south part (south to 38°N) while the combination with two early maturing cultivars to the north part of Hebei Plain.

<table><caption>Table 1. Yield, economic return and WUE of different treatments (Yuan·ha⁻¹, 2013)</caption><thead><tr><th></th><th>EM<sub>1</sub><br>EM<sub>1</sub></th><th>-C<sub>1</sub><br>C<sub>1</sub></th><th>EM<sub>2</sub>-C<br>EM<sub>2</sub></th><th>C<sub>2</sub><br>C<sub>2</sub></th><th>C<sub>2</sub>- EM<br>C<sub>2</sub></th><th>EM<sub>2</sub></th><th>W-M<br>wheat</th><th>maize</th></tr></thead><tbody><tr><td>Yield (kg·ha<sup>-1</sup>)</td><td>7495</td><td>9115</td><td>7822</td><td>8982</td><td>7369</td><td>6939</td><td>6822</td><td>10167</td></tr><tr><td>Value of production</td><td>16264</td><td>19780</td><td>16973</td><td>19490</td><td>15991</td><td>15057</td><td>16045</td><td>22061</td></tr><tr><td>Machinery cost</td><td>2925</td><td>1425</td><td>2925</td><td>1425</td><td>2925</td><td>1425</td><td>2475</td><td>2175</td></tr><tr><td>Material cost</td><td>9459</td><td>5787</td><td>9408</td><td>5989</td><td>9229</td><td>6166</td><td>9033</td><td>7887</td></tr><tr><td>Net return</td><td>6805</td><td>13994</td><td>7567</td><td>13502</td><td>6763</td><td>8891</td><td>7012</td><td>14175</td></tr><tr><td>Annual yield (kg·ha<sup>-1</sup>)</td><td colspan="2">16610 a</td><td colspan="2">16803 a</td><td colspan="2">14308 b</td><td colspan="2">16988 a</td></tr><tr><td>Annual net return</td><td colspan="2">20 99</td><td colspan="2">21069</td><td colspan="2">15654</td><td colspan="2">21187</td></tr><tr><td>ET (mm)</td><td colspan="2">7 430 b</td><td colspan="2">435 b</td><td colspan="2">428 b</td><td colspan="2">778 a</td></tr><tr><td>WUE (kg·mm<sup>-1</sup>·ha<sup>-1</sup>)</td><td colspan="2">38.5a</td><td colspan="2">38.5 a</td><td colspan="2">33.5 b</td><td colspan="2">21.8 c</td></tr></tbody></table>

Note: Different lower case letters after figures indicate a statistically significant difference of 95% probability.

## 4 Conclusions

5

A preliminary conclusion can be made that double maize system is competitive to the traditional wheat - maize system in annual productivity and economic return, and much more water efficient, given that suitable cultivars are selected and properly combined. It could be an alternative cropping system in the north part of the North China Plain, or be used as a remedy after winter kill of winter wheat. However, systematic and in-depth studies on ecological, economic and social impacts of the system are needed and relevant techniques need to be fine-tuned and packaged before the new system can be widely extended.

*Acknowledgements.* The study presented in this paper was financially supported by the national special research fund for public good agricultural program 'Establishment of modern farming systems and development of corresponding techniques' (project ID: 201103001). Sincerely thanks go to colleagues and students who have offered great assist in implementing the field trial, data collection and processing.

## References

Keating, B. A., Carberry, P. S., Hammer, G. L, Probert, M. E., Robert-son, M. J., Holzworth, D., Huth, N. I., Hargreaves, J. N. G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J. P., Silburn, M., Wang, E., Brown, S., Bristow, K. L., Asseng, S., Chapman, S., McCown, R. L., Freebairn, D. M. & Smith, C. J. (2003). An overview of APSIM, a model designed for farming systems simulation. Eur J Agron, 18: 267-288.

Li, L. J, Wang M. Y & Zhao, M. (2011a). Adaptability of Varieties to Double-cropping in Early Spring and Late Summer. ACTA AGRONOMICA SINICA, 37(9): 1660-1665. DOI: 10.3724/SP.J.1006.2011.01660.

Li, L. J, Wang, M.Y., Xue, Q. L., Cui, Y.H., Hou, H. P., Ge J. Z. & Zhao, M. (2011b). Yield Performance and Resource Efficiency of Double-cropping Maize in the Yellow, Huai and Hai River Valleys Region. ACTA AGRONOMICA SINICA, 37(7): 1229-1234. DOI: 10.3724/SP.J.1006.2011.01229.
# Trade-off between productivity and nitrous oxide emissions in subtropical broad-acre systems – a modelling approach

Henrike Mielenz *±1*, Peter J. Thorburn 1, Clemens Scheer 2, Massimiliano De Antoni Migliorati 2, Mike Bell 3
& Peter R. Grace 2

**1 Introduction**

Subtropical regions host 23 % of global agricultural land, and hence have a considerable contribution to global food production and to nitrous oxide (N2O) emissions. N2O emissions from subtropical farming systems contribute more than 15 % of the global N2O emissions from fertilised land. Efficient N2O mitigation strategies for this climate zone that also preserve crop yields are therefore necessary. In this study we explored possible mitigation opportunities for representative subtropical grain cropping systems and management practices by simulating long-term scenarios with the agricultural systems model APSIM (Holzworth et al. 2014). Beforehand APSIM was calibrated and validated with data from two field sites in subtropical Queensland, Australia.

**2 Materials and Methods**

In the first step, APSIM was calibrated with measured water contents, yields and high frequency N₂O emission data from field experiments in subtropical Australia. The experiments included contrasting soil types (an Oxisol and a Vertisol), various fertiliser and irrigation treatments and different crops (Scheer et al., 2012; 2013; De Antoni Migliorati et al., 2014; 2015). In the calibration step, we focused on a thorough calibration of soil physical properties for each site, i.e. field capacity, permanent wilting point and the water filled pore space above which denitrification starts (dnitlim), instead of calibrating a large number of parameters controlling the carbon and nitrogen (N) cycles. A small subset of the data from each site was used for calibration, while the majority were used for validation. In the second step, long-term (40 yrs) crop rotations with varying fertilisation and irrigation strategies representative for the humid subtropics were simulated with the validated model to assess possible N₂O mitigation options.

**3 Results – Discussion**

Water dynamics (not shown), yields (Fig. 1a) and seasonal N₂O emissions (Fig. 1b) were accurately predicted for the crops subjected to different irrigation and fertilisation strategies. These results confirm the capability of APSIM to reliably predict these output variables without adjusting process parameters for each site, season or treatment. The parameter dnitlim was the only parameter calibrated separately for each site because it had a large effect on predicted denitrification and may be related to other, measurable soil properties such as soil texture. Being able to accurately simulate the results with minimal calibration suggests that the model can be more readily applied in other locations.

<figure><img src="image_19.png" /><figcaption>**Fig. 1.** Predicted against measured (a) crop yield and (b) seasonal N₂O emissions for the validation data sets of the two experimental sites. Standard deviation of the observations, 1:1 (solid) and regression lines (dashed) are shown. RMSE is root means square error, R<fnref n="2" /> is coefficient of determination and ME is model efficiency.</figcaption></figure>

<footnote n="1">1 CSIRO Agriculture, Brisbane, Australia</footnote>

<footnote n="2">2 Institute for Future Environments, Queensland University of Technology, Brisbane, Australia</footnote>

<footnote n="3">³ Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Gatton, Australia</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: henrike.mielenz@csiro.au)</footnote>
The long-term scenarios revealed two relationships between yield and N₂O emissions (Fig. 2). At high N rates (large symbols), increasing N input increased N₂O emissions but not yield, resulting in a negative outcome for both variables. At low N rates (small symbols), increasing N inputs led to an increase in both yield and N₂O emissions and resulted in a trade-off between maximising yield and minimising N₂O emissions. Crop yields were significantly higher when irrigation (open symbols) was applied, compared to the rainfed counterparts (closed symbols), indicating that water supply limited plant growth and yield. At the same time, however, N₂O emissions at high N rates were lower in irrigated than in rainfed wheat. This appeared counterintuitive because high soil moisture is an important driver for N₂O emissions, but it occurred because water stress in highly-fertilised rainfed wheat resulted in higher surplus N compared to the irrigated treatments. Thus more substrate for denitrification was available when the soil got wet.

When a legume (here chickpea) was included in a crop rotation, the yield plateau for wheat was reached at lower N rates compared to a monoculture (Fig. 2). This provided savings of up to 40 kg N ha⁻¹ per wheat crop and reduced N₂O emissions by 0.5 to 0.9 kg N ha⁻¹. However, the yield plateau for rainfed wheat was ~20 % smaller when in rotation with chickpea than in monoculture, suggesting that chickpea depleted more water from the soil than wheat. Thus, less water was available for the next wheat crop, which is important in this environment where water is often limiting for crop growth. Long-term scenarios showed high interannual variability of N₂O emissions and yields. Results from short-term experiments may therefore not be representative of the long-term behaviour of these subtropical agro-ecosystems, and so simulation studies may be important to gain insights into long-term emissions and mitigation options.

<figure><img src="image_20.png" /><figcaption>**Fig. 2.** Trade-off between yield and N₂O emissions for two crop rotations simulated for the vertisol; seven fertilisation intensities: 0, 20, 40, 80, 120, 160 and 200 kg N ha⁻¹ (size of bubbles) per non-legume crop, rainfed and irrigated (irrigation when 50% of the plant available water capacity in the upper 60 cm of the soil was depleted).</figcaption></figure>

## 4 Conclusions

After targeted calibration of a small number of site-specific parameters, the APSIM model was able to adequately predict water dynamics, yields and cumulative N₂O emissions for two soils in the subtropics, four crops and a number of irrigation and fertilisation intensities.

N₂O emissions were not caused or influenced by only one management factor (e.g. irrigation or fertilisation) but by a complex interaction of several factors. Consequently, a holistic approach should be taken to identify N₂O mitigation strategies that do not compromise yield. N fertiliser should be applied after considering soil N stocks and available water (soil water storage, irrigation and expected rainfall). When legumes were included in the crop rotation, N application and thus N₂O emissions could be reduced. Given the annual variability in climate and in soil N, yield forecasting tools in combination with soil testing for mineral N and reliable weather forecasts would provide great benefit in managing N application and thus optimising the dynamic trade-off between yield and N₂O emissions.

*Acknowledgements.* This research was undertaken as part of the National Agricultural Nitrous Oxide Research Program (NANORP) funded by the Grains Research and Development Corporation (GRDC) and the Department of Agriculture, Fishery and Forestry (DAFF), Australia.

## References

De Antoni Migliorati, M., Scheer, C., Grace, P.R., Rowlings, D.W., Bell, M. & McGree, J. (2014). Influence of different nitrogen rates and DMPP nitrification inhibitor on annual N₂O emissions from a subtropical wheat–maize cropping system. *Agric. Ecosyst. Environ.* **186**, 33–43.
doi:10.1016/j.agee.2014.01.016

De Antoni Migliorati, M., Bell, M., Grace, P.R., Scheer, C., Rowlings, D.W. & Liu, S. (2015). Legume pastures can reduce N₂O emissions intensity in subtropical cereal cropping systems. *Agric. Ecosyst. Environ.* **204**, 27–39. doi:10.1016/j.agee.2015.02.007

Holzworth, D.P., Huth, N.I., DeVoil *et al*. (2014). APSIM - Evolution towards a new generation of agricultural systems simulation. *Environ. Model. Softw.* **62**, 327–350. doi:10.1016/j.envsoft.2014.07.009

Scheer, C., Grace, P.R., Rowlings, D.W. & Payero, J. (2012). Nitrous oxide emissions from irrigated wheat in Australia: impact of irrigation management. *Plant Soil* **359**, 351–362. doi:10.1007/s11104-012-1197-4

Scheer, C., Grace, P.R., Rowlings, D.W. & Payero, J. (2013). Soil N₂O and CO₂ emissions from cotton in Australia under varying irrigation management. *Nutr. Cycl. Agroecosystems* **95**, 43–56. doi:10.1007/s10705-012-9547-4
# Impact of two simple fertilization strategies on farmers' product carbon footprints and gross margins of wheat and maize production in the North China Plain

Nan Ha<sup>±1</sup>, Til Feike<sup>2</sup>, Haifeng Xiao<sup>3</sup> & Enno Bahrs<sup>1</sup>

## 1 Introduction

Overuse of Nitrogen (N) fertilizer in crop production exerts massive negative environmental effects in the North China Plain (NCP). In the intensive winter wheat (WW) – summer maize (SM) double cropping system, which is the dominant cropping system of this important crop production region, excessive N rates are common practice (e.g. Cui *et al.*, 2008). Additionally to the pollution of groundwater excessive N is also a major contributor to agricultural greenhouse gas (GHG) emissions and thus strongly contributes to global climate change. Despite the ongoing efforts, which mainly focus on field experiment based development of improved fertilization strategies, and include the promotion of technologically sophisticated N management schemes (e.g. Cui *et al.*, 2008), farmers’ N rates maintain at excessive rates. Therefore the current study tests two simple and easily to apply N fertilizer recommendation strategies, which could be implemented on large scale through the existing agricultural advisory system of China, at comparatively low cost. To capture farmers’ current crop management conditions and the inherent diversity among farmers, the present study builds on a primary data set of detailed crop management information of 65 WW-SM producing farm households. In a first step the farmers’ individual product carbon footprint and gross margins of WW and SM are determined. In the next step the effects of the two simple N fertilization strategies are tested under conditions of constant, increasing and decreasing yield levels.

## 2 Materials and Methods

Farm household survey was conducted in 2011 in Quzhou – a representative county of the NCP. Detailed crop management data, including timing, amounts and prices of all crop production measures, inputs and outputs were interrogated and complemented by secondary data sources. Product carbon footprint (PCF), describing the amount of emitted CO₂-equivalents per unit produced grain, was determined following ISO 14040 standards (ISO, 2006). Gross margin (GM) was selected as economic indicator, describing the difference between sales revenue and variable production cost expressed in monetary value per land use area (EURO ha⁻<fnref n="1" />). A detailed description of the methodological procedure for PCF and GM calculations as well as the status quo performance of the surveyed farm households can be found in Ha *et al.* (2015).

The first of the two simple fertilizer recommendation strategies, the N-uptake strategy is based on a comprehensive study conducted by Ju & Christie (2011), who assessed numerous long-term field experiments for WW and SM production in the NCP. Here the recommended amounts of N are determined based on the farmers’ individual expected yield levels. The second N fertilization strategy (N-fixed) is based on Meng *et al.* (2012), who determined the optimum N rates for WW and SM in Quzhou at 176 and 185 kg N ha⁻<fnref n="1" />, respectively.

To account for the uncertainty regarding the effect of adjusted N fertilization levels on the potential grain yields obtained by the individual farmer two yield change scenarios were introduced, additionally to the assumption of constant yields under changed fertilization (baseline yield). In the yield loss scenario (Y<sub>loss</sub>) yields were reduced by 10%, while in the yield increase scenario (Y<sub>increase</sub>) yields were increased by 10%.

## 3 Results and Discussion

<table><caption>**Table 1.** Status quo average, minimum and maximum PCF and GM of WW and SM production of the 65 sampled farm households.</caption><thead><tr><th></th><th>Units</th><th>Average</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td></td><td>WW</td><td></td><td></td><td></td></tr><tr><td>PCF</td><td>kg CO<sub>2</sub>e kg<sup>-1</sup></td><td>0.90</td><td>0.25</td><td>2.61</td></tr><tr><td>GM</td><td>EURO ha<sup>-1</sup></td><td>713.5</td><td>48.3</td><td>1433.9</td></tr><tr><td></td><td>SM</td><td></td><td></td><td></td></tr><tr><td>PCF</td><td>kg CO<sub>2</sub>e kg<sup>-1</sup></td><td>0.46</td><td>0.17</td><td>1.62</td></tr><tr><td>GM</td><td>EURO ha<sup>-1</sup></td><td>980.5</td><td>483.9</td><td>1372.4</td></tr></tbody></table>

<footnote n="1">¹ Institute of Farm Management (410b), University of Hohenheim, Stuttgart, Germany</footnote>

<footnote n="2">² Institute for Strategies and Technology Assessment, Julius Kühn-Institut (JKI) | Federal Research Centre for Cultivated Plants, Kleinmachnow, Germany</footnote>

<footnote n="3">³ College of Economics and Management, China Agricultural University, Beijing, China</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: nan_ha@uni-hohenheim.de</footnote>
The mean PCFs in WW and SM production were 0.90 and 0.46 kg CO₂e kg⁻¹, respectively, while the average GMs of WW and SM production were 713.5 and 980.5EURO ha⁻¹, respectively. This clearly shows that SM performs relatively better regarding its environmental effect and economic performance compared to WW. Furthermore, a huge heterogeneity in PCF and GM was observed among the 65 sampled WW-SM farm households, as shown in Table 2. In both WW and SM the PCF of the worst performing farm household was ten times higher than the PCF of the best performing farm household. Those results highlight the importance of assessing PCF and GM improvement potentials of the alternative simple fertilization strategies in the context of the existing diversity in crop management.

<table><caption>**Table 2.** Average PCF reduction and GM increase of WW and SM production under the three yield and two N fertilization scenarios compared to baseline conditions; percentage changes to average PCF and GM under baseline conditions are given in brackets.</caption><thead><tr><th rowspan="2"></th><th colspan="2">PCF reduction (kg CO<sub>2</sub>e kg<sup>-1</sup>)</th><th colspan="2">GM increase (EURO ha<sup>-1</sup>)</th></tr><tr><th>N-uptake</th><th>N-fixed</th><th>N-uptake</th><th>N-fixed</th></tr></thead><tbody><tr><td>WW:</td><td></td><td></td><td></td><td></td></tr><tr><td>yield loss</td><td>+0.236 (+26 %)</td><td>+0.209 (+23 %)</td><td>-80.6 (-11 %)</td><td>-82.5 (-12 %)</td></tr><tr><td>yield constant</td><td>+0.259 (+29 %)</td><td>+0.277 (+31 %)</td><td>+45.9 (+6 %)</td><td>+52.2 (+7 %)</td></tr><tr><td>yield increase</td><td>+0.278 (+31 %)</td><td>+0.332 (+37 %)</td><td>+172.2 (+24 %)</td><td>+186.9 (+26 %)</td></tr><tr><td>SM:</td><td></td><td></td><td></td><td></td></tr><tr><td>yield loss</td><td>-0.037 (-7 %)</td><td>-0.022 (-4 %)</td><td>-142.1 (-15 %)</td><td>-137.7 (-14 %)</td></tr><tr><td>yield constant</td><td>-0.026 (-5 %)</td><td>+0.029 (+5 %)</td><td>-6.5 (-1 %)</td><td>+7.3 (+1 %)</td></tr><tr><td>yield increase</td><td>-0.010 (-2 %)</td><td>+0.078 (+14 %)</td><td>+129.2 (+13 %)</td><td>+152.2 (+16 %)</td></tr></tbody></table>

The impact of the two tested N fertilization strategies on farmers' PCF and GM under potential yield change was evaluated with regard to the average improvement in PCF and GM over all farmers (Table 2). Except for the PCF reduction potential in WW under yield loss conditions, the N-fixed strategy always resulted in a better performance compared to the N-uptake strategy. When comparing the relative improvement potentials of the two N scenarios in PCF with the improvement potentials in GM under constant yield a strong difference can be observed for both crops. While the mean PCF reduction potentials in WW range from 29 % to 31 %, the potential GM increases only reach from 6 % to 7 %. For SM the effects are generally smaller with 5 % to -5 % in PCF and 1 % to -1 % in GM.

