sensibility testing of the model to incremental changes to CO2 and temperature, results in a yield response comparable with Li et al. (2014), for 13 rice models, reporting a 7% to 11% (APSIM 6.11%) increase per 100 µmol mol⁻<fnref n="1" /> for the 380-480 ppm CO2 range and 3% to 5% (APSIM 4.13%) for the 630-730 ppm range and a reduction in yield of 2% to 11% (APSIM 4%) per degree C increase above the baseline temperature. Simulated long-term mean yields of 3.65 t ha⁻<fnref n="1" /> (MSTR), 4.64 t ha⁻<fnref n="1" /> (SSDR) and 5.16 t ha⁻<fnref n="1" /> (SDDI) for 380 ppm CO2 and 1997-2011 temperature and rainfall are the basis for directly comparing the resilience of the selected management scenarios for projected increases in temperature and CO2 (2020-2060) and changes in annual rainfall of -15% (a), 0% (b), +15% (c). Baseline yields are comparable with on-farm yields observed by Dalgliesh et al., (2015 this edition) in 3 years of experimentation. For the 15% decline in annual rainfall

<footnote n="1">¹ CSIRO Agriculture Flagship, Toowoomba, Qld, Australia;</footnote>

<footnote n="2">² CARDI Cambodian Agricultural Research and Development Institute, Phnom Penh, Cambodia;</footnote>

<footnote n="3">³ iDE Phnom Penh, Cambodia</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: perry.poulton@csiro.au</footnote>
example, for 2020 (Fig. 1 MSTR (a)), the probability of a crop failure occurs in 39% of years and increases to 42% by 2060. Years exceeding baseline yields declined from 24% (2020) to 15% (2060). In comparison, for a 15% increase in annual rainfall in 2020 (Fig. 1 SSDR (c)), the probability of crop failure occurs in only 9% of years. For this scenario baseline yields are exceeded in 55% of years but decline to 42% by 2060. While crop failure remained relatively static within each rainfall scenario, yields fell in response to the combination of higher temperature and CO2, particularly with declining rainfall. A 15% increase in rainfall and elevated CO2 partially offset the effect of increased temperature on crop development whereas a reduction in rainfall of 15% results in a 40% yield penalty.

<figure><img src="image_74.png" /><figcaption>**Fig. 1.** Probability of exceeding baseline yields (0% on X axis) for current ambient air temperature and 380 ppm CO2, for a rainfed transplanted single medium variety (MSTR), rainfed single direct-seeded short variety (SSDR) and irrigated double cropped direct-seed short variety (SDDI) in response to projected increase in temperature and CO2 and for a changes in annual rainfall of (a) -15%, (b) 0%, (c) +15% for 2020, 2040, 2060.</figcaption></figure>

Access to irrigation mitigates the effect of climate extremes, reducing risk of crop failure and improving yields for all rainfall scenarios in 60% to 85% of years (Fig. 1. SDDI a-c). Except for the wetter climate scenario (+15%) where additional rainfall offsets the irrigation required to maintain current yields until 2040, a significant investment in irrigation infrastructure or improved water use efficiency will be required to maintain existing rainfed yields in response to increasing temperature by 2060. In comparison, use of supplementary irrigation early in the season has been shown to support successful early crop establishment and development during the critical growing period and delivers the opportunity for a second crop and therefore higher on-farm returns. For a 15.7% increase in CO2 by 2030, early established modern rice varieties and improved N fertiliser management can double production compared with traditional systems utilising low input late maturing local varieties.

## 4 Conclusions

The response of APSIM-Oryza to elevated temperature and CO2 is consistent with measured physiological CO2 effects on C3 crop yields and is comparable with results from similar crop modelling studies. For Cambodian small-holder farmers, traditional rainfed rice production beyond 2030 is at risk from increased variability in the distribution and timing of rainfall. The long-term resilience of a 'response farming' approach in managing seasonal variability is demonstrated by a reduction in crop failures when compared with traditional transplanted practice. Adoption of direct seeding of higher yielding, quicker maturing rice varieties; access to mechanical harvesting; improved nitrogen management; and use of supplementary irrigation at sowing and/or better utilisation of available water, all support early crop establishment and deliver farmers additional strategies for maintaining and potentially increasing rice production in response to future climate uncertainty.

*Acknowledgements.* We acknowledge the financial support of Australian Centre for International Agricultural Research (ACIAR).

## References

Dalgliesh, N.P., Poulton, P.L., Charlesworth, P. & Le.Non, L. (2015). Farming systems to support Flexible climate resonance strategies for Cambodian smallholder farmers. 5th International Symposium for Farming System Design. 7-10 Sep. 2015, Montpellier, France.

Gaydon, D. S., Probert, M. E., Buresh, R. J., Meinke, H., Suriadi, a., Dobermann, a., Bouman, B., et al. (2012). Rice in cropping systems—Modelling transitions between flooded and non-flooded soil environments. *European Journal of Agronomy*, 39:9-24.

Kokic, P., Crimp, S. and Howden, M. (2011). Forecasting climate variables using a mixed-effect state-space model. *Envirometrics*. 22: 409–419.

Li, T., T. Hasegawa, X. Yin, Y. Zhu, K. Boote, M. Adam, S. Bregaglio, S. Buis, R. Confalonieri, T. Fumoto, D. Gaydon, M. Marcaida, H. Nakagawa, P. Oriol, A.C. Ruane, F. Ruget, B. Singh, U. Singh, L. Tang, F. Tao, P. Wilkens, H. Yoshida, Z. Zhang, and B. Bouman. 2014. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. *Glob. Chang. Biol*. http://dx.doi.org/10.1111/gcb.12758.

Poulton, P.L., Vesna, T., Dalgliesh, N.P and Seng, V. (2015). Applying simulation to improved rice varieties in reducing the on-farm yield gap in Cambodian lowland rice ecosystems. *Experimental Agric. J.* 51(2) 264-284.

Rosenzweig, C. and Parry, M.L. (1994). Potential impact of climate change on world food supply. *NATURE*. 367. 13 Jan 1994.