Over all scenario combinations the PCF reduction potential is much higher in WW compared to SM, both in absolute and relative terms. The differences are mainly caused by the comparatively stronger over fertilization in WW compared to SM, both with regard to quantity of farmers and amount of fertilizer per farmer. Assessing the impact of the yield scenarios over both N scenarios reveals that yield change has a much stronger impact on GM than on PCF, even under the N fixed rate scenario.

## 4 Conclusion and Recommendations

The study revealed that fertilization according to the recommended fixed N rate (N-fixed) would result in a massive reduction of GHG emission from crop production in the NCP. It was furthermore revealed that saving fertilizer as a result of improved N fertilization strategy reduced farmers' PCF significantly, while it had no significant effect on farmers' GM under the assumption of constant yield. On the other side, a potential 10 % yield loss would have only a marginal effect on PCF, but a detrimental effect on farmers' income. With farmers currently applying excessive N rates as a “cheap insurance” against potential N limitations, the agricultural advisory system of China requires fundamental changes to successfully overcome the excessive fertilizer use and respective environmental pollution. The study concludes that the indirect subsidization of N fertilizers needs to be stopped and a cross compliance system should be implemented, which punishes non-compliance with maximum allowed N rates by cutting direct farm payments.

*Acknowledgements.* The authors highly acknowledge the financial support by the China Scholarship Council (Fund No.20073020), the German Research Foundation (GRK1070), and the Ministry of Education of the People's Republic of China.

## References

Cui, Z.L., Chen, X.P., Miao, Y.X., Zhang, F.S., Sun, Q.P., Schroder, J., Zhang, H.L., Li, J.L., Shi, L.W., Xu, J.F., Ye, Y.L., Liu, C.S., Yang, Z.P., Zhang, Q., Huang, S.M.&Bao, D.J. (2008). On-farm evaluation of an in-season nitrogen management strategy based on soil Nmin test. *Field Crops Research*, **105**, 48-55.

Ha, N., Feike, T., Angenendt, E., Xiao, H.F. & Bahrs, E. (2015). Impact of farm management diversity on the environmental and economic performance of the wheat-maize cropping system in the North China Plain. *International Journal of Agricultural Sustainability*, DOI: 10.1080/14735903.2015.1004856 (in press).

ISO, International Organization for Standardization (2006). Environmental Management – Life Cycle Assessment – Principles and Framework, EN ISO 14040.

Ju, X.T. & Christie, P. (2011). Calculation of theoretical nitrogen rate for simple nitrogen recommendations in intensive cropping systems: A case study on the North China Plain. *Field Crops Research*, **124**, 450-458.

Meng, Q.F., Sun, Q.P., Chen, X.P., Cui, Z.L., Yue, S.C., Zhang, F.S. & Römheld, V. (2012). Alternative cropping systems for sustainable water and nitrogen use in the North China Plain. *Agriculture, Ecosystem and Environment*, **146**, 93-102.
# Environmental performance of nitrogen fertiliser limits on root crops potatoes and sugar beets

Karoline D'Haene *±1, Joost Salomez 2, Stefaan De Neve 3, Jeroen De Waele 3 & Georges Hofman 3

## 1 Introduction

Previous efforts to improve the quality of ground- and surface water have resulted in improvements, but in several regions in Flanders, the 50 mg nitrate ($NO_3^-$) $l^{-1}$ norm from the European Nitrates Directive has still not been achieved. Limiting nitrogen (N) fertiliser application rates is one of the best N management measures to minimise N losses. This limitation, however, can not only be seen in view of improving the water quality (regulating ecosystem service) but also taking into consideration its impact on crop yield and quality (provisioning ecosystem service).

In the range from low to optimum fertilisation rates, most crops show a rather constant residual soil mineral N (RSMN) at harvest. This constant RSMN is considered to be the minimum mineral N buffer necessary to guarantee optimal growth. When N fertiliser rates are increased to rates above this optimum, the RSMN shows a breakpoint and increases steeply for most crops, thus increasing the risk of $NO_3^-$ leaching during winter (Hofman et al., 1981). Nitrogen fertilisation experiments can help to determine this breakpoint. In this study, we collected a large set of data from N fertiliser experiments on root crops potatoes and sugar beets and critically re-analysed these data to calculate the effects of N fertiliser rate and soil N availability on RSMN, yield and N uptake and to evaluate the current fertiliser limits.

## 2 Materials and Methods

We used the pooled data of different field trials (1991-2010) on potatoes and sugar beets in Flanders and northern Wallonia (D'Haene et al., 2014) to determine the effect of different N fertiliser rates (= applied effective N) and crop N availability on RSMN, yield and N uptake. The N in organic manure is only partially crop available. Mineral fertiliser, slurry or farmyard manure contain 100, 60 and 30% effective N, respectively (Anonymous, 2011; Webb et al., 2011). The crop available N equals mineral N in the soil at the start of the growing season + N deposition + N mineralisation + applied effective N during the growing season.

We re-analysed the data sets consisting of RSMN values ($NO_3^-$-N only) of the 0-60 and 0-90 cm layer (rooting depth) for potatoes (sampled 14/09 - 13/10) and sugar beets (sampled 17/09 - 29/10), respectively. The mixed soil was analysed for $NO_3^-$-N by extraction with 1M KCl solution (ISO, 2003). We have tested a (segmented) linear, exponential, quadratic and power model for RSMN versus applied effective and crop available N. The breakpoint with the smallest confidence interval of segmented linear regression was calculated according to Oosterbaan et al. (1990).

If measured N concentrations (Kjeldahl method) were available, these were used to calculate N uptake, alternatively calculations were based on measurements made in other locations and on literature data. To avoid an over- or under-estimation of the N uptake, any unmeasured N concentration was adjusted via a process of gradual N increase in function of crop available N. A minimum % N on dry matter (DM) was fixed at a crop available N of 225 kg N $ha^{-1}$, being the average of fields fertilised with less than 50 kg applied effective N $ha^{-1}$. Based on the measurements performed by PCA, an increasing % N between 1.15 and 1.75% N on DM was used for tubers of potato plots with 225 to 375 kg crop available N $ha^{-1}$. Nitrogen export of sugar beets was calculated by multiplying fresh yield by 0.23 (DM fraction) and a % N (increasing % N between 0.6 and 0.8% N on DM for plots with 225 to 325 kg crop available N $ha^{-1}$) (Hofman et al., 1984; IRS, 2013). For the unharvested roots, stalks, cobs and leaves of unfertilised and fertilised plots, the N uptake data are based on measurements of PCA and KBIVB and various literature data (Hofman et al., 1984; Bries et al., 1995; Draycott, 2006; IRS, 2013). A logistic model was used to relate exported yield and total N uptake to i) crop available N and ii) applied effective N. Its characteristic S-shape gives it a broad applicability to a variety of processes that exhibit such sigmoidal behavior, including yield and crop N uptake (Overman et al., 2003).

## 3 Discussion

For potatoes and sugar beets a maximum tuber yield of 61.8 and 90.9 Mg fresh matter $ha^{-1}$, respectively, was calculated with a logistic model in function of applied effective N. Calculated maximum total N uptake (= N uptake by tubers and unharvested plant parts) in function of applied effective N was 268 and 365 kg N $ha^{-1}$ for potatoes and sugar beets,

<footnote n="1">¹ Plant Sciences Unit, Institute for Agricultural and Fisheries Research (ILVO)</footnote>

<footnote n="2">² Flemish government, Department of Environment, Nature and Energy</footnote>

<footnote n="3">³ Department of Soil Management, Faculty of Bioscience Engineering, Ghent University</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: karoline.dhaene@ilvo.vlaanderen.be</footnote>
respectively (Fig. 1). The calculated exported tuber yield and maximum total N uptake differed insignificantly between the two approaches (in function of crop available and applied effective N) (P > 0.05).

<figure><img src="image_21.png" /><figcaption>**Fig. 1.** Nitrogen (N) dose-response curves (Δ) and residual soil mineral nitrogen (rooting depth) (◊) in function of crop available N (left) and applied effective N fertilisation rate (right) for potatoes and sugar beets.</figcaption></figure>

For potatoes, a breakpoint in RSMN was noticed for both approaches (Fig. 1). RSMN at the breakpoint in function of the effective N fertilisation rate was 72±29 kg N ha⁻¹ and was situated at 199±77 kg applied effective N ha⁻¹. There is no ground for increasing the Flemish maximum allowed effective N application rates of 190 and 210 kg N ha⁻¹ for sandy and non-sandy soils (Anonymous, 2011), respectively, given that RSMN increases sharply after the breakpoint. A decrease of the N fertiliser limit is not advised from an economic standpoint, as the N fertilisation rate not only affects total but also marketable yield (van Evert *et al.*, 2011). For both approaches, RSMN for sugar beets was low and constant (13±4 kg N ha⁻¹) over the entire range under study (0-160 kg applied effective N ha⁻¹). The maximum applied effective N application rate of field experiments with sugar beets is relatively low, as an excessive N amount increases impurities and decreases extractable sugar content. The maximum allowed effective N fertilisation rates for sugar beets (135 and 150 kg N ha⁻¹ on sandy and non-sandy soils, respectively) (Anonymous, 2011) result in low RSMN, showing that the current fertiliser limits are well-founded, and there is even room to slightly increase the N limits.

## 4 Conclusions

Combining dose response curves with RSMN enables to derive maximum allowed N fertilisation rates which allows improving the provisioning ecosystem service (i.e. optimal qualitative yield) but also the regulating ecosystem service by limiting the potential risk of NO₃⁻ leaching during winter. RSMN not only depends on the crop but also on crop available and applied effective N, except for sugar beets where RSMN is low within the range of applied effective N not negatively affecting tuber quality. The presence of a breakpoint between RSMN and applied effective N for potatoes gives the opportunity to deduce optimum N fertilisation rates limiting the potential risk of NO₃⁻ leaching during winter whilst maintaining yield levels. However, even after an optimal fertilisation the maximum allowed NO₃⁻ concentration can be exceeded after the harvest of some crops. RSMN before the breakpoint of potatoes is rather high and only part of the N mineralised from the potato and sugar beet leaves, which remain on the field after harvest, is denitrified or can be found in the soil in the following spring. Therefore, NO₃⁻ leaching during winter period after optimal fertilisation has to be estimated in order to evaluate the necessity of measures such as sowing catch crops or removal of crop residues.

*Acknowledgements.* We would like to thank Soil Service of Belgium, PCA, KBIVB and Ghent University for supplying the field information.

## References

* Anonymous (2011). Decreet houdende wijziging van het Mestdecreet van 22 december van 2006. *Belgisch Staatsblad*, 27876-27894.

* Bries, J., Vandendriessche, H. & Geypens, M. (1995). Bemesting en beregening van aardappelen in functie van opbrengst en kwaliteit. Soil Service of Belgium, Heverlee.

* D'Haene, K., Salomez, J., De Neve, S., De Waele, J. & Hofman, G. (2014). Environmental performance of the nitrogen fertiliser limits imposed by the EU Nitrates Directive. *Agriculture, Ecosystems and Environment*, **192**, 67-79.

* Draycott, P.A. (2006). Sugar Beet. Blackwell Publishing, Oxford.

* Hofman, G., Van Ruymbeke, M., Ossemerct, C. & Ide, G. (1981). Residual nitrate nitrogen in sandy loam soils in a moderate marine climate. *Pedologie*, **31**, 329-346.

* Hofman, G., Ossemerct, C., Ide, G. & Van Ruymbeke, M. (1984). Stikstof-, fosfor- en kalibehoeften van de belangrijkste akkerbouwteelten. In: *Studie- en vervolmakingsdag 'Bemesting van intensieve akkerbouwteelten'*. KVIV, Antwerpen, 4.1-4.22.

* IRS (2013). Teelthandleiding. IRS Edt, Bergen op Zoom. http://www.irs.nl/alle/teelthandleiding

* ISO (2003). ISO 14256-1:2003. Soil quality - Determination of nitrate, nitrite and ammonium in field-moist soils by extraction with potassium chloride solution.

* Oosterbaan, R.J., Sharma, D.P. & Singh, K.N. (1990). Crop production and soil salinity: Evaluation of field data from India by segmented linear regression. Symposium on Land Drainage for Salinity Control in Arid and Semi-Arid Regions, Vol. 3, Cairo.

* Overman, A.R., Scholtz III, R.V. & Martin, F.G. (2003). In defense of the extended logistic model of crop production. *Communications in Soil Science and Plant Analysis*, **34**(5 & 6), 851-864.

* van Evert, F.K., van der Schans, D.A., Malda, J.T., van den Berg, W., van Geel, W.C.A. & Jukema, J.N. (2011). Geleide N-bemesting voor aardappelen op basis van gewasreflectie-metingen. Praktijkonderzoek Plant & Omgeving (PPO), Wageningen. http://edepot.wur.nl/181147

* Webb, J., Sorensen, P., Velthof, G.L., Ramon, B., Pinto, M., Rodhe, L., Salomon, E., Hutchins, N., Burczyk, P. & Reid, J. (2011). Study on variation of manure N efficiency throughout Europe. AEA Technology plc, Didcot.
# FARMING SYSTEMS TO SUPPORT FLEXIBLE CLIMATE RESPONSE STRATEGIES FOR CAMBODIAN SMALLHOLDER FARMERS

N.P. Dalgliesh *±<fnref n="1" />, P.L. Poulton <fnref n="1" />, P. Charlesworth <fnref n="2" /> & L. Le Non <fnref n="2" />

## 1 Introduction

The production of rice (*Oryza sativa* L.) is an important contributor to the Cambodian economy and to national food security. With government policy aiming to increase annual paddy rice production and to significantly increase annual exports, there is an imperative to increase national rice yields. Currently, the Cambodian national average yield of 2.41 t/ha is lower than those of the neighbouring countries of Thailand (2.86 t/ha⁻<fnref n="1" />), Laos (2.67 t/ha⁻<fnref n="1" />) and Vietnam (5.6 t/ha⁻<fnref n="1" />) (2010-12) (IRRI, 2013). As rainfed, lowland rice constitutes around 90% of the annual harvest area of 2.4 million hectares (MAFF, 2011), it is an obvious starting point in meeting national goals and in supporting the food security of a nation where 25% of the population suffered undernourishment in 2004-05 (Magnan and Thomas, 2011).

Overarching the agronomic and social challenges facing policy makers and farmers is the effects of seasonal climate variability on productivity, and the vulnerability of agriculture to the future impacts of climate change which are predicted to increase the frequency, severity and unpredictability of extreme weather-related events such as hurricanes, droughts, floods, and rising sea levels (IPCC, 2007). Nowhere is this likely to have more impact than in the south-eastern provinces of Svay Rien (11.089N, 105.819E) and Prey Veng (11.485N, 105.328E) where a combination of variable climate and low quality soils affects rice productivity (average provincial yield of 2.29 t/ha⁻<fnref n="1" />) (IFAD, 2013). In response, economic migration to population centres has been an important societal trend, with resultant labour shortages further exacerbating the challenges of increasing agricultural productivity (Roth et. al, 2013). It was hypothesised that in these provinces productivity could be increased, climate risk reduced and farmer livelihoods improved through the introduction of a number of systems interventions which were tested using on-station and on-farm biophysical research, financial analysis and cropping system analysis using the APSIM farming system model (Keating et al. 2003).

## 2 Methods

On-farm research was conducted on 167 farms (mean treatment size of 1230 m<fnref n="2" />) between 2011 and 2013. Comparisons were made between typical farmer practice, of using locally sourced medium and long duration rice varieties, transplanted in mid-wet season (June/July) and grown with low levels of nutrition, and a range of system interventions that included the use of modern, open-pollinated, short and medium duration varieties, increased cropping intensities and levels of nutrition and weed control, the use of supplementary irrigation and direct seeding (Table 1).

<table><caption>Table 1. Intervention options tested during on-farm research undertaken in south-eastern Cambodia (2011-2013).</caption><thead><tr><td>System</td><td>Rice variety (duration)</td><td>Crop establishment</td><td>Seasonal crop number; establishment</td><td>Agronomic practice</td></tr></thead><tbody><tr><td>Farmer practice</td><td>Local, farmer retained seed (140-170 days)</td><td>Transplanted</td><td>Crop 1: mid-wet (June/July)</td><td>Limited organic/inorganic fertiliser; hand weeding</td></tr><tr><td>Inter1- early</td><td>Modern, short duration (90-110 days)</td><td>Direct-drum seeding or hand broadcast</td><td>Crop 1: early-wet (May-early June)</td><td>N50:P23:K30 basal/top dress; hand/chemical weed/insect control</td></tr><tr><td>Inter1-mid</td><td>Modern, short duration (90-110 days)</td><td>Direct-drum seeding or hand broadcast</td><td>Crop 1: mid-wet (July-Sept)</td><td>N50:P23:K30 basal/top dress; hand/chemical weed/insect control</td></tr><tr><td>Inter2</td><td>Modern, short duration (90-110 days)</td><td>Direct-drum seeding or hand broadcast</td><td>Crop 1: early wet (May to early June). Crop 2: mid-wet (Sept)</td><td>N50:P23:K30/crop-basal/top dress; hand/chemical weed/insect control; irrigation to establish and/or to complete crop if required</td></tr><tr><td>Inter3</td><td>Modern, medium duration (120-140 days)</td><td>Direct-drum seeding or hand broadcast</td><td>Crop 1: mid-wet season (June/July)</td><td>N50:P23:K30-basal and top dress; hand/chemical weed/insect control</td></tr></tbody></table>

On-station research was used to calibrate the APSIM-Oryza model for current Cambodian rice varieties and conditions (Poulton et al. 2014) before being validated using on-farm experimental data. Confident in its ability to predict yield for local conditions, the model was then used to investigate the riskiness of rice production under current environmental conditions and future climate scenarios (Poulton et al. 2015, in these proceedings).

## 3 Results and Discussion

The use of modern, short and medium duration rice varieties, established using direct seeding, and grown using recommended fertiliser rates and agronomic management resulted in higher seasonal productivity and financial return than traditional practice (Table 2). The growing of 2 consecutive, short duration crops (Inter2) provided the highest

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

<footnote n="2">² iDE, Phnom Penh, Cambodia</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: Neal.Dalgliesh@CSIRO.Au</footnote>
median seasonal rice production in both 2012 and 2013, although in 2012, a season marked by late monsoonal onset and later flooding, the highest median gross margin (GM) (and the GM range for the middle 50% of farmers) was achieved by the medium duration crop (Inter3). In 2013, a more benign climatic season, the growing of 2 sequential crops (Inter2) achieved both the highest seasonal yield and GM (with labour).