Ruane, A.C., D.C. Major, W.H. Yu, M. Alam, S.G. Hussain, A.S. Khan, A. Hassan, B.M.T. Al Hossain, R. Goldberg, R.M. Horton, and C. Rosenzweig. (2013). Multi-factor impact analysis of agricultural production in Bangladesh with climate change. *Glob. Environ. Chang.* 23(1): 338–350.
# Assessing impacts of changes in farm management and farm structural change on sustainable development in a rural area in the Netherlands

Pytrik Reidsma *±<fnref n="1" />, Martha M. Bakker <fnref n="2" />, Argyris Kanellopoulos <fnref n="1" />,<fnref n="3" />, Shah J. Alam <fnref n="4" />, Wim Paas <fnref n="1" />, Johannes Kros <fnref n="5" />& Wim de Vries <fnref n="5" />,<fnref n="6" />

## 1 Introduction

Changes in climate, technology, policy and prices affect agricultural and rural development. Policy makers and stakeholders involved in landscape planning require impact assessments that integrate economic, social, and environmental dimensions of land use dynamics. Therefor, there is a need for integrated assessments considering multiple drivers of change and multiple impacts. The aim of this paper is to show such an application by performing an integrated assessment of impacts of climate change in concert with socio-economic change on sustainable development of the Baakse Beek catchment in the east of the Netherlands.

## 2 Materials and Methods

A bio-economic farm model (FSSIM; Kanellopoulos et al., 2014), an agent-based land-use change model (RULEX; Bakker et al., 2014) and a regional emission model (INITIATOR; Kros et al., 2014) have been used to simulate economic, social and environmental indicators at both farm and landscape level. While the bio-economic farm model can assess detailed farm management and resulting agricultural outputs, the spatially explicit agent-based land-use model allows to assess farm structural changes and interactions with nature. The regional emission model allows to assess environmental impacts in a spatially explicit way.

Climate change affects crop yields, which were simulated with the crop models WOFOST and LINGRA and the semi-quantitative participatory method Agro Climate Calender (Schaap et al. 2013). Impacts of technology change on crop and milk yields were based on historical analysis, extrapolation and agronomic knowledge. The market model CAPRI was used to assess changes in product prices. All these changes were provided as input to FSSIM to simulate changes in farm plans and management. The resulting gross margins were used as one of the inputs in RULEX to simulate land use change. Changes in crop areas, crop yields and animal numbers as simulated with FSSIM were used as input in INITIATOR to simulate environmental emissions.

Impacts were assessed for 12 indicators (Table 1) for the year 2050 and for four scenarios: Global Economy (GE) and Regional Communities (RC) with changes in climate, technology, policies & prices (further denoted as full GE and RC scenario), and considering climate change alone (GE CC, RC CC).

<table><caption>Table 1. Selected indicators in the economic, social and environmental dimensions.</caption><thead><tr><td>Dimension</td><td>Indicator</td><td>Unit</td><td>Current</td><td>Sign of impact</td></tr></thead><tbody><tr><td rowspan="4">Economic</td><td>dairy farm income</td><td>euro/farm/year</td><td>98000</td><td>+</td></tr><tr><td>arable farm income</td><td>euro/farm/year</td><td>40854</td><td>+</td></tr><tr><td>dairy output/input</td><td>euro/euro</td><td>1.90</td><td>+</td></tr><tr><td>milk production</td><td>ton/year</td><td>333491</td><td>+</td></tr><tr><td rowspan="4">Social</td><td>farm size</td><td>ha</td><td>22.5</td><td>-</td></tr><tr><td>number of farmers</td><td>#</td><td>1349</td><td>+</td></tr><tr><td>nature area</td><td>ha</td><td>1731</td><td>+</td></tr><tr><td>landscape diversity</td><td>Shannon-Weaver index (-)</td><td>0.78</td><td>+</td></tr><tr><td rowspan="4">Environmental</td><td>global warming</td><td>kg CO2eq/ha/year</td><td>13510</td><td>-</td></tr><tr><td>terrestrial eutrophication</td><td>NH3 emission/ha/year (in kg N)</td><td>65</td><td>-</td></tr><tr><td>aquatic eutrophication P</td><td>kg P/ha/year run off</td><td>1.9</td><td>-</td></tr><tr><td>aquatic eutrophication N</td><td>NO3 leaching/ha/year (in kg N)</td><td>15.5</td><td>-</td></tr></tbody></table>

<footnote n="1">¹ Plant Production Systems Group, Wageningen University, the Netherlands</footnote>

<footnote n="2">² Land Use Planning Group, Wageningen University, the Netherlands</footnote>

<footnote n="3">³ Operational Research and Logistics Group, Wageningen University, the Netherlands</footnote>

<footnote n="4">⁴ School of Science & Engineering, Habib University, Pakistan</footnote>

<footnote n="5">⁵ Alterra, Wageningen UR, the Netherlands.</footnote>

<footnote n="6">⁶ Environmental Systems Analysis Group, Wageningen University, the Netherlands</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: pytrik.reidsma@wur.nl</footnote>
## 3 Results – Discussion

Results show that in the Baakse Beek area, climate change alone will have mainly negative economic impacts in the more extreme ‘global economy’ climate change scenario (GE CC), while impacts are slightly positive in the moderate climate change scenario ‘regional communities’ (RC CC) (Fig. 1). Conversely, changes in technology, prices and policy are projected to have a positive economic impact in the full GE scenario, more than offsetting the negative climate impacts. Important is however that their social and environmental impacts are largely negative. In the full RC scenario, the average dairy farm income in particular is negatively affected. Social impacts are similarly negative, while environmental impacts are less severe compared to the GE scenario.

<figure><img src="image_75.png" /><figcaption>**Fig. 1.** Impact of the scenarios on 12 indicators in the Baakse Beek, in % change compared to the current values (average/total). Note that an increase is considered positive for sustainable development; signs of the values have been changed depending on perceived impact.</figcaption></figure>

Focusing specifically on farm structural change, we observe that dairy farms further increase in number (Fig. 2). The number of medium sized farms (M) decreases, and the number of large (L) and very large farms (XL) increases. Although land use change patterns are similar in both scenarios, it is interesting to observe that in RC the increase in very large farms is larger than in GE. This may be due to the lower income increase, causing more medium farms willing to sell their land to larger farms. In GE more farms may want to buy land, but because of competition this is not possible.