<table><caption>Table 2. Median rice yield (kg/ha⁻¹) and gross margin (GM) for the production interventions tested (2011-2013).</caption><thead><tr><th rowspan="2">System</th><th colspan="4">2012<sup>*</sup></th><th colspan="4">2013<sup>a</sup></th></tr><tr><th>Median yield (14% moisture) (kg/ha<sup>-1</sup>)</th><th>Median GM (-labour) (US$/ha<sup>-1</sup>)</th><th>Median GM (+labour) (US$/ha<sup>-1</sup>)</th><th>GM-Mid 50% (+labour) (US$/ha<sup>-1</sup>)</th><th>Median yield (14% moisture) (kg/ha<sup>-1</sup>)</th><th>Median GM (-labour) (US$/ha<sup>-1</sup>)</th><th>Median GM (+labour) (US$/ha<sup>-1</sup>)</th><th>GM-Mid 50% (+labour) (US$/ha<sup>-1</sup>)</th></tr></thead><tbody><tr><td>Farmer practice</td><td>1852</td><td>283</td><td>(91)</td><td>(179)-(9)</td><td>3185</td><td>315</td><td>119</td><td>(33)-318</td></tr><tr><td>Inter1- single short crop, early plant</td><td>2740</td><td>193</td><td>(19)</td><td>(98)-125</td><td>3784</td><td>450</td><td>245</td><td>71-360</td></tr><tr><td>Inter1- single short crop, mid-season plant</td><td></td><td></td><td></td><td></td><td>3663</td><td>522</td><td>392</td><td>337-424</td></tr><tr><td>Inter2-2 short crops, early/mid plant</td><td>4923</td><td>376</td><td>9</td><td>(112)-255</td><td>6491</td><td>824</td><td>485</td><td>330-598</td></tr><tr><td>Inter3-single medium crop, mid season plant</td><td>1886</td><td>343</td><td>171</td><td>129-371</td><td>3404</td><td>469</td><td>292</td><td>249-351</td></tr></tbody></table>

While the timing of establishment of a single short duration crop (Inter1-early and -mid) had little impact on the median yield or the yields achieved by the middle 50% of farmers (not shown), there was a marked difference in median GM and an increased level of risk associated with early planting (Inter1-early) as shown by the GM range achieved by the middle 50% of farmers. This was a result of the increased variability associated with early monsoon season rainfall and the higher input costs associated with the growing of an early crop, in particular, weed and pest control and the need for supplementary irrigation. Farmer practice yields were similar to Inter3 (the nearest comparable intervention), but GMs were 2.5-3 times lower than those achieved for Inter3 with the differences reflecting the higher labour costs associated with rice transplant (25 person days/ha, compared to <1 day/ha with direct seeding) and high levels of variability in farmer input costs. The higher median farmer practice yield achieved in 2013 reflects the increasing confidence of individual farmers to adopt the more easily applied interventions including direct seeding and modern varieties.

## 4 Conclusions

There is little doubt that the adoption of the intervention options will increase crop yield and improve current farmer livelihoods. However, the replacement of a relatively simple, but inflexible, recipe based approach, with a range of more complex technologies which vary according to timing of monsoon onset and seasonal conditions are likely to be more attractive to those aiming to optimise production or are labour constrained. While the above examples could indicate that an individual would utilise a single cropping option, it is more likely that multiple options would be used to mitigate climatic and financial risk with choices varying according to seasonal conditions. Options include the use of short and medium, modern and traditional varieties, established at varying times to spread production risk, balance labour supply and demand and to meet market requirements, in conjunction with improved agronomic management.

Underlying these improvements will be the increasing use of mechanisation to reduce labour inputs and to improve timeliness of operations, particularly crop establishment and harvest.

As a result of this research, response farming now forms part of government and non-government extension programs across a number of the lowland rice provinces with detailed training materials currently being developed to support these activities.

## References

IPCC (Intergovernmental Panel on Climate Change). 2007. Fourth assessment report: Climate change 2007. http://www.wmo.int/pages/partners/ipcc/index_en.html (Accessed 16th July 2014)

IFAD (2013). Rural Poverty Reduction Project in Prey Veng and Svay Rieng-Project Performance Assessment Report No. 3097-KH. International Fund for Agricultural Development, Via Paolo di Dono, 44, Rome, Italy. August 2013.

IRRI World Rice Statistics (2013) http://ricestat.irri.org:8080/wrs2/entrypoint.htm (Accessed 16th July 2014).

Keating, B.A., Carberry P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N.G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J.P., Silburn, M., Wang, E., Brown, S., Bristow, K.L., Asseng, S., Chapman, S., McCown, R.L., Freebairn, D.M., Smith, C.J. (2003) The Agricultural Production Systems Simulator (APSIM): its history and current capability. *European Journal of Agronomy* **18**, 267–288.

MAFF (Ministry of Agriculture Forestry and Fisheries, Cambodia) (2011). *Agricultural statistics*. Phnom Penh, Cambodia: Department of Planning, Statistics and International Cooperation.

Magnan, N. and Thomas T.S. (2011). Food Security and Climate Change to 2050: Cambodia. A Policy Discussion Paper. Cambodia Food Security and Agricultural, Policy Stocktaking Roundtable. 4 November 2010 - Phnom Penh. ISBN 978-99963-59-00-2. (Accessed 16th July 2014)

Poulton, P.L., Veasna, T., Dalgliesh, N.P and Seng, V. (2014). Applying simulation to improved rice varieties in reducing the on-farm yield gap in Cambodian lowland rice ecosystems. Experimental Agric. J. on CJO2014. doi:10.1017/S0014479714000271.

Poulton, P.L., Dalgliesh, N.P., Seng, V. (2015). Building resilience of small-holder farmers in Cambodian lowland rice ecosystems in responding to future climate uncertainty. Proceedings-5th International Symposium for Farming Systems Design, Montpellier, France 7-10th September 2015.

Roth, V., Hing, V., Phann, D. and Sum, S. (2013). Effects of emigration on household's livelihoods: The case of Cambodia. Published by the Partnership for Economic Policy (October 2013). http://portal.pep-net.org/projects/view/zone/public/id/12379 (Accessed 16th July 2014).

<footnote>*2012 season-late monsoon start and seasonal flooding; a2013 season-early monsoon start and no extreme seasonal conditions</footnote>
# Reducing pesticide use while reaching economic and environmental sustainability in Arable Farming

Martin Lechenet *<fnref n="1" />, Vincent Bretagnolle <fnref n="2" />, Christian Bockstaller <fnref n="3" />,<fnref n="4" />, François Boissinot <fnref n="5" />, Marie-Sophie Petit <fnref n="6" />, Sandrine Petit <fnref n="1" /> & Nicolas M. Munier-Jolain ±<fnref n="1" />

## 1 Introduction

The substantial reduction in pesticide use is one of the key-issue for improving agriculture sustainability. Integrated farming, based on Integrated Pest Management (IPM) principles that emphasize physical and biological regulation strategies to control pests while reducing the reliance on pesticides, is presented as an alternative path in between conventionnal and organic farming likely to better reconcile agricultural productivity with other components of sustainability (Holland et al., 1994). Here, we used a network of 48 contrasted cropping systems (conventionnal, integrated or organic) to analyse the relationship between the level of pesticide use and eight aspects of agricultural sustainability (Lechenet et al., 2015).

## 2 Materials and Methods

The cropping systems included in the study were from two regions in France (Burgundy and Poitou-Charentes), and were either tested in cropping system experiments or surveyed in commercial farms sampled so as to maximise the variability in the level of pesticide use. Eight systems complied with the specifications of organic farming, 30 systems followed some IPM principles (diversified crop rotations and/or non-chemical pest management options such as resistant cultivars, mechanical weeding and false seed bed techniques) and 10 systems were classified as conventional systems. At each site, a conventional system was identified as a local reference corresponding to the current standard crop rotation and crop management. Each system was described with a detailed sequence of operations for soil tillage and crop management.

For each system the level of pesticide use was estimated using the Treatment Frequency Index (TFI) quantifying the number of registered doses applied, and eight indicators of sustainability were computed from the management data, namely the productivity (harvested MJ ha⁻<fnref n="1" /> year⁻<fnref n="1" />), the energy efficiency, the economic profitability expressed as the semi-net margin (Euros ha⁻<fnref n="1" /> year⁻<fnref n="1" />), the fuel consumption (L ha⁻<fnref n="1" /> year⁻<fnref n="1" />), the level of N fertilization (kg of N ha⁻<fnref n="1" /> year⁻<fnref n="1" />), the indicator of environmental impact related with pesticides I-Pest in standardized environmental conditions (van der Werf & Zimmer, 1998), the sensitivity of the semi-net margin to price volatility, and the workload (h ha⁻<fnref n="1" /> year⁻<fnref n="1" />). The Dia'terre® reference database was used for the computations of energy inputs and outputs. The sensitivity to price volatility was defined as the relative standard deviation of the semi-net margin calculated over ten contrasting real prices scenarios selected between 2000 and 2010. Each indicator was expressed as natural logarithm of the ratio between the cropping system and the local reference indicators, and this allowed separating the effects of the cropping system itself from the effects of the specific production context at each site. Pesticide use was expressed as relative TFI, i.e. as a ratio with the TFI of the local reference system.

## 3 Results - Discussion

Productivity of organic systems was about -50% on average below productivity of non-organic systems, which is a higher gap than highlighted in a recent review (Ponisio et al., 2015). But we found no correlation between TFI and productivity for conventional and integrated systems (Fig. 1A). The energy efficiency was frequently higher in integrated systems as compared to organic and conventional ones (Fig. 1B), partly because the frequency of crops for which the whole above-ground biomass is harvested was higher in IPM-based systems. Excluding organic systems, the correlation between TFI and energy efficiency kept a negative tendency when cropping systems producing grain crops only were considered, because IPM-based systems reduced energy inputs thanks to lower levels of N fertilisation and higher frequency of legume crops. In spite of their low energy input, organic farming yielded significantly lower energy efficiency as compared to IPM-based systems.

<footnote n="1">¹ INRA, UMR1347 Agroécologie, BP 86510, F-21000 Dijon, France</footnote>

<footnote n="2">² CEBC-CNRS, F-79360 Beauvoir sur Niort, France</footnote>

<footnote n="3">³ INRA, UR1121 Agronomie et Environnement, BP 20507, F-68000 Colmar, France</footnote>

<footnote n="4">⁴ Université de Lorraine, 2 Avenue Forêt de Haye, F-54500 Vandoeuvre, France</footnote>

<footnote n="5">5 Chambre d'Agriculture des Pays de la Loire, F-49105 Angers, France</footnote>

<footnote n="6">⁶ Chambre Régionale d'Agriculture de Bourgogne, F-21800 Quetigny, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: nicolas.munier-jolain@dijon.inra.fr</footnote>
The semi-net margin, when averaged over the ten price scenarios, was very similar for organic, conventional and IPM-
based systems, and no correlation was detected between TFI and the average semi-net margin (Fig. 1C). The range of
profitability was higher for IPM-based systems, as both the systems providing the highest and the lowest profitability
were following IPM principles. The sensitivity to price volatility was significantly lower in organic cropping systems
than in other ones, probably because (i) they were based on more diversified crop rotations, which spread risks and
buffered semi-net margin at the farming system scale, and (ii) they are less dependent on exogenous inputs, and notably
volatile inputs such as N fertilizers.

<figure><img src="image_22.png" /><figcaption>**Fig. 1.** Relationship between the level of pesticide use (relative TFI) and four sustainability indicators for 48 contrasted cropping systems: (A) productivity, (B) Energy efficiency (linear regression is represented with standard error, Pearson correlation test: rp = -0.38, P = 0.02), (C) Semi-net margin, and (D) workload. Each sustainability indicators is expressed as the natural logarithm of the ratio between the cropping system and the local reference indicators.</figcaption></figure>

We found a large variability in the workload required to manage cropping systems (ranging from 2.5 to 6.2 h ha⁻¹ year⁻¹), but surprisingly there was no correlation with TFI. High labour requirements were mainly correlated with the application of organic manure.

As expected we found a close relationship between TFI and the indicator related with the impact of pesticide residues in the environment I-Pest. Organic systems consumed significantly higher levels of fuel as compared to the rest of the sample, but we found no correlation between this non-renewable input and TFI for non-organic systems.

**4 Conclusions**

According to the results, IPM-based system appeared as the best compromise in sustainability trade-offs: they contribute to reduce substantially the use of pesticide and related environmental impacts while providing high productivities (higher than organic farming), good profitability, high energy efficiency, and this without necessarily increasing the workload and the consumption of non-renewable inputs. Both the crop diversification and the insertion of legume crops in grain crop rotations appear as major components of sustainable cropping systems (Lechenet et al., 2015).

*Acknowledgements.* The study was funded by ANR (STRA-08-02 Advherb) and by Région Bourgogne. We thank A. Villard, C. Vivier and M. Geloen for their contribution to the experimental network in Burgundy, and D. Meunier, P. Farcy, P. Chamoy for technical assistance.

**References**

Holland, J.M., Frampton, G.K., Cilgi, T. & Wratten, S.D. (1994). Arable acronyms analysed - a review of integrated arable farming systems research in Western Europe. *Annals of applied biology*, **125**, 399-438.

Lechenet, M., Bretagnolle, V., Bockstaller, C., Boissinot, F., Petit, M. S., Petit, S., & Munier-Jolain, N. M. (2014). Reconciling pesticide reduction with economic and environmental sustainability in arable farming. *PloS one*, **9**(6), e97922.

Ponisio, L. C., M'Gonigle, L. K., Mace, K. C., Palomino, J., de Valpine, P., & Kremen, C. (2015). Diversification practices reduce organic to conventional yield gap. *Proceedings of the Royal Society of London B: Biological Sciences*, **282**(1799), 20141396.

Van der Werf, H., Zimmer, C. (1998). An indicator of pesticide environmental impact based on a fuzzy expert system. *Chemosphere* **36**, 2225-2249.
# USING FADN TO DERIVE CROPPING SCHEME DISTRIBUTION IN ITALY

Giuliano Vitali<sup>*±1</sup>, Sergio Albertazzi<sup>1</sup>, Davide Rizzo<sup>2</sup>, Concetta Cardillo<sup>3</sup> & Guido Baldoni<sup>1</sup>

## 1 Introduction

Arable land is a major component both in landscape and in food production system. The ability to understand and describe how spatial and dynamical characters affect human life and eventually to design the agricultural systems beyond the processes ruling them is fundamental to preserve land and ensure economic, ecological and social sustainability.

In this paper, we will refer to cropping scheme as a general term merging two aspects of cropping systems, the spatial and the temporal one, represented respectively in terms of crop pattern and crop rotation. Analysis of crop rotations is of basic importance in sustainable farming strategies dealing with climate change, food security and organic farming. In fact crop rotations are fundamental to maintain fertility and, even if their analysis often focus on local crops, they help facing market and weather uncertainties as well as interpreting landscape diversity and its dynamics (Dury et al., 2012, Thenail et al. 2009).

In European Community (EC) an agricultural holding data-base standard has been developed, the Farm Accountancy Data Network (FADN), which is seldom rich of information on farm management techniques. The present study makes use of Italian FADN to identify more recurrent crop patterns and evaluate the possibility to describe them as a crop rotation.

## 2 Materials and Methods

The FADN survey platform (http://ec.europa.eu/agriculture/rica/) is designed to monitor and evaluate agricultural dynamics to assess Common Agricultural Policies (CAP) and is based on the collection of information in a complex data-base (RICA). Italian data are from on a sample of farms extracted from Italian Census (ISTAT) farm population (about 750,000). Representativeness and geographic homogeneity requirements make the sample to be adjusted yearly not ensuring holding to be observed continuously. For the present investigation years 2008-2012 have been used, involving 18,608 different farms. As the study is addressed to detect rotations, a number of exceptions have been formerly identified (e.g. farms with no arable land) which makes to retain about 50% of farms of the initial sample.

RICA data-base includes, beyond economical accounting information, a description of environment, land use, crops and live stocks, agricultural practices, productions and resources (e.g. fertiliser, labour).

The crop list is comprehensive of 270 items: as they cannot be used directly for pattern / rotation recognition, they have been grouped into six categories on the base of the agro-technical inputs: set aside and not cropped surfaces (TR), forages, where tillages are performed each several years together with sowing, no treatments, no-irrigation, multiple harvests (FO), cereals, characterised by main and refining tillage, few treatments, no-irrigation, sowing, harvest (CR), intensive crops, including industrial crops and maize, similar to CR but with a higher environmental control together with irrigation (IN), rice, which has been considered separately because of its peculiar character in the Italian scenario (RI), other crops (OC).

Also this former analysis adopts two basic assumptions: 1) a crop rotation reflects the pattern observed on the whole arable surface - one crop rotation for farm; 2) annual crops are only involved in rotations (which also means neglecting the practice of successions winter-summer crops). Such assumptions allow to identify those patterns whose surface stay in integer ratios, reflecting both a surface and a time divide, which are coded after crop-groups and their ratios, e.g. "1CR-3FO" means that 25% of surface is grown with cereals and 75% with forages, or in a time-representations corresponds to a four-year rotation with 1 year cereals and 3 years forage crop; 4 is pattern rank.

## 3 Discussion

From the 411 patterns emerged from analysis, 16 of them are able to explain 90% of farms. On such patterns a stability analysis has been performed showing that yearly pattern records own to the same population ($\chi^2$> 99.95). Observing the same pattern on a farm for multiple years is both a validation of the pattern and an indicator of possibility to interpret the pattern as a rotation. However the real possibility of interpreting every patterns as rotation is prevented from some intrinsic feature of RICA protocol, which address population representativity, not requiring a farm be included in the survey every years; also RICA does not include any information of single fields.

Distribution of patterns in Italy on a regional basis (Fig.1) show a sound picture: in the north (Veneto and Friuli-

<footnote n="1">¹ DipSA, University of Bologna - IT</footnote>

<footnote n="2">² UMR 1248 AGIR, INRA-INPT, Castanet-Tolosan, FR</footnote>

<footnote n="3">³ CRA-INEA, Ministry of Agriculture - IT</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: giuliano.vitali@unibo.it</footnote>
Venezia-Giulia and Lombardia) a marked presence of industrial crops (including maize which is continuously cropped) the similar distribution on middle Italy hilly regions (Umbria, Marche and Toscana) as well as the extensive cerealsdominating the south landscape (Puglia and Calabria). Rice monocolture characterising only the Piemonte region is not reported.

<figure><img src="image_23.png" /><figcaption>Fig. 1. Distributions of crop patterns in Italian regions; in X axis is reported the pattern code given by space/time frequency and crop codes: CR: cereals, IN: intensive crop, FO: forage.</figcaption></figure>

4 Conclusions

The present analysis represents a former approach in using FADN data to recognise cropping schemes. The methodology, based on crop clustering based on technical inputs, seems able to put in evidence the more common schemes practised in Italy but still limited is the possibility to relate crop patterns to crop rotations required from policy makers, landscape analysts, farm decision process and also useful to be compared to outputs of rotation generators used in landscape and farm modelling.

References

Dury, J., Schaller, N., Garcia, F., Reynaud, A., & Bergez, J. E. (2012). Models to support cropping plan and crop rotation decisions. A review. *Agronomy for Sustainable Development*.

Thenail, C., Joannon, A., Capitaine, M., Souchère, V., Mignolet, C., Schermann, N., Baudry, J. (2009). The contribution of crop-rotation organization in farms to crop-mosaic patterning at local landscape scales. *Agriculture, Ecosystems and Environment, 131*(3-4), 207-219.
# CHARACTERIZATION AND QUANTIFICATION OF ROBUSTNESS FOR DESIGNING MORE SUSTAINABLE AND ROBUST WHEAT PRODUCTIONS

Nicolas Urruty *±1,2*, Christian Huyghe <fnref n="1" />, Delphine Tailliez-Lefebvre <fnref n="3" /> & Philippe Gate <fnref n="4" />

## 1 Introduction

The context of increasing uncertainty for the farmers (global warming, price volatility...) defines major challenges for agronomic research. In European crop production, climatic variability has strong impacts, as illustrated by wheat yield stagnation since the middle of 1990's despite variety improvement (Brisson et al., 2010). An important challenge is then to design more sustainable agricultural systems in a more variable environment (Naylor, 2008). Different concepts have been mobilized to discuss adaptability in a changing world (resilience, vulnerability, robustness ...) but the meaning attributed to each of them is ambiguous and operationalization into empirical studies remains a challenge. In this paper, we propose a new method for quantifying robustness in agricultural systems and apply it on French bread wheat production to quantify yield robustness against abiotic constraints.

## 2 Materials and Methods

### 2.1 Robustness framework

Robustness has been first used in industrial production to optimize desired system characteristics despite internal or external perturbations (Taguchi and Clausing, 1990). In agricultural systems, robustness can be seen as the ability to maintain agricultural performances (or ecosystem services) above a certain threshold despite abiotic, biotic, economic or other constraints (ten Napel et al., 2006). In the following case study, yield robustness against abiotic constraints will be assessed through the response of wheat yield to abiotic stress variability.

### 2.2 Case study on wheat yield

Wheat yield and crop management data were collected from DEPHY-Ecophyto network run by InVivo and including 210 farms distributed in contrasting soil and weather conditions and studied over a three years time span (since 2011). It corresponds to 1600 wheat crops. Daily climatic data between 1983 and 2013 were provided by Météo-France at local scale (8 x 8 km). The 210 farms have been first classified into 33 climatic zones regarding their average exposition to 8 ecophysiological stresses (including water excess, water deficit, frost occurrence and high temperature during grain filling). Classification was done using hierarchical clustering method on these 8 variables. For each climatic zone, soil diversity among wheat crops was characterized using Arvalis Institut-du-Végétal regional soil database. Six different soil types were described on average for each climatic zone.

#### 2.2.1 Simulated impacts of abiotic stress on wheat yield

Over the 1983-2013 period, wheat yield variability was simulated for each soil type through STICS crop model (Brisson et al., 2002). STICS crop model integrates plant, soil and climate interactions and has been recently validated over a wide range of agro-environmental conditions (Coucheney et al., 2015).

To reflect only the impact of climate variability on yield, we used a fixed and standardized crop management file (i.e. constant in time and space)<fnref n="1" />. The simulation period was long enough (1983-2013) to cover contrasting climatic conditions. For each year, an Abiotic Stress Index (ASI) was calculated by relating the simulated yield on year *t* (*y*<sub>t</sub>) to the maximum yield reached during the 1983-2013 period (*y*<sub>max</sub>): ASI<sub>t</sub> = (y<sub>max</sub> - y<sub>t</sub>) / (y<sub>max</sub>).

As defined, ASI summarizes the impact of the different abiotic stress on yield production as a percentage of yield reduction in reference to the maximum yield potential (without limiting factors from climate).

#### 2.2.2 Yield variability and robustness against abiotic constraints

Temporal yield variability was measured through the coefficient of variation for the 62 farms scored over the three years of data. Based on elasticity measure, robustness of wheat yield was then assessed through the responsiveness of

<footnote n="1">¹ INRA (CODIR), 147 rue de l'Université, Paris (France)</footnote>

<footnote n="2">² Université de Poitiers, PRES France Centre Atlantique Université</footnote>

<footnote n="3">³ InVivo AgroSolutions, 83 Avenue de la Grande Armée, Paris (France)</footnote>

<footnote n="4">⁴ Arvalis Institut-du-Végétal, 5-7 Rue Joseph et Marie Hackin, Paris (France)</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: nicolas.urruty@paris.inra.fr</footnote>

<footnote n="1">¹ Only sowing date was differentiated between North (sowing at 5th October) and South (sowing at 20th October).</footnote>
yield to a change in simulated abiotic stress index. Robustness was calculated for each farm as the ratio between Abiotic Stress Index variability and observed yield variability (R = ΔASI/ΔYield).

## 3 Discussion

### 3.1 Abiotic Stress Index

Mean simulated and actual yields over 2011-2013 period were compared between the 62 farms and average simulated yields were consistent with observed data (R² = 0.65).

Simulation results of wheat yield over the 33 climatic zones are highly variable in space (from less than 6 t/ha in South-West to 10 t/ha in Bassin Parisien area) due to strong differences in climate and soil characteristics. Spatial variability of ASI is also important and the results indicate that wheat yields are particularly affected by abiotic constraints in the North-East of France where soils are more superficial and climate more severe (Fig. 1. left). We also observed an increasing temporal variability of ASI over the recent period (Fig. 1. right), meaning that impact of climate variability on agricultural production is getting stronger.

<figure><img src="image_24.png" /><figcaption>Fig. 1. Spatial (on the left) and temporal (on the right) variability of Abiotic Stress Index in wheat production.</figcaption></figure>

### 3.2 Yield performance and yield robustness assessment

Temporal yield variability of the 62 farms with 3 years data is presented in Fig. 2. (left). Yield variability is higher in southwest and northeast France, i.e. regions with the lowest yields. Yield robustness was measured as the inverse of the ratio between yield variability and abiotic stress index variability (Fig. 2. right).

<figure><img src="image_25.png" /><figcaption>Fig. 2. Coefficient of variation of wheat yield (on the left) and response of yield variability to abiotic stress variability (on the right).</figcaption></figure>

The preliminary results show a high variability of yield robustness to abiotic constraints in France. In a next step, agronomic determinants of yield robustness will be investigated to identify the most robust crop management strategies and economic and environmental consequences of yield robustness will be estimated. These findings will provide a better understanding of robustness in agriculture and potentially new decision tools to combine sustainability and robustness.

## References

* Brisson, N., Gate, P., Gouache, D., Charmet, G., Oury, F.-X., Huard, F., 2010. Why are wheat yields stagnating in Europe? A comprehensive data analysis for France. Field Crops Research 119, 201-212.

* Brisson, N., Ruget, F., Gate, P., Lorgeou, J., Nicoullaud, B., Tayot, X., Plenet, D., Jeuffroy, M.-H., Bouthier, A., Ripoche, D., 2002. STICS: a generic model for simulating crops and their water and nitrogen balances. II. Model validation for wheat and maize. Agronomie 22, 69-92.

* Coucheney, E., Buis, S., Launay, M., Constantin, J., Mary, B., de Cortázar-Atauri, I.G., Ripoche, D., Beaudoin, N., Ruget, F., Andrianarisoa, K.S., 2015. Accuracy, robustness and behavior of the STICS soil-crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France. Environmental Modelling & Software 64, 177-190.

* Naylor, R., 2008. Managing food production systems for resilience, in: Chapin, F.S., Kofinas, G.P., Folke, C. (Eds.), Principles of natural resource stewardship: resilience-based management in a changing world. Springer, New-York, p. 46.

* Taguchi, G., Clausing, D., 1990. Robust quality. Harvard Business Review 68, 65-75.

* ten Napel, J., Bianchi, F.J.J.A., Bestman, M.W.P., 2006. Utilising intrinsic robustness in agricultural production systems, Invention for a sustainable development of agriculture. TransForum, Zoetermeer, pp. 32-54.
# Cut fallow to replace black fallow in an organic production system

Göran Bergkvist *±<fnref n="1" />*, Kjell Mangerud <fnref n="2" />, Ewa Magnuski <fnref n="1" /> & Lars Olav Brandsæter <fnref n="2" />

## 1 Introduction

Couch grass (*Elymus repens*) has large impact on yield and management strategies in temperate areas of the world, particularly in the Nordic countries. The control is to a large extent based on use of glyphosate in conventional agriculture and repeated soil cultivations in autumn, i.e. during a period when soil cultivations should be avoided due to the risk of increased nutrient leaching, in organic farming (Askegaard *et al.* 2011). The energy input for the common practice of stubble cultivation exceeds by large the input needed for chemical control in conventional farming (Tzilivakis *et al.* 2005). Therefore it is important to develop methods of couch grass control that are efficient and more environmentally friendly than repeated tillage or use of large quantities of glyphosate. By using subsidiary crops between cash crops and avoiding both glyphosate and tillage, environmental problems like nutrient and pesticide leaching could be avoided and positive cropping system effects could be achieved. However, competition from the subsidiary crop may not be enough to control couch grass.

In earlier investigations under-sown crops have competed well with couch grass during the autumn and the subsequent year and reduced the amount of couch grass substantially compared to treatments without competition, but generally, couch grass biomass have increased compared to the initial situation (Håkansson 1969, Dyke and Barnard 1976, Bergkvist *et al.* 2010). The possibility to improve the effect of competition by mowing has been investigated by e.g. Håkansson (1969) and Brandsæter *et al.* (2012). According to Håkansson (1969) the method works, but the cutting interval must be very short. Brandsæter *et al.* (2012) and Ringselle *et al.* (2015) found positive effect of mowing, but it was quite small.

Our aim was to investigate methods to improve the competitive effect of subsidiary crops by management. The hypothesis was that cutting (fragmentation) of the rhizomes by making slits in the soil by a spade (spading) would increase the number of couch grass shoots, thus improve the effect of repeated mowing. A recently developed prototype, “Kverneland Vertical rhizome/root cutter” (tractor propelled), can make similar slits in field scale.

## 2 Materials and Methods

The hypothesis was tested in three two-factorial field experiments arranged in complete randomized blocks with four replicates. The effect of cutting vertical slits in the soil was tested by comparing treatments with cross-cutting (grid) vertical slits with a control and the effect of mowing was tested by comparing mowed and not mowed plots (Table 1). Experiments were established at Krusenberg, Uppsala, Sweden, in 2012 and 2013, and at Ås, Norway, in 2013, by sowing a pure stand of white clover with 10 kg ha⁻<fnref n="1" /> in May. The mowed treatments were mowed to 3-5 cm above soil surface when the couch grass had about three new leaves in the experiments at Krusenberg and slightly later in the experiment at Ås. The experiments at Krusenberg were mowed eight and seven times during 2012 and 2013, respectively, and the experiment at Ås was mowed four times. Immediately after the first mowing, 10 cm deep slits was made by a spade in a 20 cm * 20 cm or 10 cm * 10 cm cross cutting pattern according to treatment (Table 1). The number of couch grass shoots was counted in the 80 cm by 80 cm centre in each 1.0 m<fnref n="2" /> plot before first mowing and spading, before second mowing at Krusenberg and at final sampling in early October 2012 and late August 2013 at Krusenberg and in late October 2013 at Ås. The couch grass shoot and rhizome biomasses were sampled and dry weight determined at the final sampling. Data were log-transformed to equalize variance and analysed in accordance with the statistical design and with shoot numbers before first cutting as covariate using Model Mixed in SAS.

<table><caption>**Table 1.** Treatments used in six field experiments investigating the effect of cutting 10 cm deep slits in the soil (Cross Cutting (grid)) with a spade and mowing on couch grass</caption><thead><tr><th rowspan="2">Treatment</th><th colspan="2">Below ground weed control</th><th rowspan="2">Mowing</th></tr><tr><th>Cross Cutting</th><th>Distance between slits</th></tr></thead><tbody><tr><td>Control</td><td>No</td><td>-</td><td>No</td></tr><tr><td>C20</td><td>Yes</td><td>20 cm</td><td>No</td></tr><tr><td>C10</td><td>Yes</td><td>10 cm</td><td>No</td></tr><tr><td>M</td><td>No</td><td>-</td><td>Yes</td></tr><tr><td>MC20</td><td>Yes</td><td>20 cm</td><td>Yes</td></tr><tr><td>MC10</td><td>Yes</td><td>10 cm</td><td>Yes</td></tr></tbody></table>

<footnote n="1">¹ Dept. of Crop Production Ecology, Swedish University of Agricultural Sciences (SLU), P. O. Box 7043, SE-75007 Uppsala, Sweden</footnote>

<footnote n="2">² Plant Health and Plant Protection Division, Norwegian Institute for Agricultural and Environmental Research, N-1432 Ås, Norway</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: goran.bergkvist@slu.se</footnote>
## 3 Results – Discussion

Cross cutting reduced the amount of rhizomes at Krusenberg (P=0.003), but the effect was different at Ås (P<0.0001). At Ås, the amount of rhizomes was actually higher after cross cutting with the wide spacing than in the control or with narrow spacing between slits (P<0.05). Mowing reduced the amount of rhizomes at all sites (P<0.001), but the effect was similar with and without Cross cutting, which means that the effects of cross cutting and mowing were additive and that the mechanism was different from the hypothesized. Therefore, the hypothesis that Cross cutting increase the effect of mowing cannot be supported. The reason for the lack of interaction between the two factors could be that the number of shoots before mowing the second time, i.e about two weeks after first mowing and cross cutting, was lower than in the control at Krusenberg (data not presented), which was contrary to the hypothesis that Cross cutting would stimulate shooting and thereby increase the proportion of couch grass biomass cut of at mowing. Thus the control effect of cross cutting found at Krusenberg is probably an effect of damages to the rhizomes caused by the cross cutting procedure. The best effect of the cross cutting was found in the first year at Krusenberg, where cross cutting with 10 cm between the slits reduced the amount of rhizomes in late autumn compared to the control by as much as 60 %, on average (P<0.001). Repeated mowing was even more efficient and reduced the amount of rhizomes by more than 90 % (P<0.001). The combined effect was even greater. Cross cutting tended (P=0.13) to have an effect also at Krusenberg in 2013, but all effects at Krusenberg in 2013 were smaller than 2012. The reason for the smaller effect could be less soil moisture (data not presented) and therefore less growth in 2013 than 2012, but the difference could also be due to the shorter experimental period in 2013. The experimental period was shortened in 2013 compared to 2012, because a shorter fallow period would be beneficial for farmers that want to establish winter wheat after the fallow and because of the big effect of mowing in 2012 that almost wiped out the couch grass and made it difficult to evaluate the effect of cross cutting. We have not been able to interpret the stimulating effect of the cross cutting on couch grass at Ås.

<figure><img src="image_26.png" /><figcaption>**Fig.1.** Predicted amount of rhizomes in autumn depending on cross cutting (grid) 10 cm deep vertical slits (C) in the soil with a spade (10 cm by 10 cm = 10; 20 cm by 20 cm = 20) in spring and repeated mowing (M) during summer at a) Krusenberg in 2012, b) Krusenberg in 2013 and c) Ås in 2013.</figcaption></figure>

## 4 Conclusions

We conclude that Cross cutting to 10 cm could reduce the amount of rhizomes, but that the effect is variable. We also conclude that the cross cutting do not improve the effect of mowing, but that the effects are additive. Cross cutting reduce the amounts of couch grass shoots.

*Acknowledgements.* The work was funded by EU-FP7 as part of OSCAR (Optimizing subsidiary crop applications in rotations).

## References

* Askegaard, M., Olesen, J.E., Rasmussen, I.A. & Kristensen, K. (2011). Nitrate leaching from organic arable crop rotations is mostly determined by autumn field management. *Agriculture Ecosystems & Environment*, **142**, 149-160.

* Bergkvist, G., Adler, A., Hansson, M. & Weih, M. (2010). Red fescue undersown in winter wheat suppresses *Elytrigia repens*. *Weed Research* **50**, 447-455.

* Brandsæter, L.O., Goul Thomsen, M., Wærhus, K. & Fykse, H. (2012). Effects of repeated clover undersowing in spring cereals and stubble treatments in autumn on *Elymus repens*, *Sonchus arvensis* and *Cirsium arvense*. *Crop Protection*, **32**, 104-110.

* Dyke, G.V., Barnard, A.J. (1976). Suppression of Couch grass by Italian ryegrass and broad red-clover undersown in barley and field beans. *Journal of Agricultural Science, Cambridge*, **87**, 123-126.

* Håkansson S. (1969). Experiments with Agropyron repens (L.) Beauv. 4. Response to burial and defoliation repeated with different intervals. *Lanthrukhshögskolans annaler*, **35**, 989-1030.

* Ringselle, B., Bergkvist, G., Aronsson, H. & Andersson. L. (2015). Under-sown cover crops and post-harvest mowing as measures to control Elymus repens. *Weed Research*. DOI: 10.1111/wre.12144

* Tzilivakis, J., Jaggard, K., Lewis, K.A., May, M. & Warner, D.J. (2005). Environmental impact and economic assessment for UK sugar beet production systems. *Agriculture Ecosystems & Environment*, **107**, 341-358.
# Assessing environmental impacts of cropping systems: comparison of an indicator-based and a life-cycle analysis-based method

Christian Bockstaller ±<fnref n="1" />, Marine Marion-Wuillemin <fnref n="2" />,<fnref n="3" />, Nouraya Akkal-Corfini <fnref n="2" />,<fnref n="3" />, Nicolas Munier-Jolain <fnref n="4" /> & Michael S. Corson <fnref n="2" />,<fnref n="3" />

## 1 Introduction

Design and experimental testing of innovative cropping systems requires assessing their performances. When measurements of impacts are not possible, direct impacts have mainly been assessed at the field level using indicator-based methods (Bockstaller et al., 2008). Systemic assessment frameworks based on life-cycle analysis (LCA) have also been applied to cropping systems (Deytieux et al., 2012). LCA methods address direct and indirect impacts (e.g. linked to production and transport of farm inputs) and therefore are scientifically interesting, but they require more input data and calculation time. This paper aims to compare both approaches and discuss to what extent it is necessary to implement an assessment method based on LCA.

## 2 Materials and Methods

Fifteen cropping systems were selected for study from the DEPHY database, containing average cropping systems from farms of the French national network "Ecophyto DEPHY" (French action plan to reduce pesticide use (Ecophyto, 2015)). These cropping systems have crop-rotation lengths ranging from 1 (maize monoculture) to 9 (maize/cereals/3-4 years grassland), application rates of mineral nitrogen fertilizers of 0-250 kg N.ha⁻<fnref n="1" />, and pesticide treatment frequency indices of 0-5.3.

LCA methods implemented in the study were based on pollutant-emission models from the AGRIBALYSE framework, recently developed to support environmental labeling policies of agricultural products in France (Colomb et al., 2015). For the indicator-based method, INDIGO was chosen to estimate direct impacts at the field level (Bockstaller et al., 2008). We focused on the nitrogen-based emissions of nitrate (NO₃), ammonia (NH₃), and nitrous oxide (N₂O). They are estimated in the LCA method by models: the COMIFER approach for NO₃, EMEP/EEA 2009 Tier 2 (for organic fertilization) and EMEP/CORINAIR 2006 Tier 2 (for mineral fertilization) for NH₃ and IPCC 2006 Tier 2 for N₂O. For the INDIGO method, these emissions were estimated by the I_N indicator (Bockstaller et al., 2008). In both methods, a mineral nitrogen balance is calculated for the time from harvest to start of the drainage period at the beginning of winter to estimate nitrate leaching.

## 3 Results – Discussion

Among cropping systems, INDIGO tended to estimate more various nitrate leaching than LCA, and results of the two methods were weakly correlated (r = 0.18) (Fig. 1a). INDIGO tends to estimate slightly lower ammonia emissions than LCA, but results were strongly correlated (r= 0.88) (Fig. 1b). Estimates of nitrous oxide emissions were intermediate, with a weaker correlation than for ammonia but stronger than for nitrate (r = 0.55, data not shown).

<figure><img src="image_27.png" /><figcaption>**Fig. 1.** Comparison of (a) nitrate leaching and (b) ammonia emissions of INDIGO and LCA methods.</figcaption></figure>

According to the LCA method, eutrophication impacts were due almost completely to direct emissions (mainly phosphate and nitrate) from the crop field (Fig. 2a), while climate change impacts came mainly from production

<footnote n="1">¹ INRA, UMR LAE Nancy-Colmar, France</footnote>

<footnote n="2">² INRA, UMR SAS Rennes, France</footnote>

<footnote n="3">³ Agrocampus Ouest, UMR SAS, Rennes, France</footnote>

<footnote n="4">⁴ INRA, UMR AgroEcologie, Dijon, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: christian.bockstaller@colmar.inra.fr</footnote>
of mineral fertilizers, with the contribution of direct field emissions of nitrous oxide less than 30% (Fig. 2b). This highlights the utility of LCA in identifying the sources of impacts. Similar results can be found in the study of Deytieux *et al.* (2012).

<figure><img src="image_28.png" /><figcaption>**Fig. 2.**Contribution of production stages or inputs to (a) eutrophication and (b) climate change impacts estimated by LCA.</figcaption></figure>

## 4 Conclusions

This study shows that indicator-based and LCA-based methods can estimate similar magnitudes of emissions that contribute to potential impacts, such as eutrophication (or acidification), that are mainly determined by direct emissions from the crop field. Combining an indicator-based method with an LCA-based method could reduce the workload required to estimate direct field emissions in LCA. Such combination of methods will require data transfer between calculation tools to avoid entering the same data twice.

*Acknowledgements.* This work was supported by ONEMA (project PSPE SCEP-DEPHY). We thank the farmers and the engineers of the DEPHY network who provided the cropping system data.