<figure><img src="image_76.png" /><figcaption>**Fig. 2.** Current land use (a) and projected farm structural change (b) based on land use type and size category, in the full GE and RC scenarios.</figcaption></figure>

## 4 Conclusions

Our results suggest that integrated assessments at farm and landscape level can be used to guide decision-makers in spatial planning policies and climate change adaptation. As there will always be trade-offs between economic, social and environmental impacts, stakeholders need to interact and decide upon most important directions for policies and measures.

## References

* Bakker, M.M., Alam, S.J., van Dijk, J., Rounsevell, M.D.A. (2014). Land-use change arising from rural land exchange: an agent-based simulation model. *Landscape Ecology*, **30**, 273–286.

* Kanellopoulos, A., Reidsma, P., Wolf, J., van Ittersum, M.K. (2014). Assessing climate change and associated socio-economic scenarios for arable farming in the Netherlands: An application of benchmarking and bio-economic farm modelling. *European Journal of Agronomy*, **52**, 69-80.

* Kros, J., Bakker, M.M., Reidsma, P., Kanellopoulos, A., Alam, S.J., de Vries, W. (2014). Impacts of agricultural changes in response to climate and socio-economic change on nitrogen deposition in nature reserves. *Landscape Ecology*, DOI: 10.1007/s10980-014-0131-y.

* Schaap, B.F., Reidsma, P., Verhagen, J., Wolf, J., van Ittersum, M.K. (2013). Participatory design of farm level adaptation to climate risks in an arable region in The Netherlands. *European Journal of Agronomy*, **48**, 30-42.
# POTENTIAL IMPACTS OF CLIMATE CHANGE ON LEGUME-BASED CROPPING SYSTEMS

Ralf Bloch *±1 & Johann Bachinger

## 1 Introduction

In organic farming systems (OFS) legume-grass swards (LGS) are systemically relevant, first, because of the symbiotic N₂-fixation, and second, because it is the basis of the forage production. LGS shows a high susceptibility to climate change, due to the high water demand. Therefore LGS may become the major weakness in legume-based cropping systems. Particularly, dry regions with low precipitation and sandy soils are under threat of decreasing forage production and thus lower N supply through symbiotic N₂-fixation. In Germany, in particular, the federal state of Brandenburg, which presently already has low precipitation and where pre-summer droughts are common, the production of LGS is endangered. These effects are particularly relevant for the large-scale organic farms on sandy soils, which are prevalent in Brandenburg and covers approximately 10% of total agricultural area (Reyer *et al.*, 2012). As such, a modelling approach was developed in order to assess the potential impacts of climate change on organic farming in Brandenburg. With it the yield performance of LGS was modelled for two study regions (Uckermark and Spreewald). In both study regions 30% of the total agricultural area is organic but they differ in their site characteristics. In the Spreewald region (SP) the average annual temperature is 9.5 °C and average annual precipitation is 551 mm whereas in the Uckermark region (UM) the average annual temperature and average annual precipitation is 8.5 °C and 517 mm respectively. The climatic water balance (CWB) is approximately the same (SP: -143 mm; UM: -147 mm). The sandy soils in SP are only suitable for red clover grass (RCG) whereas in UM lime rich sandy loams allows for the cultivation of alfalfa clover grass, which needs pH values above 6.0. In contrast, red clover alfalfa is characterised by a higher drought tolerance (Bloch, 2015). For both regions the following hypotheses were examined:

1. Caused by a warmer climate the growing period will be extended. This can be used in more productive way for the cultivation of LGS. Until the end of the 21st century the average annual yields of LGS will increase.

2. Caused by a warmer and dryer climate the uncertainty of the single cut yields will increase.

## 2 Materials and Methods

Using the yield model for legume-grass swards (Bloch *et al.* 2015) applying historical and projected weather data generated by the regional climate model STARS, single cut dry matter yields were calculated for LGS based on alfalfa (Model A) and red clover (Model B) for the periods 1972–2008 (Past) and 2062–2092 (Future). The calculation is based on the assumptions of the emission scenario RCP 8.5 in which the average annual temperature will increase from 2011 to 2100 by approx. 4 °C. In order to account for the uncertainties in the precipitation development, a wet and a dry scenario were used for the yield assessment (CWB at Wet: SP: -298 mm, UM: -250 mm; CWB at Dry: SP: -361 mm, UM: -377 mm). The dry matter yield calculation for a maximum of four single cuts in this model are based on regression analyses of cumulated actual Evapotranspiration values as described by Bachinger and Reining (2009). The beginning of the growing period and the cutting dates, indicated by the Julian Day Number (JDN), was predicted by using cumulative temperature sums and threshold values for the yield of the third (2 t DM ha⁻<fnref n="1" />) and fourth cut (1 t DM ha⁻<fnref n="1" />). Based on single cut yield data of the extreme drought year, 2003 an algorithm for drought impact assessment was developed. This is algorithm based on the assumption that in contrast to alfalfa LGS (Model A) pure red clover grass stands (Model B) will dry up when, between the second and third cut, 50 modelled drought days (MDD) will occur. For the model calibration long-term data on single cut yields and LGS cutting dates were used from the ZALF experimental station. For the model validation phenological data from the German Weather Service (DWD) and cutting dates from organic farms located in the study regions were used. For more details see Bloch *et al.* (2015).

## 3 Results – Discussion

The results show, that by the end of the 21st century the growing season will begin up to three weeks earlier. The growing period for a productive LGS growth will thus be considerably extended (Fig. 1). In spite of the extended growing season, the LGS annual yields for the period of 2062 to 2092 will remain almost unchanged in both regions, assuming a wet scenario (Table 1). The dry (Dry) scenario in contrast will show declining annual yields in both regions up to 20%. Hypothesis 1 can therefore not be confirmed. The reduction of the annual crop yields is a result of the explicit change of the single cutting dates and yields (Fig. 1). Based on an earlier cutting date for the first cut (up to four

<footnote n="1">¹ Institute of Land Use Systems, Leibniz Centre for Agricultural Landscape Research (ZALF)</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author bloch@zalf.de</footnote>
weeks earlier than in the period of 1972–2008) and at, this point, lower global radiation, the yields for the first cut will be reduced by up to 0.5 TM tha⁻¹. Contrary to this, the yields for the second cut can be higher, if there is sufficient soil water from the winter precipitation (up to 1 DM t ha⁻¹ at the Uckermark location in scenario Wet). Despite the change in the yield level, the yield stability will slightly increase for the first and second cut. The third cut, in contrast, will be in all locations, particularly under the Dry scenario, highly affected by dry periods and thus distinctly unstable. Pure RCG (Tab. 1; model B), cultivated on sandy soils in the Spreewald region, are especially vulnerable due to their lower dry tolerance. During the 2062–2092 period there is the threat that pure RCG would dry out every second year. The third cuts impacted by drought stress are characterized in Fig. 2 by i) significantly delayed cutting dates; and ii) only achieving the minimum threshold yield as predefined by the model (see oval symbol). The dry periods during summer also influence the total amount of possible fourth cuts and their yields, showing increased yield fluctuations. Hypothesis II can thus be confirmed for the third and fourth cut.

<table><caption>**Table 1.** Annual dry matter yields (dt ha⁻¹) of alfalfa (model A) and red clover grass (model B) in the regions of Spreewald and Uckermark modelled for the time periods 1978–2008 and 2062–2092 (scenarios Wet and Dry).</caption><thead><tr><th rowspan="2">Study region</th><th rowspan="2">Model</th><th colspan="3">Annual yield (t TM ha<sup>-1</sup>)</th></tr><tr><th>1978–2008</th><th>2062–2092 (Wet)</th><th>2062–2092 (Dry)</th></tr></thead><tbody><tr><td rowspan="2">Spreewald</td><td>A</td><td>x̃<br>σ</td><td>8,8<br>1,7</td><td>8,9<br>1,7</td><td>8,4<br>1,3</td></tr><tr><td>B</td><td>x̃<br>σ</td><td>8,7<br>2,2</td><td>7,3<br>2,5</td><td>6,0<br>1,9</td></tr><tr><td rowspan="2">Uckermark</td><td>A</td><td>x̃<br>σ</td><td>10,3<br>2,0</td><td>10,4<br>1,5</td><td>8,5<br>1,6</td></tr><tr><td>B</td><td>x̃<br>σ</td><td>10,1<br>2,1</td><td>10,2<br>2,3</td><td>7,0<br>2,4</td></tr></tbody></table>

<figure><img src="image_77.png" /><figcaption>**Fig. 1.** Single cut yields and cutting dates modelled for the time periods 1972–2008 (Past) and 2062–2092 (Future). Region: Spreewald; scenario Dry.</figcaption></figure>

## 4 Conclusion

The yield stability and the cultivation of legume-grass swards will become more uncertain in both regions. This could result in a higher risk for the nitrogen supply and fodder production for organic farming in Brandenburg. To adjust to the climate-change, it is recommended to develop drought resistant legume-grass swards for the Brandenburg region.

*Acknowledgements.* This study was financed by the Federal Ministry of Education and Research, Germany and the Brandenburg Ministry of Sciences, Research and Cultural Affairs, Germany.

## References

* Bachinger, J. & Reining, E. (2009): 'An empirical statistical model for predicting the yield of herbage from legume-grass swards within organic crop rotations based on cumulative water balances'. *Grass and Forage Sci* **64**: 144–159.

* Bloch, R., Wechsung, F., Heß, J & Bachinger, J. (2015): 'Climate change impacts of legume-grass swards: implications for organic farming in the Federal State of Brandenburg, Germany'. *Regional Environmental Change* **15** (2): 405–414.

* Reyer, C. et al. (2012): 'Climate change adaptation and sustainable regional development: a case study for the Federal State of Brandenburg, Germany'. *Regional Environmental Change* **12** (3): 523–542.
# Climate change adaptation in Australian wheat farm systems upscaled from farm to national scale

Afshin Ghahramani *$\pm$1, Philip N. Kokic 2, Andrew D. Moore 1 & Bangyou Zheng 1

## 1 Introduction

Wheat production is known to be sensitive to variations in both temperature and rainfall (Lobell et al., 2011). Changes in climate are expected to have varying impacts in different regions of the globe although negative impacts are expected to be more common than positive ones (Porter et al., 2014). A reduction in Australian wheat production can potentially affect global food security (FAO, 1996), its global availability (Ingram, 2011), and the global food market (Lobell et al., 2011) as Australia is the fourth largest wheat exporter in the world (Connor et al., 2011). Changes in climate over the past century interact with advances in agricultural technology and farming systems (Lobell et al., 2011). As, greater changes in climate are predicted in the near future compared to the changes of the late 20th century (Parry et al., 2007) continued technology and farming systems adaptations will be needed. Previous evaluations of climate change impacts on Australian wheat yield and gross margin change have been applied to a limited number of sites and have not included effective methods to scale up the analyses to a national level to provide industry and policy makers with a clearer insight for high level planning. In this paper we evaluated the impact of climate change and the effectiveness of adaptations for projected climate scenarios in 2030 relative to a historical baseline of 1980-1999, in order to estimate the value of adaptation in terms of production and financial returns. We did biophysical modelling of unit scale results and used farm survey data and a survey estimation method to upscale results to a cross-regional/ national level.