## References

Bockstaller, C., Guichard, L., Makowski, D., Aveline, A., Girardin, P., and Plantureux, S. (2008). Agri-environmental indicators to assess cropping and farming systems. A review. *Agronomy for Sustainable Development* **28**, 139-149.

Colomb, V., Amar, S. A., Basset Mens, C., Gac, A., Gaillard, G., Koch, P., Mousset, J., Salou, T., Tailleur, A., and van der werf, H. M. G. (2015). AGRIBALYSE®, the French LCI Database for agricultural products: high quality data for producers and environmental labelling. *Oléagineux Corps gras Lipides* **22**.

Deytieux, V., Nemecek, T., Knuchel, R. F., Gaillard, G., and Munier-Jolain, N. M. (2012). Is Integrated Weed Management efficient for reducing environmental impacts of cropping systems? A case study based on life cycle assessment. *European Journal of Agronomy* **36**, 55-65.

Ecophyto (2015). http://agriculture.gouv.fr/DEPHY-Ecophyto-2. Accessed 9 Marsh 2015.
# Water productivity, Irrigation management and Systematization for Rice Farming Systems in Uruguay

Gonzalo Carracelas*±<fnref n="1" />, Claudia Marchesi<fnref n="1" /> & Andrés Lavecchia<fnref n="2" />

## 1 Introduction

A high proportion of water inputs for rice crop irrigation in the Central Region of Uruguay comes from rainfall water stored in dams. Maximizing water productivity is important as savings in water inputs would reduce the costs of pumping irrigation, increase annually sown rice area, allows to allocate water to irrigate other crops in a rotation and contribute to reduce the impact of farming systems on Water Footprint (Chapagain & Hoekstra, 2011) and to reduce environmental impact based upon Life Cycle Assessment, energy and water analyses (Thanawong *et al.*, 2014). The aim of this experiment is to determine irrigation management practices and systematization field layout techniques that increase water productivity (WP), contemplating the economic and environmental sustainability of rice farming systems in Uruguay.

## 2 Materials and Methods

A split plot experimental design trial was conducted in the Experimental Unit located in Tacuarembó (32.18S, 55.17W). Treatments included two types of systematization with different vertical interval between levees (big plots)l: I. Conventional (VI-8cm) and II. Alternative (VI-4cm) and three irrigation management practices (small plots): 1.Continuous (C), 2.Intermittent until panicle initiation (IP), and 3.Intermittent during all crop cycle (I). In C a water layer of 10cm is maintained after flooding throughout all the crop cycle. In IP and I the water layer alternates between 10 and 0cm and is re-established when the soil is still saturated. Irrigation started 30 days after emergence and finished 20 days before harvest date. Crop was direct drilled on 1th, 16th and 19th October with 160 kg seed/ha with cultivar INIA Olimar (Indica type). Basal fertilization was 160Kg / ha of 19-19-19 (N-P-K) and Urea was 100 kg / ha fractionated at tillering and panicle initiation. The results of the joint analysis of the previous three seasons (2012-2013-2014) were evaluated by analysis of variance and mean separation test of Fisher 5% using statistical package InfoStat (www.infostat.com.ar).

## 3 Results – Discussion

Intermittent irrigation systems led to significant water inputs savings in relation to continuous irrigation C, 2041 and 3554 m3 water ha⁻<fnref n="1" /> less for IP and I respectively (Fig. 1 a,b) (P<0.05). The systematization did not determine significant differences in water input (Fig. 1 c,d) (P<0.05).

<figure><img src="image_29.png" /><figcaption>**Fig. 1.** Irrigation Water Input and Total Water Input (Irrigation plus Rainfall) for different irrigation systems and systematization (field layout techniques), Tacuarembó, Uruguay, (average seasons 2011-12, 2012-13 and 2013-14).</figcaption></figure>

<footnote n="1">¹ Rice Program, Agricultural National Research Institute (INIA) - Road 5, km 386, Tacuarembó, Uruguay.</footnote>

<footnote n="2">² Rice Program, Agricultural National Research Institute (INIA) - Road 5, km 386, Tacuarembó, Uruguay. Until Sep 2011, then Farmers Consultant.</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: gcarracelas@tb.inia.org.uy</footnote>
The highest WPi (irrigation) and WPt (irrigation plus rainfall) were recorded in treatment I, being 2.0 and 0.88 kg grain m³ water⁻¹ respectively. These values are higher than the data reported worldwide where WPt of rice in Asia ranges from 0.2 to 1.2 kg grain m³ water⁻¹, with 0.4 as the average value (Tuong *et al.*, 2005).

There were no differences in rice grain yield between irrigation treatments (P< 0.05) (Table 1). Similar results in the same region and comparable type of soils were registered by Lavecchia *et al.*, 2011. This results are explained because the soils on which the experiments were performed (planosols) have a low infiltration rate and rainfall was above the historical average throughout the crop cycle, 738 mm (from October to March). In analogous experiments conducted on soils with a higher infiltration rate in a different region (North), the intermittent irrigation determined a rice yield loss of 950 kg in relation to continuous flooding (Carracelas *et al.*, 2014).

<table><caption>**Table 1.** Rice Yield, Grain Quality and Water Productivity compared with three irrigation systems and two types of systematization, Tacuarembó, Uruguay (average seasons 2011-12, 2012-13 and 2013-14).</caption><thead><tr><th rowspan="2">Site= Central Región, Tacuarembó.</th><th rowspan="2">Rice Yield<br>(kg ha<sup>-1</sup>)</th><th colspan="2">Industrial Quality</th><th colspan="2">Water Productivity (WP)<br>kg grain m<sup>3</sup> water<sup>-1</sup></th></tr><tr><th>White<br>Grain %</th><th>Whole Grain<br>%</th><th>WPi-<br>Irrigation</th><th>WPt -<br>Irrigation +<br>Rainfall</th></tr></thead><tbody><tr><td><strong>Irrigation Systems</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>1. Continuous (C)</td><td>7850</td><td>69.22</td><td>62.73 a</td><td>0.99 c</td><td>0.52 c</td></tr><tr><td>2. Intermittent until panicle initiation (IP)</td><td>7446</td><td>69.17</td><td>62.17 ab</td><td>1.31 b</td><td>0.57 b</td></tr><tr><td>3. Intermittent during all crop cycle (I)</td><td>7843</td><td>69.08</td><td>61.94 b</td><td>2.00 a</td><td>0.68 a</td></tr><tr><td>LSD (P&lt;0.05)</td><td>NS</td><td>NS</td><td>0.63</td><td>0.17</td><td>0.04</td></tr><tr><td><strong>Systematization</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>I. Conventional - VI=8cm</td><td>7735</td><td>69.2</td><td>62.61</td><td>1.57</td><td>0.60</td></tr><tr><td>II. Alternative - VI= 4cm</td><td>7691</td><td>69.1</td><td>61.95</td><td>1.30</td><td>0.57</td></tr><tr><td>LSD (P&lt;0.05)</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td><td>NS</td></tr><tr><td>CV %</td><td>12.12</td><td>0.71</td><td>1.95</td><td>22.44</td><td>12.16</td></tr></tbody></table>

Means followed by different letters are significantly different with a probability less than 5% (P < 0.05). LSD: least-square difference. NS: non-significant differences. CV: coefficient of variation.

In relation to Industrial quality, continuous irrigation C determined a higher percentage of whole grain in comparison with intermittent irrigation I and no differences in quality with IP treatment (P<0.05) (Table 1).

## 4 Conclusions

The intermittent irrigation in low-infiltration rate soils, allowed for significant savings in water input of 35% on average without reducing rice grain yield relative to continuous irrigation, thus determining a significant increase in water productivity (P <0.05).

In relation to industrial quality, intermittent irrigation (I) determined a lower percentage of whole grain in relation to continuous irrigation C but with no differences in white grain percentage (P <0.05).

There were no significant differences in water input, grain yield, industrial quality and water productivity between the different systematizations-field layouts treatments (P <0.05).

Implementing crop irrigation systems involving savings in water input means a greater risk and would only be adopted by farmers on a larger scale if they determine more or equal rice yield per hectare without affecting the grain quality.

*Acknowledgements.* We would like to thank all farm field staff who provided their time and valuable effort to run the experiments. We also gratefully acknowledge Gonzalo Zorrilla (National Rice Program Director) for his contribution to this paper and valuable advice.

## References

Carracelas, G., Marchesi, C. & Lavecchia, A. (2014). Manejo del cultivo: Riego. In: *Presentación resultados experimentales de arroz: Zafra 2013-2014*, INIA Tacuarembó, Uruguay. SAD 738, 25-28.

Chapagain, A. & Hoekstra, A.Y. (2011). The blue, green and grey water footprint of rice from production and consumption perspectives. *Ecological Economics*, **70**, 749-758.

Lavecchia, A., Marchesi, C. & Casanova, S. (2011). Manejo del cultivo: Riego. In: *Presentación resultados experimentales de arroz: Zafra 2010-2011*, INIA Tacuarembó, Uruguay. SAD 652, 1-7.

Thanawong, K., Perret S.R. & Basset-Mens C. (2014). Eco-efficiency of paddy rice production in Northeastern Thailand: a comparison of rain-fed and irrigated cropping systems. *In: Journal of Cleaner Production* (2014) 1-14.

Tuong, T.P., Bouman, B.A.M. & Mortimer, M. (2005). More rice, less water: integrated approaches for increasing water productivity in irrigated rice-based systems in Asia. *Plant Prod. Sci.* **8**,231-241
# Phosphorus offtake by silage maize and cut grassland in temperate regions

Karoline D'Haene *±1 & Georges Hofman 2

## 1 Introduction

Long term phosphorus (P) overfertilisation have led to a large acreage of P saturated soils, high fractions of P in solution and increased diffuse P losses into ground- and surface water. In some European countries agriculture has become the main P source in water bodies (Bogestrand *et al.*, 2005). Phosphorus losses are a major factor in eutrophication of surface waters and in a reduction of regulating and cultural ecosystem services as P is typically the limiting factor of algae blooming in freshwater systems (Sterner, 2008).

Phosphorus fertilisation advice should envisage an equilibrium maintenance phosphate (P₂O₅) fertilisation rate for fields with an optimal soil P content, with differentiation for other fields depending on their soil P content. In addition to a reduction of environmental pollution due to a more P efficient use, also the limited P₂O₅ rock reserve and the increasing price of mineral P₂O₅ fertiliser stimulate closing the P cycle. Up to date information of P₂O₅ removed by silage maize (*Zea mays*) and cut grassland (*Poaceae*) is needed as scientifically sound fertilisation advice takes both optimal crop yield and quality and environmental impact into account.

## 2 Materials and Methods

We re-analysed 26 and 14 Flemish nitrogen (N) fertilisation experiments (1996-2013) on silage maize and cut grassland without clover, respectively, to derive their P₂O₅ offtake. To preclude an effect of the applied N amount, only plots receiving the maximum allowed N fertilisation norm of Manure Action Plan (MAP) IV ±25%, meaning plots fertilised with 100-200 and 225-375 kg effective N ha⁻<fnref n="1" /> for silage maize and cut grassland, respectively (Anonymous, 2011), were included in the analysis. Phosphorus application rates were based on a fertilisation advice dependent on the expected yield. Total fresh and dry matter (FM and DM) yields were determined and P concentration of the harvested plant parts was measured colorimetrically after digestion and acid decomposition. The soil P content of the upper soil layer was determined with ammonium lactate (P-AL method).

## 3 Discussion

The P₂O₅ removed by silage maize in Flanders (1996-2013) had a median of 86 kg P₂O₅ ha⁻<fnref n="1" /> with a median 0.7 and 2.1 g P kg⁻<fnref n="1" /> on a FM and DM basis, respectively (Table 1). Regression analysis showed no correlation between P₂O₅ offtake by silage maize and soil P-AL content and/or P₂O₅ fertilisation rate. There was a significant correlation (R<fnref n="2" /> = 0.47) between P₂O₅ offtake and yield. The median P₂O₅ offtake by silage maize increased significantly (P < 0.001 nonparametric Mann-Witney U-test). The increase of the median P₂O₅ offtake from 78 in 1996-1997 to 94 kg P₂O₅ ha⁻<fnref n="1" /> in 2003-2013 can be explained by the higher yield (median of 16.8 and 18.9 Mg DM ha⁻<fnref n="1" /> in 1996-1997 and 2003-2013, respectively) as the P concentration didn't change.

<table><caption>**Table 1.** Average, median and standard deviation (stdev.) of phosphorus and effective nitrogen fertilisation rate (kg P₂O₅ and N ha⁻<fnref n="1" />), phosphorus content in the soil (mg P-AL 100g⁻<fnref n="1" /> soil), dry matter yield (Mg DM ha⁻<fnref n="1" />), phosphorus concentration (g P kg⁻<fnref n="1" /> DM) and phosphorus offtake (kg P₂O₅ ha⁻<fnref n="1" />) by silage maize in Flanders.</caption><thead><tr><th rowspan="2"></th><th colspan="3">119 plots (1996-2013)</th><th colspan="3">73 plots (1996-1997)</th><th colspan="3">46 plots (2003-2013)</th></tr><tr><th>Average</th><th>Median</th><th>Stdev.</th><th>Average</th><th>Median</th><th>Stdev.</th><th>Average</th><th>Median</th><th>Stdev.</th></tr></thead><tbody><tr><td>P₂O₅ fertilisation (kg ha⁻<fnref n="1" />)</td><td>67</td><td>65</td><td>25</td><td>66</td><td>69</td><td>21</td><td>67</td><td>60</td><td>30</td></tr><tr><td>Effective N fertilisation (kg ha⁻<fnref n="1" />)</td><td>140</td><td>138</td><td>26</td><td>141</td><td>138</td><td>28</td><td>138</td><td>135</td><td>22</td></tr><tr><td>P-AL (mg 100g⁻<fnref n="1" /> soil)</td><td>34</td><td>30</td><td>15</td><td>35</td><td>33</td><td>11</td><td>32</td><td>29</td><td>21</td></tr><tr><td>Dry matter yield (Mg ha⁻<fnref n="1" />)</td><td>18.1</td><td>17.7</td><td>2.7</td><td>17.4</td><td>16.8</td><td>2.6</td><td>19.3</td><td>18.9</td><td>2.5</td></tr><tr><td>Phosphorus (g kg⁻<fnref n="1" /> DM)</td><td>2.1</td><td>2.1</td><td>0.4</td><td>2.1</td><td>2.1</td><td>0.4</td><td>2.1</td><td>2.1</td><td>0.3</td></tr><tr><td>P₂O₅ offtake (kg ha⁻<fnref n="1" />)</td><td>87</td><td>86</td><td>21</td><td>83</td><td>78</td><td>23</td><td>95</td><td>94</td><td>14</td></tr></tbody></table>

The P concentration in Flanders falls within the range of other temperate regions i.e. 1.8 g P kg⁻<fnref n="1" /> DM in the Netherlands and France (Ehler *et al.*, 2009; Gloria, 2012) and 2.2-2.3 g P kg⁻<fnref n="1" /> DM (2008-2013) in northern Germany (Egert, 2014). Fotyma & Shepherd (2001) measured in Northern and Eastern Europe on average 0.6±0.009 g P kg⁻<fnref n="1" /> FM (or 2.0 g P kg⁻<fnref n="1" /> DM). The median Flemish P₂O₅ offtake (86 kg P₂O₅ ha⁻<fnref n="1" />) is higher than in other temperate regions. In France and Saskatchewan (Canada) fertilisation advice is based on an offtake of 60 (Gloria, 2012) and 64 to 78 kg P₂O₅ ha⁻<fnref n="1" />

<footnote n="1">¹ Plant Sciences Unit, Institute for Agricultural and Fisheries Research (ILVO)</footnote>

<footnote n="2">² Department of Soil Management, Faculty of Bioscience Engineering, Ghent University</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: karoline.dhaene@ilvo.vlaanderen.be</footnote>
(Anonymous, 2012), respectively. In the UK, maintenance fertilisation advice is 55 kg P₂O₅ ha⁻¹ (Defra, 2010). Aarts et al. (2008) calculated an average offtake of 69 kg P₂O₅ ha⁻¹ from fields of representative Dutch dairy farms (1998-2006) based on an average 2.0 g P kg⁻¹ DM measured by BLGG. The P₂O₅ offtake varied from on average 56 from wet sandy soils in 1998 to on average 78 kg P₂O₅ ha⁻¹ from clay soils in 2006 (Aarts et al., 2008).

The median P₂O₅ removed by Flemish cut grassland was about 110 kg P₂O₅ ha⁻¹ with a median P concentration of 4.1 g P kg⁻¹ DM (Table 2). Regression analysis showed no correlation between P₂O₅ offtake and soil P-AL content and/or P₂O₅ fertilisation rate. There was a significant correlation (R² = 0.50) between P₂O₅ offtake and yield. The limited data from recent years suggest that the lower P₂O₅ fertilisation rate (90 compared to 116 kg P₂O₅ ha⁻¹) has resulted in an insignificant decrease of the average P concentration (P = 0.06 T-test). The median P concentration decreased from 4.2 g P kg⁻¹ DM to 3.9 g P kg⁻¹ DM. The average P concentrations of Dutch grass silage measured by BLGG decreased from 4.4 in 1998 to 4.0 g P kg⁻¹ DM in 2007, which was also explained by the stricter legislation (Aarts et al., 2008). Although the median N fertilisation rate was 50 kg effective N ha⁻¹ lower in 2003-2008 compared to 1997-1998, DM grass yield remained constant.

The Flemish P concentration of cut grassland falls within the range of measurements from intensively managed grassland under temperate climate i.e. 3.3 g P kg⁻¹ DM in Wallonia (Mathot et al., 2009), 3.3-3.6 g P kg⁻¹ DM (2008-2013) in northern Germany (Egert, 2014), 3.1-4.1 g P kg⁻¹ DM in UK (Defra, 2010) and 3.5-4.4 g P kg⁻¹ DM in the Netherlands (Aarts et al., 2008; Ehlert et al., 2009). Fotyma & Shepherd (2001) measured on average 0.6 g P kg⁻¹ FM (or 3.0 g P kg⁻¹ DM) in grassland with and without clover. The median Flemish P₂O₅ offtake is higher than other temperate regions, due to higher yields. In the UK, maintenance fertilisation advice is 90 kg P₂O₅ ha⁻¹ (Defra, 2010). Aarts et al. (2008) calculated an average offtake of 89 kg P₂O₅ ha⁻¹ from fields of Dutch representative dairy farms (1998-2006). The P₂O₅ offtake varied from on average 76 kg P₂O₅ ha⁻¹ (wet sandy soils in 2006) to 110 kg P₂O₅ ha⁻¹ (dry sandy soils in 1998) (Aarts et al., 2008).