## 2 Materials and Methods

APSIM version 7.5 was used to simulate biophysical processes. We applied additional functions to account for frost and heat stress further to APSIM's current formulation. Soil parameters were selected from the APSoil data base. Initial soil Nitrogen (N) of each location was estimated from a long term historical run under continuous sowing without resetting soil N. Models were setup for continuous sowing while soil nitrogen was reset at the end of each year. The baseline sowing window and reference cultivars were selected based on local conditions (literature) and producer's workshops. Sowing was simulated when 3-day total rainfall exceeded 10 mm and plant available soil water (PASW) exceeded a threshold. Since many Australian farmers are already applying financially optimized N fertiliser (Carberry et al., 2013), we modelled a financially optimized N fertilizer policy. Here we assessed 3 incremental adaptation options included varying the input nitrogen fertiliser, the sowing dates and the choice of crop variety maturity type. These options provide opportunities to enhance the system's efficiencies toward the enhanced frontier (Keating et al., 2010) by increasing water use efficiency. We identified the financially optimal combination of the above options at each location under historical and projected climate to determine the management resulting in the best financial output averaged over time. A factorial simulation experiment was conducted in which the factors were climate (2 scenario × 2 sensitivity × 6 GCMs), location (30), and adaptation options (11 sowing offsets × 4 levels fertilizer × 3 cultivars). A multipurpose model based survey estimation methodology (Bardsley and Chambers, 1984; referred to as the BC methodology) was used to upscale the simulation results to a national scale. This is the same methodology as used by the Australian Bureau of Agricultural and Resource Economics and Sciences to produce estimates from its national broadacre farm survey (ABARES, 2003). Simulation results for the 30 case study sites are expressed on a per-hectare wheat area harvested basis. In the BC method a weight is computed for each case study unit. To achieve this, units are categorized into a typology, in this case the ABARES (Australian Bureau of Agricultural and Resource Economics and Sciences) farm survey regions (Fig. 1a), from which covariates can be calculated for upscaling. These covariates are the mean wheat yield and wheat price averaged over the base time period 1980-1999 and estimated from ABARES's farm survey. All financial values were deflated by the consumer price index (CPI) and expressed in 2007 AUD. Corresponding national estimates of these quantities were obtained for the farm survey from ABARES's Agsurf website. All estimates produced from APSIM-derived variables need to be bias-corrected to compensate for modelling assumptions that differ from reality. Bias correction factors were computed for ABARES regions and applied equally to all locations within each region. The adapted yield (AY) and adapted gross margin (AG) are upper limits for fully enhanced systems with all adaptation strategies (in this study) at the enhanced frontier (EF). For historical climate, lower limits are the historical yield (HY) and historical gross margin (HG) under current practice. For future climate we defined lower limits as current practice yield (CPY) and current practice gross margin (CPG). AY and AG are fully

<footnote n="1">1 CSIRO Agriculture Flagship</footnote>

<footnote n="2">2 CSIRO Digital Productivity & Services Flagship</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: af.ghahramani@csiro.au</footnote>
adapted (enhanced) systems on the EF. These modelled values may not be achievable due to biophysical, management, social, or economic constraints. Here, all projections in 2030 have been associated with the effect of elevated atmospheric CO₂ unless otherwise indicated.

## 3 Results – Discussion

Enhancing system efficiency of the baseline period could increase yields across sites up to 79% with a median of 15% compared to HY. In the baseline, gross margin increased by optimal adaptations (AG) across sites in range between 1% and 216% with a median of 20% compared to HG. By applying current practice in 2030, relative yield compared to the baseline varied between -37% and +19% with a median of -1%. Adaptations could offset the impact of climate change on yield across sites up to +76% with a median of +15% compared to the baseline. This offset by adaptation in terms of gross margin was up to 208%. By applying adaptation options to the baseline, yield (tonnes/ha) and gross margin (AUD$/ha) changed by +17% and +33%, respectively, at the EF. Under projected climate for 2030 (averaging over scenarios, sensitivities, and GCMs) and without enhancing current efficiency of the farm systems, yield and gross margin over the entire Wheatbelt were projected to decline by 1% compared to the baseline, i.e. a 0.15 million tonne decline in production. With current cost and prices this will result in AUD$32M p.a. lower gross margin compared to the baseline. Yield and gross margin per unit area of the Wheatbelt increased by applying the adaptation options in both the baseline and 2030 climate while inter-annual variability (1980-1999) increased. Given the consistency of our modelling results for CO₂ effect with experiments (Ainsworth and Long, 2005; Amthor, 2001; Asseng et al., 2013a) we expect that the Wheatbelt should realise the benefits of elevated atmospheric CO₂ (Fig 4) projected here. At the national scale, the simulated fertilisation effects of the elevated atmospheric CO₂ on yield are predicted to be large enough to not only offset the negative impacts of changes in rainfall and temperature but also to increase yields without enhancing current efficiency (Fig 1b). The upscaling methodology in here is a tried and well tested methodology, it is an approach that can be used when the sample of case study sites is unbalanced relative to the whole population. The upscaling weights approximately calibrate the estimates to totals of the benchmark variables in the base period at the national level. Accurate estimation of the potential value of adaptation relies on a strong linear relationship between the benchmark variables and income, as well as the bias correction. It is not possible to predict changes in the bias so it was necessary to assume that this component remains fixed in relative terms in the projection period and at the EF.

<figure><img src="image_78.png" /><figcaption>Fig. 1. (a) Location map (b) upscaled gross margin and yield, with and without effect of elevated CO₂.</figcaption></figure>

## 4 Conclusions

Over the Australian Wheatbelt as a whole, the projected yield and the gross margin at 2030 did not change substantially compared to the baseline with current practices. At the national scale, there might be a greater opportunity to increase yield over current levels by applying currently available management options, due to a boost from the moderate elevated atmospheric CO₂ effect on enhanced water use efficiency in 2030. It should be noted, however, that at more than half of sites a decline was projected in production and gross margin.

*Acknowledgements.* This work has been supported by strategic funding of CSIRO.

## References

* ABARE (Australian Bureau of Agricultural and Resource Economics). (2003). Australian farm surveys report 2003. Canberra

* Bardsley, P., Chambers, R. (1984). Multipurpose estimation from unbalanced samples. Applied Statistics. 33, 290-299.

* Carberry, P.S., et al. (2013). Scope for improved eco-efficiency varies among diverse cropping systems. Proceedings of the National Academy of Sciences. 110, 8381-8386.

* Lobell, B.D., et al. (2009). Crop yield gaps: Their importance, magnitudes, and causes. Annual Review of Environment and Resources. 34, 79-204.

* Porter, J., et al. (2014) Working Group II. Climate Change Impacts and Adaptation, Intergovernmental Panel on Climate Change Chapter 7, Food Security and Food Production Systems.

* FAO. (1996). Rome Declaration and World Food Summit Plan of Action, Rome, Italy

* Keating, B.A., et al. (2010). Eco-efficient agriculture: concepts, challenges, and opportunities. Crop Science. 50, S-109-S-119.