<table><caption>**Table 2.** Average, median and standard deviation (stdev.) of phosphorus and effective nitrogen fertilisation rate (kg P₂O₅ and N ha⁻¹), phosphorus content in the soil (mg P-AL 100g⁻¹ soil), dry matter yield (Mg DM ha⁻¹), phosphorus concentration (g P kg⁻¹ DM) and phosphorus offtake (kg P₂O₅ ha⁻¹) by cut grassland in Flanders.</caption><thead><tr><th rowspan="2"></th><th colspan="3">56 plots (1997-2008)</th><th colspan="3">44 plots (1997-1998)</th><th colspan="3">12 plots (2003-2008)</th></tr><tr><th>Average</th><th>Median</th><th>Stdev.</th><th>Average</th><th>Median</th><th>Stdev.</th><th>Average</th><th>Median</th><th>Stdev.</th></tr></thead><tbody><tr><td>P₂O₅ fertilisation (kg ha⁻¹)</td><td>114</td><td>116</td><td>24</td><td>119</td><td>116</td><td>23</td><td>98</td><td>90</td><td>20</td></tr><tr><td>Effective N fertilisation (kg ha⁻¹)</td><td>289</td><td>296</td><td>42</td><td>296</td><td>300</td><td>43</td><td>262</td><td>250</td><td>24</td></tr><tr><td>P-AL (mg 100g⁻¹ soil)</td><td>28</td><td>27</td><td>11</td><td>29</td><td>37</td><td>12</td><td>23</td><td>22</td><td>8</td></tr><tr><td>Dry matter yield (Mg ha⁻¹)</td><td>12.0</td><td>11.9</td><td>2.1</td><td>11.8</td><td>11.9</td><td>1.5</td><td>12.9</td><td>12.3</td><td>3.5</td></tr><tr><td>Phosphorus (g kg⁻¹ DM)</td><td>4.0</td><td>4.1</td><td>0.6</td><td>4.1</td><td>4.2</td><td>0.6</td><td>3.7</td><td>3.9</td><td>0.4</td></tr><tr><td>P₂O₅ offtake (kg ha⁻¹)</td><td>111</td><td>111</td><td>26</td><td>111</td><td>111</td><td>27</td><td>110</td><td>109</td><td>21</td></tr></tbody></table>

## 4 Conclusions

To fine-tune the P₂O₅ fertilisation rate, it is important that legislation and P₂O₅ fertilisation advice not only takes into account the soil P content but also the P₂O₅ offtake by agricultural crops as scientifically sound fertilisation advice accounts for optimal yield and quality, soil P content and water quality objectives. Re-analysis of Flemish N fertilisation experiments shows that the median P₂O₅ offtake by silage maize increased significantly from 78 kg P₂O₅ ha⁻¹ in the last decennium of 20th century to 94 kg P₂O₅ ha⁻¹ in recent years with a median of 2.1 g P kg⁻¹ DM due to the increased yield. The median P₂O₅ removed by cut grassland was about 110 kg P₂O₅ ha⁻¹ with a median of 4.1 g P kg⁻¹ DM.

*Acknowledgements.* We would like to thank Soil Service of Belgium, Inagro, ILVO, HoGent and Ghent University for the field information.

## References

Anonymous (2011). Decreet houdende wijziging van het Mestdecreet van 22 december van 2006. Belgisch Staatsblad, 27876-27894.

Aarts, H.F.M., Daatselaar, C.H.G. & Holshof, G. (2008). Bemesting, meststofbenutting en opbrengst van productiegrasland en snijmaïs. PRI, Wageningen.

Anonymous (2012). Phosphorus Fertilization in Crop Production. http://www.agriculture.gov.sk.ca/

Bogestrand, J., Kristensen, P. & Kronvang, B. (2005). Source apportionment of nitrogen and phosphorus inputs into the aquatic environment. European Environment Agency, Copenhagen.

Defra (2010). Fertiliser Manual (RB209). Department for Environment, Food and Rural Affairs (Defra), London.

Egert, M. (2014). Mineralstoffgehalte in silagen. http://www.lufa-nord-west.de/index.cfm/article/1743.html

Ehlert, P.A.I., Dekker, P.H.M., Schoot, J.R, Visschers, R., Middelkoop, J.C, Maas, M.P, Pronk, A.A. & Dam, A.M. (2009). Fosforgehalten en fosfaatafvoercijfers van landbouwgewassen: eindrapportage. Alterra, Wageningen.

Fotyma, G. & Shepherd, M. (2001). Sustainable nutrient management, MAINTAIN. Pulawy.

Gloria, C. (2012). La recherche s'active sur le phosphore. http://grandes-cultures.reussir.fr/actualites/fertilisation-la-recherche-s-active-sur-le-phosphore:N56NEE6K.html

Mathot, M., Vermeiren, E. & Lambert, R. (2009). Indices de nutrition minérale et ensilage d'herbes: évaluation et validation de leur utilisation pour la détection des déficiences en prairies. Université catholique de Louvain, Louvain-la-Neuve. www.fourragesmieux.be

Sterner, R.W. (2008). On the phosphorus limitation paradigm for lakes. *International Review of Hydrobiology*, **93**(4-5), 433-445.
# The effect of bio-phosphate fertilizer to increasesoil phosphate, growth and yield of maize and P fertilizer efficiency on marginal soil

Betty Natalie Fitriatin *±1, Anny Yuniarti <fnref n="1" /> & Tien Turmuktini <fnref n="2" />

## 1 Introduction

Phosphorus (P) is one of the macro-nutrients for plants that have an important role in plant growth. There are several problems that make the element P in the soil becomes unavailable to plants, especially on marginal soils such as Ultisols with high pH (>7). This soil has disadvantages in its use, among others, have physical, chemical and biological less support plant growth. pH values usually acid, and the amount of nutrients, especially Pare low due to fixation of P is a constraint on the growth of plants. Soil with high pH has a problem low available soil P content due to fixation by soil calcium (Tan, 2008).

There have been many efforts made to improve productivity Ultisols with fertilization into the soil. However, many obstacles are encountered with this artificial fertilization. One of them is the residual effect of fertilizers that can pollute the environment, so that a continuous fertilization will affect not good for the soil and the environment.

To improve fertilizer efficiency and reduce costs, it is necessary to develop biotechnology ground. Some free-living microbes in the soil has the ability to dissolve P soil is bound to be available, so that the plant can absorb P to make ends meet (Lambers et al., 2006). Phosphate solubilizing microbes (PSM) is a group of soil microbes that have the ability to extract P from its binding with Al, Fe, Ca, and Mg, so as to dissolve P whose origin is not available to the plant becomes available to plants. This happens because these microbes secrete organic acids that can form stable complexes with cations binding P in the soil microbes that play a role in the process of dissolving phosphorus, among others, oft he group of bacteria: *Pseudomonas*, *Bacillus*, *Mycobacterium*, *Micrococcus* called "phosphobacteria", while the group of fungi: *Penicillium*, *Aspergillus*, *Fusarium*, *Sclerotium* (Whitelaw, 2000).

Microbial groups phosphate solvent has many virtues in influencing plant growth, in addition to releasing the fixed P also can produce the enzyme phosphatase (Saparatka, 2003; Yadav and Tarafdar, 2003) and can produce phytohormones (Fitriatin and Simarmata. 2008; Fitriatin et al., 2013). Phosphatase enzyme released by these microbes can mineralise organic P into P inorganic. (George et al., 2002; Saparatka, 2003).

## 2 Materials and Methods

The experiment was conducted at the experimental farm of the Faculty of Agriculture, University of Padjadjaran in Ciparanje Jatinangor, Sumedang regency, West Java, which belong to the order Ultisol. Materials used in this study were (1) of hybrid corn seeds (Zea mays L.) (2) Phosphate solubilizing bacteria: *Pseudomonas mallei* and *Pseudomonas cepacea*; Phosphate solubilizing fungi: *Penicillium* sp. and *Aspergillus* sp. (4) Nutrient Broth (NB) and Potato Dextrose Agar (PDA) (5) Urea, KCl, super phosphate (SP) and rock phosphate, (6) cow manure as fertilizer base, (7) Carrier (compost and peat in the ratio 1: 1).

The experimental design used is a randomized block design with nine treatments and three replications to test how applications of bio-phosphate (1,2 and 3 times application) as well as the type of P fertilizer (super phosphate and rock phosphate with 100% and 50% doses of recommendation). The land area used is 3 m × 2 m per plot and use spacing of 70 cm × 30 cm. So that in each plot contained approximately 30 plants. Fertilizer applied as basal fertilizer is organic fertilizer and inorganic fertilizer. Organic fertilizers such as cow manure mixed together inoculants were incubated 2 days before planting as much as 2 t ha⁻<fnref n="1" />. Inorganic fertilizer used is urea (300 kg ha⁻<fnref n="1" />), and KCl (100 kg ha⁻<fnref n="1" />). Super phosphate fertilizer was given according to each treatment.

## 3 Discussion

The results showed that biophosphate application time and the type of fertilizer P was not significantly different effect on the available soil P (Table 1). Soil acidity greatly affects the availability of P. According to Tan (2008), states that the optimal pH range that supports the availability of P between 5.5 and 7.0 it is supported by the results of the analysis of soil that has a pH of 5.84 which is quite a bit acid. Based on Table 1, it can be seen that the fertilization of super phosphate with 50% and bio-phosphate with one application has the potential to increase the available soil P in comparison with the others, namely P is available for 21.41 mg kg⁻<fnref n="1" /> (an increase of 104.8%). This is presumably because of SP-36 has the properties of fast release and phosphorus contained in fertilizers SP has been in the form available to plants. Treatment type of rock phosphate fertilizer dose of 50% and the provision of PS Moneal

<footnote n="1">¹ Soil Science Department, Agriculture Faculty of Padjadjaran University, Indonesia</footnote>

<footnote n="2">² Agriculture Faculty of Winayamukti University, Indonesia</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: fitriatin@yahoo.com</footnote>
so able to increase 55.12% p-available compared to the control. It is proved that the administration of one biological fertilizer is sufficient to increase the P-available in the soil.

<table><caption>Table1. Effect of bio-phosphate application and type of P fertilizer on soil P and yield of Maize</caption><thead><tr><th>Treatments</th><th>Available P (mg kg<sup>-1</sup>)</th><th>P soil potential (mg 100 g<sup>-1</sup>)</th><th>Yield of maize (kg plot<sup>-1</sup>)</th></tr></thead><tbody><tr><td>control (without fertilizer P and biophosphate)</td><td>10,45 a</td><td>9,07 a</td><td>296,87</td></tr><tr><td>super phosphate 100 kg ha<sup>-1</sup></td><td>16,12 ab</td><td>10,85 ab</td><td>318,53</td></tr><tr><td>rock phosphate 300 kg ha<sup>-1</sup></td><td>10,64 ab</td><td>11,49 ab</td><td>307,93</td></tr><tr><td>super phosphate 50 kg ha<sup>-1</sup> + bio-phosphate with 1 times</td><td>21,41 b</td><td>19,56 c</td><td>327,13</td></tr><tr><td>super phosphate 50 kg ha<sup>-1</sup> + bio-phosphate with 2 times</td><td>14,34 ab</td><td>16,02 bc</td><td>343,13</td></tr><tr><td>super phosphate 50 kg ha<sup>-1</sup> + bio-phosphate with 3 times</td><td>11,60 ab</td><td>16,92 bc</td><td>308,87</td></tr><tr><td>rock phosphate 150 kg ha<sup>-1</sup> + bio-phosphate with 1 times</td><td>16,21 ab</td><td>16,48 bc</td><td>292,40</td></tr><tr><td>rock phosphate 150 kg ha<sup>-1</sup> + bio-phosphate with 2 times</td><td>5,26 a</td><td>12,64 abc</td><td>309,53</td></tr><tr><td>rock phosphate 150 kg ha<sup>-1</sup> + bio-phosphate with 3 times</td><td>7,86 ab</td><td>14,48 abc</td><td>318,20</td></tr></tbody></table>

Statistical analysis showed that a bio-phosphate application and type of fertilizer P no real influence on yield of maize in Ultisol (Table 1). The result of experiment showed that application of bio-phosphate yield of maize up to 15.58% on Ultisols. This is presumably due to the provision of PSM can increase soil P content due to the dissolution mechanism P caused the organic acids of the PSM (Fitriatin et al., 2013). According to Rao. (1994) due to the level of grow than dsoil microbial activity is influenced by abiotic factors that include the physical and chemical properties of soil and biotic factors (presence of other microbes and absorption of higher plants).

## 4 Conclusions

Based on the experimental results it can be concluded that the application of the PSM inoculant as bio-phosphate (*Pseudomonas mallei*, *Pseudomonas cepacea*, *Penicillium sp.* and *Aspergillus sp.*) increased the content of soil phosphate growth and yield of maize. The application of bio-phosphate in the early planting has been able to increase the P content of the soil and plants. Bio-phosphate fertilizer with super phosphate as P fertilizer with dose 50 % recommendation gave better effect on soil P and yield of maize on marginal soil.

## References

* Fitriatin, B.N., B. Joy dan T. Subroto, 2008. The Influence of organic phosphorous substrate on phosphatase activity of soi microbes. International Seminar of Chemistry.30-31 October 2008, Indonesia.

* Fitriatin, B.N., D.H. Arief, T. Simarmata, D.A. Santosa, and B. Joy. 2011. Phosphatase-Producing Bacteria Isolated from Sanggabuana Forest and Their Capability to Hydrolyze Organic Phosphate. Journal of Soil Science and Environmental Management Vol. 2(10), pp. 299-303

* Fitriatin, B.N., A. Yuniarti, T.Turmuktini and M. Saman. 2013. Effect of P solubilizing microbe producing growth regulators to increase solubilizing of soil phosphate and yield of maize on marginal soil. Soil-Water Journal. Vol 2. Number 2 (1). pp. 547-554.

* George, T.S., P.J. Gregory, M. Wood, D. Read, R.J. Buresh. 2002. Phosphatase activity and organic acids in the rhizosphere of potential agroforestry species and maize. Soil Biology and Biochemistry34: 1487-1494.

* Lambers, H., M.W. Shane, M. Cramer, S.J Pearse and E.J. Veneklaas. 2006. Root Structure and Functioning for Efficient Acquisition of Phosphorus: Matching Morphological and Physiological Traits. Annals of Botany 98: 693-713.

* Rao, Subba. 1994. Soil Microorganisms and Plant Growth. Terjemahan.PenerbitUniversitas Indonesia

* Saparatka, N. 2003.Phosphatase activities (ACP, ALP) in Agroecosystem Soils. Doctoral thesis. Swedish University of Agricultural Sciences. Uppsala. diss-epsilon.slu.se/archive/00000286/01/Agraria 396 Docutech Tryckfil

* Tan, K.H. 2008.Soils in the Humid Tropics and Monsoon Region of Indonesia.CRC Press.Taylor and Francis Group.Boca Raton London New York.

* Timmusk, S., B. Nicander, U. Granhall, E. Tillberg.1999. Cytokinin Production by *Paenibacilluspolymyxa*. Soil Biologi and Biochemistry 31 (1999) 1847-1852.

* Whitelaw. 2000. Growth promotion of plants inoculated with phosphate solubilizing fungi. Adv. Agron. 69: 99-151.

* Yadaf, R.S. and J.C. Taradar. 2003. Phytase and phosphatase producing fungi in arid and semi-arid soils and their efficiency in hydrolyzing different organic P compounds. Soil Biology and Biochemistry 35: 1-7.
# Effect of green manure on chemical, physical of soil and yield of millet

Choon-woo Lee ±<fnref n="1" />, Min-tae Kim <fnref n="2" />, Jin-heeRyu <fnref n="2" />, Jong-seo Choi <fnref n="2" />, Min-tae Kim <fnref n="2" />, Suk-jin Kim <fnref n="2" />, Kang-sup Kim <fnref n="2" /> & Ki-do Park<fnref n="2" />

## 1 Introduction

The green manure incorporation is the environmental friendly agriculture cultivation technologies which could reduced the use of chemical fertilizer, micro-flora of soil. The leguminous green manure which is incorporated in soil would fix the N2, and release the fixed nitrogen to soil. The hairy vetch and green barley are the winter survival crop and the higher biomass among green manure corps, and hairy vetch has the fixation nitrogen ability. The use of living mulches required new cropping system (Feil, 2001), and suppressed weed occurrence (Lee *et al.*, 2011). It was reported that the effect of green manures which were incorporated in soil were evaluated the growth and yield of millet, and the physical characteristics of soil.

## 2 Materials and Methods

There were 3 green manure crops treatments which are hairy vetch, green barley, hairy vetch + green barley, and 1 control treatment which is chemical fertilizer. Millet is only cultivated green manure, excepted chemical fertilizer plot. Hairy vetch, green barley, hairy vetch + green barley were incorporated in soil, and plowed and incorporated under soil depth 25~30cm, and then soil was prepared with rotary tillage. The green manure and the chemical nitrogen fertilizer were incorporate to soil May 16. Millet was planted at two weeks after incorporation of green manures and chemical fertilizer. Stalk height was measured at milky stage. Chlorophyll contents were collected using SPAD (Minolta corp., Japan) were taken at central point between the margin and the mid-rib from era leaves of 30 millet plants. The characteristics of grain were measured from 30 plants at physiological maturity when black layer was formed below kernel. Grain yield were adjusted to 15% moisture content. Chemical and physical characteristics of soil were measured bulk density, rate of liquid, air, solid and void state after harvesting.

## 3 Results – Discussion

Fresh biomass of green manure was harvested before 15days of transplanting and weighed in 3 square meters (3 replications). The amount of hairy vetch, barley and hairy vetch mixed barley were incorporated in soil (Table 1). Fresh weight of hairy vetch, barley and hairy vetch + barley were 12.9, 31.5 and 22.8 t ha⁻<fnref n="1" />, respectively. So, nitrogen rate of hairy vetch, barley, and hairy vetch +barley were 0.829, 0.339 and 0.469 %, respectively.

<table><caption>Table 1. Fresh weight, nitrogen rate and N supply of hairy vetch and barley at soil incorporation as green manure.</caption><thead><tr><td>Green manure crops</td><td>Date</td><td>Fresh weight (t ha<sup>-1</sup>)</td><td>N % of Fresh manure</td><td>N supply (t ha<sup>-1</sup>)</td></tr></thead><tbody><tr><td>Hairy vetch</td><td>May 16</td><td>12.90</td><td>0.829</td><td>0.107</td></tr><tr><td>Barley</td><td>May 16</td><td>31.52</td><td>0.339</td><td>0.107</td></tr><tr><td>Hairy vetch + barley</td><td>May 16</td><td>22.78</td><td>0.469</td><td>0.107</td></tr><tr><td>Chemical fertilizer</td><td>May 16</td><td>-</td><td>-</td><td>0.100</td></tr></tbody></table>

Chlorophyll contents collected using SPAD was measured at three growth stages, booting, heading and maturity stages (Table 2). Chlorophyll contents of booting and heading stage were not different among treatments. But at maturity stage, the range of SPAD were in 38.0~62.5, and the hairy vetch was considered low than other three treatments. It was considered that the nitrogen amounts in hairy vetch was insufficient to produce millet grain, it was reported that proper nitrogen concentration of leaf was 2.75%(Larson & Hanway, 1997), and SPAD value of hairy vetch was slight low among green manure, and also its trends were more clear at dough stage than other stage by plant nitrogen transfer of nitrogen from leaf to ear (Seo *et al.*, 2000)

<table><caption>Table 2. Chlorophyll contents collected using SPAD at three growth stages</caption><thead><tr><td>Green manure crops</td><td>Boosting stage</td><td>Heading stage</td><td>Maturity stage</td></tr></thead><tbody><tr><td>Hairy vetch</td><td>50.3±1.0</td><td>54.3±2.5</td><td>38.0±4.9</td></tr><tr><td>Barley</td><td>55.3±0.2</td><td>58.7±1.3</td><td>54.9±1.7</td></tr><tr><td>Hairy vetch + barley</td><td>52.4±0.9</td><td>59.0±0.9</td><td>62.5±0.3</td></tr><tr><td>Chemical fertilizer</td><td>54.3±0.8</td><td>58.8±1.3</td><td>55.4±4.9</td></tr></tbody></table>

<footnote n="1">¹ Crop Post-harvest Technology Department, National Institute of Crop Science, Republic of Korea</footnote>

<footnote n="2">² Crop Cultivation and Environ Technology Department, National Institute of Crop Science, Republic of Korea</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: leecw4623@korea.kr</footnote>
Differences in soil chemical properties between before planting (May 16) and after harvesting (Nov. 16) was shown (Table 3). Soil pH was slightly higher in hairy vetch and chemical fertilizer than that of soil incorporated barley. Green manure crops seemed to increase slightly soil total carbon and total nitrogen than that of chemical fertilizer plot. It was similar to report which green manure improve on soil chemical as pH and EC of soil (Lee *et al.*, 2011), and green manure crops also improved bulk density (Kang *et al.*, 2014)

<table><caption>**Table 3.** Changes of chemical properties of soil.</caption><thead><tr><th rowspan="2">Green manure crops</th><th colspan="3">Before planting (May 16)</th><th colspan="3">After harvesting (Nov. 16)</th></tr><tr><th>pH</th><th>T-C (%)</th><th>T-N (%)</th><th>pH</th><th>T-C (%)</th><th>T-N (%)</th></tr></thead><tbody><tr><td>Hairy vetch</td><td>6.0±0.4</td><td>0.81±0.09</td><td>0.07±0.01</td><td>6.8±0.2</td><td>0.83±0.05</td><td>0.08±0.01</td></tr><tr><td>Barley</td><td>5.5±0.3</td><td>0.73±0.09</td><td>0.07±0.01</td><td>6.2±0.1</td><td>0.91±0.04</td><td>0.08±0.01</td></tr><tr><td>Hairy vetch + barley</td><td>5.6±0.2</td><td>0.80±0.06</td><td>0.09±0.01</td><td>6.6±0.2</td><td>0.85±0.02</td><td>0.09±0.01</td></tr><tr><td>Chemical fertilizer</td><td>6.0±0.3</td><td>0.85±0.02</td><td>0.08±0.01</td><td>6.5±0.1</td><td>0.83±0.02</td><td>0.08±0.01</td></tr></tbody></table>

1000 grains weight, culm weight and yield of millet were shown (Table 4). 1000grain weight of hairy vetch was highest among treatments, but culm weight and yield of hairy vetch+barley were highest other treatment. The N supplement by hairy vetch+barley was seem to be sufficient to grow millet. It suggested that corn absorbed enough nitrogen from hairy vetch (Seo *et al.*, 2000), and corn yield more increased with cropping system with winter legume hairy vetch (Torbert, 1996). Physical characteristics of soil were measured bulk density, rate of liquid, air, solid and void state (Table 5). Soil air state of was increased by incorporation with green manure as hairy vetch, barley. It was similar to the report that improved soil structure and water infiltration (N.L. Hartwig & H.U. Ammon, 2000)

<table><caption>**Table 4.** 1000 grains, culm weight and yield of millet</caption><thead><tr><th>Green manure crops</th><th>1000grain wt. (g)</th><th>Culm wt. (ton/ha)</th><th>Yield (ton/ha)</th></tr></thead><tbody><tr><td>Hairy vetch</td><td>18.9±0.9</td><td>7.87±0.18</td><td>3.45±0.44</td></tr><tr><td>Barley</td><td>16.7±2.2</td><td>8.22±0.23</td><td>4.03±0.04</td></tr><tr><td>Hairy vetch+barley</td><td>16.4±1.2</td><td>10.4±1.00</td><td>4.40±0.31</td></tr><tr><td>Chemical fertilizer</td><td>17.9±1.1</td><td>9.59±1.62</td><td>3.93±0.22</td></tr></tbody></table>

<table><caption>**Table 5.** Bulk density, rate of liquid, air, solid and void state of soil after harvest</caption><thead><tr><th></th><th>Bulk density (mg m<sup>-3</sup>)</th><th>Liquid state (%)</th><th>Air state (cm)</th><th>Solid state (%)</th><th>Void (%)</th></tr></thead><tbody><tr><td>Hairy vetch</td><td>1.29±0.14</td><td>28.5±1.1</td><td>22.7±5.8</td><td>48.8±5.2</td><td>51.2±5.2</td></tr><tr><td>Barley</td><td>1.31±0.02</td><td>29.4±1.8</td><td>21.3±2.5</td><td>49.3±0.9</td><td>50.7±0.9</td></tr><tr><td>Hairy vetch+barley</td><td>1.15±0.02</td><td>30.8±0.7</td><td>25.8±0.5</td><td>43.4±0.9</td><td>56.6±0.9</td></tr><tr><td>Chemical fertilizer</td><td>1.29±0.05</td><td>34.0±1.5</td><td>17.4±0.7</td><td>48.6±2.1</td><td>51.4±2.1</td></tr></tbody></table>

## 4 Conclusions

The results of trials were that the leaf color and 1000 grain weight by green manure were increased than chemical fertilizer trial. The soil physical characteristics as the percent of void were lowest in chemical fertilizer treatment. The green manure treatment were not decreased the yield of millet. The results suggest that the green manure incorporation in soil can cultivate millet without the chemical fertilizer

## References

* Hartwig N.L. & Ammon H.U.(2000). Cover crops and living mulches. *Weed Science*, 50,688-699.

* Jong-ho Seo, Ho-jin Lee, Il-bong, Si-ju Kim, Chung-kuk Kim and Hyeok-sukJo(2000). Use of hairy vetch green manure as nitrogen fertilizer for cornproduction. *Korean J. Crop Sci.*, 45,294-299.

* Kang J.K., Lee S.H., Lee K.B., Lee K.D., and HwangS.W. (2014). Effect of cultivation and application of green manure crop on soil physic-chemical properties in saemangeum reclaimed tidal land. *Korean Int. Agric.*, 26, 54-61

* Larson, W.E., and Hanway J.J.(1977). Corn production. In corn and corn improvement, G.F. Spraque(ed). American Society of Agronomy, Madison, Wisconsin.

* LeeS., Jung J., Choi B., Lee Y., Lee J., Song B. and Sung J.(2011). Monoculture and mixture effect of green manure corps on soil quality, weed suppression and organic red-pepper production. *Korean J. Soil Sci. Fert.*, 44, 581-590
# Application of Organic Fertilizers for pesticide degradation from Agricultural Soil under Controlled Conditions

Mohammad Mehdizadeh *±1

## 1 Introduction

The intensive utilization of pesticides as a strategy to improve agricultural productivity induces environmental and toxicological risks, groundwater contamination and serious health problem to the population. Metribuzin is a selective systemic herbicide used for control of many broad-leaved and grasses weeds in soya beans, potatoes, tomatoes, sugar cane, and cereals at 0.07–1.05 kg a.i./ha (Tomline, 2000). It is well known that organic amendments may under some conditions enhance the retention, persistence, and mobility of herbicide in the soil profile, and under others decrease them (Singh, 2008; Kravvariti *et al.*, 2010). In this present study the effect of different organic amendments on the degradation of metribuzin was carried out by high performance liquid chromatographic method.

## 2 Materials and Methods

Soil was obtained from the upper layer (0-10 cm) of soil, air dried at room temperature, passed through a 2-mm mesh sieve and mixed with organic amendments (poultry manure (PM) and cow manure (CM)) in 2.5 percent rate (w/w). Then treated with an aqueous suspension of commercial formulation of metribuzin to give a final concentration of 5 mg kg⁻<fnref n="1" /> of air-dried soil. The moisture content of soil samples had been adjusted to 70% of soil water-holding capacity and transferred to glass jars, and incubated in the dark at 20 °C for 120 days. Sub samples (20 g) were taken for herbicide analysis one day after addition of herbicide and then 5, 15, 30, 50, 90 and 120 days, and frozen at -20 °C until Analytical procedure. Final soil samples (10 g) were extracted twice with methanol (20 mL) on a horizontal shaker for 90 min. After equilibration, the tubes were centrifuged for 10 min at 10000 rpm in a centrifuge maintained at 20°C. 10 mL of the supernatant were filtered and the extract was diluted with 5 mL of methanol (HPLC grade) and stored in 5 °C before analyzed by HPLC (Shimadzu model 10A) equipped with a Spectrophotometric UV detector. The reverse phase column was a 25 cm × 4.6 mm i.d. Adsorbosphere C18. The mobile phase was 80% methanol and 20%: water. The flow rate was 0.5 mL min⁻<fnref n="1" />. The wavelength was set at 290 nm, and the retention time of metribuzin was 9.53 min. A 20 µL injection volume was used. Metribuzin dissipation curves in original and amended soil samples were fitted to first-order kinetics (C = Cᵢe⁻⁰⁻⁰t) and half-lives (t₁/₂) calculated.

## 3 Results – Discussion

Calibration graph and the peak results of HPLC analysis of metribuzin standard (1 mg kg⁻<fnref n="1" /> soil) are presented (Fig. 1). Metribuzin content were decreasing progressively in soil amended with organic amendments. However, this decrease depended on organic amendment type applied. At the end of the experimental period and compared with the unamended soil, metribuzin content significantly decreased with application of PM and CM.

<figure><img src="image_30.png" /><figcaption>**Fig. 1.** Calibration graph of Reversed-phase HPLC separation of metribuzin standard (1 mg kg⁻<fnref n="1" /> soil).</figcaption></figure>

<footnote n="1">¹ PhD Student in Weed Science, Faculty of Agronomy and Plant Breeding, University of Mohaghegh Ardabili, Iran.</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: mehdizade.mohammad@gmail.com</footnote>
Metribizin degradation coefficients (K) in PM and CM treatments were 1.29 and 1.22 times NF treatment respectively. Metribizin half life in mentioned treatments was 92 and 97 days respectively, that were significant lower than NF treatment (119 days). Dissipation of metribizin well described by first-order kinetics with regression coefficients (R²) ≥0.94. The obtained first-order rate constant and half-lives are presented (Table 1).

<table><caption>**Table 1.** Observed speed constants (K) and half-life values (DT₅₀) of the kinetics of metribuzin degradation in soil with different organic amendments.</caption><thead><tr><td>Organic Amendments</td><td>K (±σ)(Day<sup>-1</sup>)</td><td>DT<sub>50</sub> (days)</td><td>DT<sub>90</sub> (days)</td><td>R<sup>2</sup></td></tr></thead><tbody><tr><td>NF</td><td>0.0058 (0/0002)</td><td>119.48</td><td>396.89</td><td>0.98</td></tr><tr><td>PM</td><td>0.0075 (0.0004)</td><td>92.40</td><td>306.93</td><td>0.99</td></tr><tr><td>CM</td><td>0.0071 (0.0003)</td><td>97.60</td><td>324.22</td><td>0.97</td></tr></tbody></table>

Although addition of organic manures has been an integral part of sustainable agriculture practices, the earlier findings give a new dimension of its utilization for removal of persistent pesticides (Kadian *et al.*, 2007). Degradation of metribuzin was enhanced by 0.5% manure, 5% peat, and 5% cornstalk amendments compared to non amended soils (Mooram *et al.*, 2001). Organic amendment is a rich source of nitrogen, carbon and other nutrients, which make it well suited for supporting the soil microorganism growth.

## 4 Conclusions

In this study the effect of organic amendments on the half-life of metribuzin were investigated. It seems that the application of organic amendments favored microbial development and consequently accelerated the degradation process. The results indicated that microbiological soil properties were responsible for metribuzin dissipation. Study suggests that organic amendments certainly enhanced degradation of metribuzin in soil. These results have implications in managing the persistent soil residues of metribuzin. However, to get a more realistic picture study under actual field conditions is advised.

## References

* Kadian, N., Gupta, A., Satya, S., Kumari, R. & Malik, A. (2007). Biodegradation of herbicide atrazine in contaminated soil using various bioprocessed materials. *Bioresour Technology*. **99**, 4642-4647.

* Kravvariti, K., Tsiropoulos, N.G. & Karpouzas, D.G. (2010). Degradation and adsorption of terbuthylazine and chlorpyrifos in biobed biomixtures from composted cotton crop residues. *Pest Management Science*. **66**, 1122-1128.

* Moorman, T.B., Cowan, J.K., Arthur, E.L. & Coats, J.R. (2001). Organic amendments to enhance herbicide biodegradation in contaminated soils. *Biology and Fertility Soils*. **33**, 541-545.

* Singh, N. (2008). Biocompost from sugar distillery effluent: effect on metribuzin degradation, sorption and mobility. *Pest Management Science*. **64**, 1057-1062.

* Tomlin, C.D.S. (Ed.). (2000). The Pesticide Manual, 12th ed., British Crop Protection Council, Farnham.
# Designing innovative strategies of nitrogen fertilization by coupling diagnosis of the uses of existing tools and participatory workshops.

Clémence Ravier *±<fnref n="1" />,<fnref n="3" />,<fnref n="4" />*, Marie-Hélène Jeuffroy <fnref n="1" />, Philippe Gate <fnref n="2" />, Jean-Pierre Cohan <fnref n="3" /> & Jean-Marc Meynard <fnref n="4" />

## 1 Introduction

In France, for all the arable crops, N fertilizer rates are planned at the beginning of spring, based on the balance sheet method (Hébert, 1969) and splitting is planned, depending on calendar dates and crop stages. This strategic planning aims at adjusting N fertilizer to soil supply in order to fulfill crop requirements (Meynard et al., 1997). The balance sheet method is a rigorous and scientifically approved model that makes consensus in French agricultural R&D. Since the 80's, progress in nitrogen fertilization followed a typical way of rule-based design (Meynard et al., 2012): R&D organisms gradually improved N fertilization through refining the estimation of the balance-sheet equation parameters. Dynamic modeling of soil mineralization is considered as one important step of progress to improve the precision of the method (Justes et al., 2009). Further evolution focused on the ways to divide the whole rate on the crop cycle in order to improve N use efficiency and on the development of monitoring tools, always targeting the objective of non-limiting nitrogen nutrition all over the crop cycle. However, we question this strategy regarding the evolution of the context: issues related to environmental pollution, resources management, production (yield stagnation, grain protein content), farmers' difficulties to appropriate the method (Cerf & Meynard, 1987; Felix & Reau, 1995) and unused knowledge on N dynamics (that do not fit into the conceptual framework of the balance sheet method).

We assume that the consensus on the balance sheet method may have limited the exploration of alternative strategies, which could have similar or improved results. There is a need to shift toward new paradigms, to think about strategies that could satisfy new expectations such as achieving both environmental, agronomic and economic objective or including advancement in knowledge and technologies. Following the approach proposed by Cerf et al. (2012) for the design of decision support tools; we coupled diagnosis of the uses of existing tools and participative workshop to design prototypes for new strategies.

## 2 Materials and Methods

### Diagnosis of uses of existing tools

The diagnosis of uses was carried out based on two complementary sources of information. (i) Official report of the regional working groups on the implementation of the balance-sheet method, mandated by the Ministry of Agriculture. Within the fifth Action Program of the Nitrates Directive, twenty regional groups of experts (GREN) have been created to agree on an equation for the calculation of the total rate and its parameterization in each region. We analyzed the controversies occurring when stakeholders confronted their perceptions and ways of using the balance sheet. (ii) Interviews of farmers and technical advisors on their practical use of the balance sheet method. We choose four regions of France with diversified agricultural systems and climatic contexts (Normandie, Bourgogne, Bretagne, Provence).

### Design framework

The objective of the design process was to explore new concepts for fertilization, based on the involvement of new knowledge, in order to unlock the current paradigm. To move forward from rule-based to innovative design for management of nitrogen fertilization we choose an innovative theory: the C-K theory (Hatchuel & Weil, 2009). Based on a dual expansion of concepts (C) space and knowledge (K) space, it can be used to overcome fixation effects in design (Le Masson et al., 2011). During workshops that gather different stakeholders with various background, it is a practical guideline to structure exchanges. The diagnosis of uses helped us to define the basis of new concepts to be explored and we used the C-K framework to lead the workshop and explore the potential design paths of innovative fertilization strategies.

## 3 Discussion

The diagnosis of uses showed that some principles of the method such as the estimations of the target yield and of the available soil nitrogen were controversial and potentially source of difficulties for the implementation of the balance-sheet method. In the equation, crop requirements are estimated with a target yield and a requirement per unit of production (Coïc, 1959). The target yield is source of controversies because it is unpredictable, due to inter-year climate

<footnote n="1">¹ INRA UMR 211 Agronomie INRA-AgroParisTech, 78850 Thiverval-Grignon, France</footnote>

<footnote n="2">² Arvalis-Institut-du-Végétal, 3 rue Joseph & Marie Hackin, 75116 Paris, France</footnote>

<footnote n="3">³ Arvalis institut du végétal, Station expérimentale de la Jaillière, 44370 La Chapelle Saint-Sauveur, France</footnote>

<footnote n="4">⁴ INRA UMR 1048 SAD-APT, INRA-AgroParisTech, 78850 Thiverval-Grignon, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: clemence.ravier@grignon.inra.fr</footnote>
variability. While the target yield should be based on the mean of yields obtained during the 5 previous yields (standard rule), farmers tend to estimate the target yield as the desired yield, close to the highest value ever reached (Box. 1). The debates about the standard rule clearly show that the concept of fixing a target yield is source of difficulties. From the report of the GREN, we found various arguments: the failure to achieve the potential yield in favorable years, the failure to enhance the genetic progress, the risk of strengthening the trend of yields' stagnation or the fact that an insufficient nitrogen nutrition could lead to low protein content. This shows that there is still no consensus on rightfulness of the standard rule and on the fact to strictly frame the way to fix the target yield. The model also requires estimating soil supply. Several GREN members thought that using a soil analysis to estimate the soil N content at the end of winter may be replaced by modeling, due to the improvement of knowledge on soil N cycling. The measurement is considered as time consuming and presents a number of sources of uncertainties such as a sampling procedure or extrapolation of measures.

Those controversies contribute to give ideas to formulate new concepts. For instance, regarding the target yield, we suggest to think of strategies that move forward from a planned fertilization to a more adaptive one. From the controversies on the soil analysis, we came out with ideas such as looking for plant indicators that could replace it.

**Box 1. Extracts from interviews that illustrate the perception of the target yield as the desired yield.**

"Farmers are entrepreneurs, we can not ask people to reason this logic to target an average result" (Advisor Bretagne).

"My yields are around 70 to 80 q.ha-1, 90 for some plot [...], I often use 90 (as target yield), I already did it» (Farmer, Bourgogne)

"I take 100q/ha-1 where I know I can do it" (Farmer Normandie)

"My average is about 60 q.ha-1, but I did not take that on my "provisional fertilization plan, I based it on 80". (Farmer Normandie)

"Target yield, it's an average value for a group of plot which is equivalent to the expected yield, the one of good years". (Farmer Provence)

The design workshops were organized based on new concepts to be explored. Those initial concepts result from both the diagnosis and the need to integrate recent knowledge not taken into account in the current paradigm. For the first participatory workshop we established two initial concepts:

1) "Strategies of nitrogen fertilization based on pathways of plant nitrogen status".

This concept was formulated to explore adaptive management without fixing a target yield and to integrate the knowledge on nitrogen deficiency to optimize NUE. With this concept, thinking of strategies of N fertilization that include periods of N deficiency is a shift with the current paradigm which is based on an objective of non limiting N nutrition. The exploration was fruitful and led to two main concepts: a regular monitoring of plant and a strategy based on anticipation of soil N supply. Various knowledge were discussed to make concrete those fertilization strategies such as an early monitoring based on nitrogen nutrition index and crop biomass; weather forecast; plants' N uptake capacity and soil humidity at the moment of N application to provide a triggering threshold to apply N fertilizer.

2) "Taking account of farmers' ability to learn". The idea was to explore tools that enable farmers to assess their fertilization strategies (rate and timing) at the end of the crop cycle, to adapt next year their fertilization strategy (adaptive management). The exploration led to imagine a collaborative tool based on a post harvest assessment. Then it would provide specific indicators to observe the next year regarding what could be improved in the current strategy.

**4 Conclusions**

We highlighted how the current paradigm is flawed regarding the objective to achieve both environmental and agronomical issues. Some principles of the method are sources of controversies and uncertainties. Incremental improvement of the model is called into question to achieve current and future nitrogen issues. We emphasized the need to investigate new strategies of N fertilization to comply with news expectations. Introducing the workshops with the diagnosis of uses created good conditions to explore concepts of innovative management strategies that we provided. The two first concepts will further be explored and tested with farmers and advisors in contrasted regions of France.