* Ingram, J. (2011). A food systems approach to researching food security and its interactions with global environmental change. Food Security 3, 417-431.

* Connor, D.J., Loomis, R.S., Cassman, K.G. (2011). Crop ecology: productivity and management in agricultural systems. Cambridge University Press, Cambridge.

* Parry, M., et al. (2007). Climate change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Vol. 4. Cambridge University Press, Cambridge.
# Climate change impact on Western Australian mixed farm systems

Afshin Ghahramani*±<fnref n="1" />, Andrew D. Moore<fnref n="1" />, Steven J. Crimp<fnref n="1" />, David Bowran<fnref n="2" />

## 1 Introduction

Primary enterprises are expected to contend with more frequent climate crises, environmental degradation and even climate-related regulatory change (IPCC, 2014). These stressors occur against an existing backdrop of conventional drivers including economic, biophysical, institutional, cultural and political pressures (Marshall et al., 2012). Australia's primary industries have historically operated in a highly variable climate and this has posed significant challenges to production, requiring sound and responsive risk management practices. Climate change, brings with it a number of new challenges not yet accounted for by Australian primary producers, and so understanding the scale of these impacts is of importance in undersanding the changing nature of agricultural risk in the near future. Western Australia with about 4 million ha of wheat production is a major contributor to the Australian agrifood sector and economy. Like cereal production, pastures in WA play a major role in agricultural enterprises and contribute over $3 billion annually through animal production, improvements to crop rotations and conserved fodder (The Department of Agriculture and Food, 2014). Farming profitably in the Western Australia in recent years has been a challenge due in part to declines in annual rainfall as well as exposure to both heat and cold temperature extremes (McConnell & O'Hare, 2013), although lower production might be still profitable. Climate drives the productivity, profitability and environmental health of these systems as they often have to respond to low and variable rainfall. Here we identify the likely effect of climate change in 2030 on mixed farm systems of the Western Australia across a climate transect in terms of production, profit, and environmental impacts for projected climate scenarios in 2030 relative to the baseline of 1980-1999. This work will give insight for designing strategies to respond to changes in climate such as optimized shift towards more intensive livestock systems, dual-purpose cropping, etc.

## 2 Materials and Methods

Four (4) representative mixed farm systems were identified across a climate gradient of 369 to 241 mm of growing season rainfall (Apr-Oct). The rationale for using a transect approach was to capture the range of possible future impacts across a range of soil and climate regimes (Fig. 1a). These sites represent complex agro-ecosystems with different soil, farm, livestock management and input intensity regimes. Representative farming systems were developed across this transect through facilitated workshops with stakeholders, and modelled by linking the APSIM soil water, soil nutrient cycling, crop and surface residue simulation models (Holzworth et al., 2014) to the GRAZPLAN pasture and ruminant simulation models (Moore et al., 1997) via an AusFarm interface. Future climate conditions we established by using two greenhouse gas emission scenarios of A1FI and A2 in conjunction with six different global climate models (GCM): ECHAM 5, GFDL 2.1, HADCM3, HADGEM1, MIROC-H, and MRI-GCM232. This range of emission and climate models allowed us to sample across a wide range of possible future climate conditions at 2030 and compare against mean conditions for the period 1980 to 1999. A gross margin (GM) calculation was carried out for each financial year of each modelled farming system (ABARES, 2014). Atmospheric CO₂ concentrations of 449 ppm for 2030 under the A1FI and 444 ppm under A2 were assumed, and for the baseline, we used monthly observed atmospheric CO₂ concentration. C-N flow was made possible by coupling organic matter cycling amongst plants, soils, and animals. In addition to expert knowledge, simulated crop yields were validated through producer's workshops and with regional database of Co-operative Bulk Handling (unpublished) as the best proxy yield data available.

## 3 Results – Discussion

Averaged over 20 years and for all climate models, projected yields declined for most of the crops × sites combinations in a range between 1.6% (Canola) to 18.2% (Lupin). Wheat yield increased only at Katanning by 6.7% while barley increased by 4.2% in Katanning and 13.7% at Cunderdin when compared to the baseline (Fig. 1b). Simulated crop gross margins were also shown to decline between 4.5% and 21.4%, except for Katanning, where GMs were simulated to increase by 8.9%. Crop gross margins were highly variable over time with greatest variability at Merredin and smallest at Katanning (Fig. 1a). Changes in simulated livestock production were much more modest than for crop production with stock sale weights increasing by up to 1.7% (Fig. 1a). Wool production declined by 3.3% and 2.7% in Cunderdin and Merredin, while increased by 1.7% in Katanning. Changes in crop/livestock production and financial outputs of sites and years were non-linearly related to the changes in growing season (Apr-Oct) rainfall and temperature projected

<footnote n="1">¹ CSIRO Agriculture Flagship, GPO Box 1600, Canberra ACT 2601, Australia</footnote>

<footnote n="2">² Department of Agriculture and Food Western Australia, Northam WA 6401, Australia</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author; af.ghahramani@csiro.au</footnote>
for 2030 (Fig. 1c). Relative changes in crop gross margins declined progressively with a warming in maximum and minimum temperatures and declines in rainfall greater than 11% (Fig. 1c). Some improvements in crop GM's were simulated for modest declines in rainfall (i.e. 1 to 10% declines) which is most likely related to reductions in water-logging and oxygen deficits in root systems on shallow duplex soils common in this area (Fig. 1c). Livestock production/profit tended to be less responsive to changes in climate (Fig. 1b).

<figure><img src="image_79.png" /><figcaption>**Fig. 1.** (a) Location map, (b) Relative changes of production in 2030 compared to the baseline, (c) Relative change of crop and livestock production & gross margin (GM) in 2030 (averaged over all GCMs, sensitivities, and scenarios) compared to the baseline related to the changes in the local climate (Apr-Oct) at each simulated year - the fitted lines are non-parametric regressions; the shaded areas are the 95% confidence intervals.</figcaption></figure>

The fertilization effect from elevated atmospheric CO₂ is one component of the climate change impacts in water-limited environments i.e. the great majority of Australian agriculture (Tubiello et al., 2007). Here the positive effect of the elevated atmospheric CO₂ on crop yield and crop annual net primary productivity (ANPP) declined with climate gradient toward the drier eastern farming areas (Table 1) while the modelled pasture ANPP had less decline (Table 1). Changes in climate generally had less impact on livestock and pasture production (with current stocking rate) in comparison with cropping systems. A main characteristic of the climate change adaptation strategies in dryland mixed-farming system management would see shifts in enterprise mix options, and include shifts toward & away from livestock enterprises depending on how the climate drives the financial optimization of the whole farm system. Overall the livestock gross margin is more likely to be sustained in 2030 in comparison with those of crops (Fig. 1c).

<table><caption>**Table 1.** Relative changes in ANPP with (+CO₂) and without (-CO₂) fertilisation effect of elevated atmospheric CO₂</caption><thead><tr><th rowspan="2">ANPP</th><th colspan="2">Cunderdin</th><th colspan="2">Katanning</th><th colspan="2">Merredin</th><th colspan="2">Mullewa</th></tr><tr><th>+CO₂</th><th>-CO₂</th><th>+CO₂</th><th>-CO₂</th><th>+CO₂</th><th>-CO₂</th><th>+CO₂</th><th>-CO₂</th></tr></thead><tbody><tr><th>Total</th><td>-2.8%</td><td>-3.6%</td><td>+2.3%</td><td>-1.2%</td><td>-8.5%</td><td>-9.2%</td><td>-12.9%</td><td>-12.8%</td></tr><tr><th>Pasture</th><td>-2.3%</td><td>-16.6%</td><td>+2.7%</td><td>-4.8%</td><td>-8.6%</td><td>-16.4%</td><td>-</td><td>-</td></tr><tr><th>Crop</th><td>-7.1%</td><td>-2.4%</td><td>+4.5%</td><td>+1.4%</td><td>-6.6%</td><td>-8.7%</td><td>-12.9%</td><td>-12.8%</td></tr></tbody></table>

## 4 Conclusions

The current production and profitability of whole mixed farm systems in Western Australia appears to be unsustainable around the drier margins in 2030. The cropping component of mixed-farm systems is more sensitive to climate change than the livestock component, and an increase in the proportion of livestock in the farming system in these regions may enhance current and future resilience to climate change.

*Acknowledgements.* Department of Agriculture, Meat & Livestock Australia, and Australian Wool Innovation have funded this research. The authors acknowledge support from Department of Agriculture and Food of Western Australia, the Facey and the Liebe group and farmers around Merredin.

## References

* ABARE (Australian Bureau of Agricultural and Resource Economics) (2003). Australian farm surveys report 2003. Canberra.

* Holzworth, D.P., et al. (2014). APSIM-evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw, **62**, 327-350.

* IPCC (2014). Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 688 pp.

* McConnell, G., O'Hare, N. (2013). Break crop economics kwinana east rscn, GRDC.

* Marshall, N.A., et al. (2012). Transformational capacity and the influence of place and identity. Environ. Res. Lett. **7**.

* Moore, A.D., Donnelly, J.R., Freer, M., 1997. GRAZPLAN: decision support systems for Australian grazing enterprises. 3. Pasture growth and soil moisture submodels, and the GrassGro DSS. Agric. Syst. **55**, 535-582.

* Tubiello, F.N., Rosenzweig, C., Kimball, B.A., Pinter, P.J., et al., 1999. Testing CERES-Wheat with free-air carbon dioxide enrichment (FACE) experiment data: CO₂ and water interactions. Agron. J. **91**, 247-255.
# Will breeding efforts be counteracted by uncertain water supply due to prospective climate change?

Nikolai Svoboda *±1, Maximilian Strer 1 & Johannes Hufnagel 1

## 1 Introduction

Extreme yield drops in Europe in 2003 (loss of ~13 billion Euros) were partly associated with declines in precipitation of approx. 300 mm (Tubiello et al., 2007). Regional climate models (Meinke et al., 2013) show strong tendencies for decreasing rainfall in summer i.e. growing period for the North German Plain (-11%). Continuously increasing yields of the most field crops (Laidig et al., 2014) together with the knowledge of decreasing summer rainfall guided us to the question if crop yield will increase in future like it increased over the last 60 years, or if insufficient rainfall may cause a plateau or decrease in crop yield development?

## 2 Materials and Methods

In the present study Diepholz in the western part of the North German Plain was investigated. Diepholz has a long term mean temperature of 9.6°C and 719 mm of precipitation. In Diepholz, 2003 was the year with the lowest precipitation (523 mm) during the observation period (1981–2010). Regionalised observed weather data (STARS (Orlowsky et al., 2007), 1950 to 2010) and regionalized projected climate data (RCP 8.5, ECHAM6, MPI Hamburg, Germany, STARS, 2011 to 2070) was provided by the PIK on the basis of daily values. We analyzed two time slots with 20 years each. The first represents the status quo from 1981 to 2010 (slot1) and the second the future from 2051 to 2070 (slot2). Typical plant available soil water in the study region is 120 mm (pseudogleyic luvisol, mostly sand with small loam content and 60 cm rooting depth) and soil water storage is full in spring. Begin and end of crop transpiration during the growing period in the harvest year was determined on the basis of phenological data as provided by the German Weather Service (DWD). While transpiration in summer crops begins with the plant emergence (Roth et al., 2005) (silage maize 10.5.; potato 5.5.), winter wheat starts significant transpiration in the harvest year when mean daily temperature exceed 3°C in the phase of tillering (21.2. in the Diepholz region) (Roth et al., 2005). End of transpiration was assumed to match with the harvesting event (silage maize = 6.10.; potato = 19.7.; winter wheat = 6.8.). All future management dates were fixed on to the dates determined for the status quo. Yield data of silage maize, potato and winter wheat (nuts1; each for 1951 to 2010; n=60) was provided by the statistical office of Lower Saxony. For each crop we fitted a linear model to the yield (Rijk et al., 2013; Laidig et al., 2014). The slope of this function than, represented the annual yield increase of the past and served as the yield increase of the future. Water use efficiency of winter wheat was set to 349 l kg⁻<fnref n="1" /> while potato has 217 l kg⁻<fnref n="1" /> and silage maize has 191 l kg⁻<fnref n="1" /> (Roth et al., 2005).

## 3 Results

Mean annual precipitation does not change over time (709 and 708 in slot1 and slot2 respectively) but in previous studies we found a significant shift towards increased winter rainfall and consequently decreasing precipitation during summer (Svoboda et al., 2014). Rainfall during the growing period (begin of significant transpiration) in slot1 amounted in 318 mm for silage maize (10.5. to 6.10.), 160 mm for potato (5.5. to 19.7.) and 316 mm for winter wheat (21.2. to 6.8.). When summing up precipitation within the slot2 growing period we have 255 mm for silage maize (-63 mm), 139 mm for potato (-21) and for winter wheat 284 (-32) mm. Thus we have a decreased rainfall of 10 to 20 percent relative to the respective crop within the growing period due to the shift towards more winter precipitation. Taking this in mind, we calculated the yield in both time slots and, consequently, the water demand of the crops based on mean yield and water use efficiency. In slot1 winter wheat has a mean water demand of 249 mm (yield 7,13 t DM ha⁻<fnref n="1" />). For silage maize in slot1 we calculated a water demand of 262 mm (yield 12,06 t DM ha⁻<fnref n="1" />) and for potato 172 mm (yield 9,00 t DM ha⁻<fnref n="1" />). For the slot2 we found an increased water demand due to increased yield of 426 (+71) mm for winter wheat (yield 12,20 t DM ha⁻<fnref n="1" />), 304 (+42) mm (yield 15,92 t DM ha⁻<fnref n="1" />) for silage maize and 272 (+100) mm (yield 14,24 t DM ha⁻<fnref n="1" />) for potato. Thus we calculated an increased water demand of 16 to 71 percent related to the respective crop.

<footnote n="1">¹ Institute of Land Use Systems, Leibniz Centre for Agricultural Landscape Research</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: svoboda@zalf.de</footnote>
<table><caption>**Table 1.** slot1: Yield, relating water demand and possible water deficit of important crops (silage maize: SMA; potato: POT; winter wheat: WWE) today (slot1: 1991 – 2010) and in the future (slot2: 2051 – 2070).</caption><thead><tr><th>time slot</th><th>crop</th><th>plant<br>available soil<br>water<br>[mm]</th><th>growing<br>period<br>[date]</th><th>precipitation<br>during vegetation<br>period [mm]</th><th>total<br>available<br>water [mm]</th><th>yield<br>[t DM ha<sup>-1</sup>]</th><th>water<br>demand<br>[mm]</th><th>water deficit<br>[mm]</th></tr></thead><tbody><tr><td rowspan="3">slot1</td><td>SMA</td><td>120</td><td>10.5.-6.10.</td><td>318</td><td>438</td><td>12</td><td>262</td><td>176</td></tr><tr><td>POT</td><td>120</td><td>5.5.-19.7.</td><td>160</td><td>280</td><td>9</td><td>172</td><td>108</td></tr><tr><td>WWE</td><td>120</td><td>21.2.-6.8.</td><td>316</td><td>436</td><td>7</td><td>249</td><td>187</td></tr><tr><td rowspan="3">slot2</td><td>SMA</td><td>120</td><td>10.5.-6.10.</td><td>255</td><td>375</td><td>16</td><td>304</td><td>71</td></tr><tr><td>POT</td><td>120</td><td>5.5.-19.7.</td><td>139</td><td>259</td><td>14</td><td>272</td><td>-13</td></tr><tr><td>WWE</td><td>120</td><td>21.2.-6.8.</td><td>284</td><td>404</td><td>12</td><td>426</td><td>-22</td></tr></tbody></table>

Together with the plant available soil water at the beginning of the growing period (120 mm) we have a crop dependent water supply from 280 to 438 mm. During the slot1 there is sufficient water, as characterized by a positive water deficit, for all three crops but a small deficit becomes visible in the second slot. While for silage maize an oversupply of 71 mm is left, we could expect a water deficit of -13 for potato and of -22 mm for winter wheat (Table 1). Thus we could detect only little water deficit within the future time slot for potato and winter wheat while for silage maize no deficit becomes visible. For winter wheat the deficit becomes zero eight years earlier (2044 – 2063) with a yield of 11,6 t DM ha⁻¹ and for potato nine years earlier (2043 – 2062) with a yield of 13,5 t DM ha⁻¹.

## 4 Conclusions

In conclusion, we cannot expect any drought-related reductions in yield increase of silage maize. For potato as well as for winter wheat a plateau in yield increase is reached. For these crops management procedures has to be adjusted if yield gap should maintain on the present level. The intensification of irrigation is in this region a sustainable option, since the total precipitation is sufficient. Next steps will be identifying the count and length of drought periods in future scenarios. Drought periody may cause strong yield depression within the growing period due to drought stress. Calculating water demand of catch or intercrops, often grown before summer, may guide to increased water deficit and, consequently, makes adaption strategies more necessary.

## References

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* Meinke, I., Gerstner, E., von Storch, H., Marx, A., Schipper, H., Kottmeier, C., Treffeisen, R., Lemke, P. 2010. Regionaler Klimaatlas Deutschland der Helmholtz-Gemeinschaft. Mitteilungen DMG, 02/2010, pp. 5–7. http://www.norddeutscher-klimaatlas.de/klimaatlas/2071-2100/sommer/niederschlag/norddeutschland/mittlereanderung.html (in German) (last access: 28.03.2015).

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* Roth, D.; Günther, R.; Knoblauch, S. & Michel, H. (2005), 'Wasserhaushaltsgrößen von Kulturpflanzen unter Feldbedingungen', Technical report, Thüringer Landesanstalt für Landwirtschaft.

* Svoboda, N.; Strer, M. & Hufnagel, J. (2014), 'Sustainable agriculture under the impact of climate change' Aspects of Applied Biology - Agronomic decision making in an uncertain climate'.

* Tubiello, F. N.; Soussana, J.-F. & Howden, S. M. (2007), 'Crop and pasture response to climate change', Proceedings of the National Academy of Sciences 104, 19686–19690.