*Acknowledgements.* We thank Arvalis Institute of cereals crops (Arvalis, Insittut du végétal) for financing the thesis and the French Agency of the Environment and the Control of Energy (ADEME) for funding this research project as part of the REACCTIF program.

**References**

Cerf, M., Jeuffroy, M.H., Prost, L. & Meynard, J.M. (2012). Participatory design of agricultural decision support tools: taking account of the use of situations. *Agronomy for sustainable development*. **32 (4)**, 899-910.

Cerf, M. & Meynard, J.M (1987). Enquête sur la mise en œuvre des méthodes de fertilisation raisonnée.

Coïc, 1956. La nutrition et la fertilisation azotée du blé d'hiver. *Annales Agronomiques*. **1**, 115-131

Felix, I. & Reau, R. (1995). Pratiques de fertilisation : un constat paradoxal. *Perspectives agricoles*. **207**.

Hatchuel, A. & Weil, B. (2009). C-K Design Theory: An Advanced Formulation. Research in Engineering Design, **19**, 181-192.

Hébert, 1969. La fumure azotée du blé. *Bulletin technique d'information*. **244**, 755-766.

Justes, E., Cohan, J.P., Desprez, M., Cerman, J., Valé, M., Champolivier, L., Laurent, F. & Mary, B. (2009). Prédiction de la mineralization de l'azote organique humifié des sols cultivés: paramétrage et validation des modèles opérationnels. *In Congrès COMFER-GEMAS, 25 & 26 nov.2009 – Blois, France*.

Le Masson, P., Hatchuel, A. & Weil, B. (2011). The interplay between creativity issues and design theories: A new perspective for design management studies? *Creativity and Innovation management*, **20 (4)**, 217-237.
# Impacts of crop rotation, soil tillage and irrigation on yield and agroecological parameters: an assessment of different energy cropping systems in Northeast Germany

Timm Sauer*±<fnref n="1" />, Moritz Reckling<fnref n="1" />, Gunhild Rosner<fnref n="1" /> & Johannes Hufnagel<fnref n="1" />

## 1 Introduction

In the global context of population growth, socioeconomic development and food security, dealing with land use change and trade-offs posts a great future challenge. Growing demands for land and freshwater decrease the availability of adequate resources - a trend that is further aggravated by climatic changes and negative ecological side-effects of current agricultural practises. Energy cropping systems for biogas production are one of the currently omnipresent agronomic issues in Germany, with silage maize meeting the claim of high-yield yet low-price co-ferments while providing reasonable profits (Schittenhelm et al., 2011). However, continuous maize cropping bears a considerable risk of ecological impacts such as humus depletion, biodiversity loss, and nitrate leaching (Bauboeck et al., 2014). Our objective is to identify novel strategies that are both, ecologically sound and economically profitable in the long term. Therefore, we systematically assessed the productivity of alternative cropping systems in a 7-year field trial in the federal state of Brandenburg (Germany) including continuous maize with cover crops and a diverse crop rotation, both under irrigation and rainfed conditions as well as under ploughing and no-tillage. Since Brandenburg is particularly affected by climate change due to light soils and summer droughts climate change adapted cropping systems are needed. We hypothesize that (1) irrigation significantly increases biomass yields in all treatments, (2) no-tillage with direct seeding reduces biomass yields substantially, and (3) the average biomass produced in the diverse crop rotation (CR) equals the biomass produced under continuous maize cropping (CC).

## 2 Materials and Methods

The field trial was established at the experimental farm in Muencheberg using a 3-factorial split plot design. The site is characterized by a mean annual precipitation of 562 mm, a mean annual air temperature of 8.8° C, and an average growing period of 170 days. The experiment included 8 cropping systems, represented by different combinations of 3 management factors: crop rotation type (continuous maize cropping with cover crops vs. a diverse 4-year crop rotation), water management (irrigated vs. rainfed), and tillage operations (plough vs. no-tillage with direct seeding) (Table 1). The experiment started with year 1 in 2008. In this paper we analysed data from 2008 (year 1) to 2014 (year 3).

<table><caption>Table 1. Experimental design of the 7-year field trial</caption><thead><tr><th>Treatments</th><th>Year 1</th><th>Year 2</th><th>Year 3</th><th>Year 4</th></tr></thead><tbody><tr><td>Continuous maize cropping (CC)</td><td colspan="4">Maize (Zea mays, cultivar Francisco S270), harvested in September/October<br/>Cover crop: Winter rye (Secale cereale) for soil conservation, only rainfed cropping, eradicated end of March</td></tr><tr><td>Diverse crop rotation (CR)</td><td>1<sup>st</sup> crop winter rye (Secale cereale), harvested in May<br/>2<sup>nd</sup> crop forage sorghum (Sorghum bicolor, cultivar Ronal), harvested in September</td><td>1<sup>st</sup> crop winter triticale (xTriticale, cultivar Talentro), harvested May/June<br/>2<sup>nd</sup> crop lucerne-clover-grass mixture (Medicago sativa, cultivar Plato + Trifolium pratense, cultivar Milvus + Lolium perenne, cultivar Lilora)</td><td>Lucerne-clover-grass mix (Medicago sativa, cultivar Plato + Trifolium pratense, cultivar Milvus + Lolium perenne, cultivar Lilora), harvested with 3 cuts</td><td>Maize (Zea mays, cultivar Francisco S270), harvested in autumn</td></tr><tr><td>Irrigation</td><td>irrigation (only sorghum) + rainfed</td><td>rainfed</td><td>irrigation + rainfed</td><td>irrigation + rainfed</td></tr><tr><td>Primary tillage</td><td colspan="4">plough vs. no-tillage with direct seeding</td></tr></tbody></table>

Irrigation demand was computed using the BEREST/IRRIGAMA.net model (Mirschel & Wenkel, 2004). For all tillage operations of maize a Becker Aeromat drilling machine was used and for the other crops an Amazone rotary cultivator (ploughing) and a John Deere seed drill (direct seeding). Whole crop silage was harvested to determine productivity (aboveground dry matter biomass yield). Cover crops in the CC treatment were not harvested. Standard amounts of mineral fertilizers were applied but no organic fertilizers. Statistical analysis with SAS was performed for yield data

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

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: timm.sauer@zalf.de</footnote>
from 2008 to 2014 using Tukey's HSD statistical test. Where appropriate, we used mean averaged values over time and/or treatment variants (tillage, irrigation) for comparative purposes, which may include more than one crop type per year in case of the diverse crop rotation.

**3 Results – Discussion**

Irrigation significantly increased biomass yields and yield stability in CC in all years with on average 3.7 t ha⁻¹ (22 %). In CR irrigation only significantly increased biomass yields of sorghum. On the contrary, no-tillage with direct seeding did not significantly reduce biomass yields of CC except for two years (2012, 2013) (Fig. 1). However, plough-based yields were on average 1.2 t ha⁻¹ (7 %) higher than under no-tillage. In CR no-tillage did not reduce biomass yields except in one year of winter rye and one year of winter triticale.

<figure><img src="image_31.png" /><figcaption>**Fig. 1.** Results for yield comparisons «Tillage vs. No-till of CC-maize» (left) and «CC system vs. CR system» (right).</figcaption></figure>

During the 7-year-experiment CC resulted in higher biomass yields than the cereal crops in CR. Only biomass yields of the legume-grass mixture were comparable with those of maize. Over the whole period, CR reached 82.3 % of total biomass yield achieved with CC but with high annual fluctuations due to the different crops in CR (Fig. 1). In 2011, maize was cultivated simultaneously in both systems with an average yield increase of 6 % in CR across the treatments. This can be explained by the positive pre-crop effects of legume-grass mixtures as preceding crop before maize.

**4 Conclusions**

In only two out of seven years, no-tillage reduced maize productivity, which is a relevant finding for soil conservation matters as well as with respect to agronomic efficiency. Interestingly, this contrasts results from other similar studies (Anken et al., 2004; Gruber et al., 2012) and points out the hydrologic and pedological heterogeneity of the agricultural landscapes in Northeast Germany. On the other hand, the position of both these years at the end of the time series may also indicate that yield reducing effects under no-tillage develop over time. In our study region, irrigation can be a relevant option to increase biomass yields in maize but seems less relevant in diverse crop rotations. Continues maize with irrigation and ploughing was the treatment with the highest biomass production. Statistically, irrigation led to significant improvements of CC in all years. For an overall evaluation of the different systems an economic analysis is necessary to account for costs and revenues. An integrated approach also considering agroecological interdependencies and presumed climatic changes could improve process understanding and help to design sustainable farming systems.

*Acknowledgements.* The work was financed through the Federal Ministry of Food and Agriculture (BMBF), Germany; the Brandenburg Ministry of Sciences, Research and Cultural Affairs (MWFK), Germany and the BMBF funded FACCE-ERA-NET+ project Climate Change Adaptability of cropping and Farming systems for Europe (Climate-CAFÉ; Grant PTJ-031A544).

**References**

Anken, T., Weisskopf, P., Zohlmann, U., Forrer, H., Jansa, J. & Perhacova, K. (2004). Long-term tillage system effects under moist cool conditions in Switzerland. *Soil & Tillage Research, **78** (2), 171–183.

Bauboeck, R., Karpenstein-Machan, M. & Kappas, M. (2014). Computing the biomass potentials for maize and two alternative energy crops, triticale and cup plant (*Silphium perfoliatum* L.), with the crop model BioSTAR in the region of Hannover (Germany). *Environmental Sciences Europe* **2014**, *26*:19.

Gruber, S., Pekrun, C., Moehring, J. & Claupein, W. (2012). Long-term yield and weed response to conservation and stubble tillage in SW Germany. *Soil & Tillage Research, **121***, 49–56.

Mirschel, W. & Wenkel, K.-O. (2004). System zur operativen computergestützten Beregnungseinsatzsteuerung (BEREST 90). *In: Luettger, A.* (Ed.): *Pilotprojekt Beregnung des Landes Brandenburg (4. Brandenburger Beregnungstag, Gueterfelde, 27.11.2003)*, LVLF Brandenburg, 39–50.

Schittenhelm, S., Reus, D., Kruse, S. & Hufnagel, J. (2011). Assessment of productivity and profitability of sole and double-cropping for agricultural biomass production. *Journal fuer Kulturpflanzen, **63** (11), 387–395.
# Decline of rice grain yield under delayed transplanting in wheat-rice double cropping system in South Korea

Shingu Kang *±<fnref n="1" />, Bonil Ku<fnref n="1" />, Wangyu Sang<fnref n="1" />, Hyunseok Lee<fnref n="1" />, Hongkyu Park<fnref n="1" />, Youngdoo Kim<fnref n="1" />, Jiiyoung Shon<fnref n="1" />, Woonho Yang<fnref n="1" /> & Jeomho Lee<fnref n="1" />

## 1 Introduction

The southern plain area is the wheat-rice double cropping possible region in South Korea (Yoon *et al.*, 2011). To meet increasing food security within the limited croplands, double cropping is required in much of the paddy field. However, wheat harvesting is sometimes delayed due to rainy weather condition in June. These events often result in delayed rice transplanting that reduce grain yield (Kim *et al.*, 2013). When late transplanting occurs, heading date also delayed and cumulative temperature for grain ripening decreases significantly. Consequently, farmers decide whether to select to cultivar to minimize the yield loss (Timssima & Connor, 2001). The objectives of this experiment were to determine whether delayed transplanting influenced the agronomic responses of japonica rice cultivars to planting date, and to suggest rice cultivars that are suitable for late transplanting.

## 2 Materials and Methods

Experiments were conducted at the experimental field of National Institute of Crop Science (NICS) in Iksan (35° 57'N, 126° 57'E) in 2013 and 2014. The experiment was laid out in a split plot design with three replications, where transplanting dates were in the main plot and rice varieties were in subplots. Three transplanting dates were applied, starting in June 5, and occurred on approximately 20 and 30 days thereafter. The recommended date for rice single cropping is around June 5, and the latter two transplanting dates on June 25 and July 5 were regarded as delayed transplanting. Total 10 japonica rice varieties were used including early (Geumo2, Geumo3, Jopyeong, and Unkwang), medium (Suan and Dongbo), and mid-late (Nampyeong, Sodami, Sukwang, and Chinnong). Cultivation practices were managed properly for optimum growth following standard cultivation protocol of NICS.

## 3 Results – Discussion

Average heading date of 10 cultivars was August 12 in the control, August 23 in the second, and August 28 in the third transplanting (Table 1). These delays in heading dates were resulted in decrease of cumulative temperatures for 40 days from heading. The accumulative temperature in the last transplanting was below 880°C that is insufficient for grain ripening of japonica rice. The heading date of the 10 cultivars responded differently to delayed transplanting, with greater cumulative decreases occurring with mid-late maturity cultivar.

<table><caption>**Table 1.** Effect of delayed transplanting on heading date of japonica rice cultivars and cumulative temperature during ripening stage (40 days from heading).</caption><thead><tr><th>Transplanting date /Maturity</th><th>Heading date</th><th>Days to heading</th><th>Cumulative temperature (°C)</th><th>Average temperature (°C)</th></tr></thead><tbody><tr><td colspan="5" style="text-align: center; font-style: italic;">Transplanting date</td></tr><tr><td>June 5</td><td>Aug 12</td><td>69a</td><td>945a</td><td>23.6a</td></tr><tr><td>June 25</td><td>Aug 23</td><td>62b</td><td>883b</td><td>22.1b</td></tr><tr><td>July 5</td><td>Aug 28</td><td>56c</td><td>852c</td><td>21.3c</td></tr><tr><td colspan="5" style="text-align: center; font-style: italic;">Maturity group</td></tr><tr><td>E</td><td>Aug 15</td><td>56b</td><td>930a</td><td>23.3a</td></tr><tr><td>M</td><td>Aug 23</td><td>64ab</td><td>881b</td><td>22.0b</td></tr><tr><td>ML</td><td>Aug 26</td><td>67a</td><td>863bc</td><td>21.6bc</td></tr><tr><td>CV (%)</td><td>-</td><td>14.6</td><td>7.6</td><td>7.6</td></tr></tbody></table>

Average milled rice yield across 10 cultivars was declined from 5.70 t/ha in the control to 5.32 t/ha in the last transplanting (Table 2). Yield was not affected when planting was delayed 20 days, but was 6.6% lower when planting was delayed 30 days. Yield loss due to late planting was associated with decreases in panicle and spikelet and no change in grain weight. The grain yield was higher in the mid-late maturity group compared to early maturity.

<footnote n="1">¹ National Institute of Crop Science, Suwon 441-707, Republic of Korea</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: sgkang82@korea.kr</footnote>

<footnote>* and **: Significant at 0.05 and 0.01 probability levels, respectively. ns: Non-significant at 0.05 probability level. Within a column, means followed by different letters are significantly different by Duncan's multiple range test.</footnote>
<table><caption>**Table 2.** Effect of delayed transplanting on grain yield and yield component of japonica rice cultivars.</caption><thead><tr><th>Transplanting date/Maturity</th><th>No. of panicles (m<sup>-2</sup>)</th><th>No. of spikelet per panicle</th><th>1000-grain weight (g)</th><th>Ripened spikelet (%)</th><th>Milled rice yield (t ha<sup>-1</sup>)</th></tr></thead><tbody><tr><td colspan="6" style="text-align: left; font-style: italic;">Transplanting date</td></tr><tr><td>June 5</td><td>310a</td><td>95a</td><td>21.6a</td><td>89.5b</td><td>5.70a</td></tr><tr><td>June 25</td><td>291b</td><td>91b</td><td>22.8a</td><td>92.1ab</td><td>5.50ab</td></tr><tr><td>July 5</td><td>270c</td><td>89b</td><td>22.9a</td><td>94.0a</td><td>5.32b</td></tr><tr><td colspan="6" style="text-align: left; font-style: italic;">Maturity group</td></tr><tr><td>E</td><td>298a</td><td>87b</td><td>22.4a</td><td>90.8b</td><td>5.35b</td></tr><tr><td>M</td><td>295ab</td><td>85b</td><td>22.9a</td><td>94.8a</td><td>5.41ab</td></tr><tr><td>ML</td><td>280b</td><td>99a</td><td>22.3a</td><td>91.6b</td><td>5.66a</td></tr><tr><td>CV (%)</td><td>9.7</td><td>13.2</td><td>5.3</td><td>5.2</td><td>7.4</td></tr></tbody></table>

Among the early maturing cultivars, Jopyeong (5.30-5.62 t/ha) and Unkwang (5.51-5.72 t/ha) showed the highest milled rice yield in the delayed transplanting dates (Fig. 1). In mid-late maturing cultivars, Sukwang (5.55-5.77 t/ha) and Chinnong (5.55-5.77 t/ha) showed higher grain yield in the delayed transplanting. These four varieties would be the option for the farmers who are considering wheat-rice double cropping in Honam plain area.

<figure><img src="image_32.png" /><figcaption>**Fig. 1.** Milled rice yield of 10 japonica rice cultivars in optimal and delayed transplanting dates in Honam plain area.</figcaption></figure>

## 4 Conclusions

In this study, we identified the decline of rice grain yield in wheat-rice double cropping in South Korea due to delayed transplanting. To meet the high yield in rice and maximize benefit from the double cropping cultivation, suitable rice cultivars and adequate transplanting are required. Although the promising two early mature cultivars were selected in this study, new early- and medium maturity rice cultivars are needed for flexible cropping system design.

*Acknowledgements.* This work was carried out with the support of 'Research Program for Agriculture Science & Technology Development (Project No. PJ00927503 and PJ00920303)' Rural Development Administration, Republic of Korea.

## References

* Yoon, S.T., Kwak N.I., ShinH.E., Shim S.I.,&Kim Y.B. (2011). Optimization of rice cultivation method for double cropping in Cheonan area. Korean J. Intl. Agri. **23**(4): 402-409.

* Kim, Y.D., Kang S.G., Ku B.I., Choi M.K., Park H.K., Sang W.G., Lee M.H. & Kim B.K.. (2013). Growth and yield of pot seedling rice as affected by different transplanting times in double cropping. Korean J. Intl. Agri. **25**(2): 153-158.

* Timsina, J. & Connor D.J. (2001). Productivity and management of rice-wheat cropping systems: issues and challenges. Field Crop. Res. **69**: 93-132.
# Improvement of vineyard sustainability according to biogeochemical cycle of nitrogen in field

Marie Thiollet-Scholtus ±<fnref n="1" />, Najat Nassr <fnref n="2" />, Aude Langenfeld <fnref n="2" />, Mohamed Benbrahim <fnref n="2" />, Lionel Ley <fnref n="3" />, Julie Grignon <fnref n="4" /> & Christian Bockstaller <fnref n="4" />

## 1 Introduction

Good and yield wine production implies a well-balanced biogeochemical cycle of nitrogen (BCN) at field level e.i. in soil and in plant (Guilpart, et al., 2014). Nitrogen is very important for grape quality and quantity and field sustainability. The mineralization of organic nitrogen, depending on soil microbial activity which is linked to soil cover crop management, is the main source of mineral nitrogen for the vine (Barlow et al., 2009; Ingels et al., 2005; Raath and Saayman, 1995; Thiebeau et al., 2005). This paper is focused on a useful indicator fulfilling a sustainability assessment method: functional microbial populations implicated in BCN in vineyard field.

## 2 Materials and Methods

An experimental network with 6 platforms gathering 45 fields located in Atlantic coast (Loire valley and Bordeaux) and in North-East (Alsace) of France has been set up since 2012 (extract presented in Table 1). These vine sites represent a diversity of environmental factors (i.e. soil and climate) and the same method is used to assess them: agricultural, environmental, social and economical. The tested prototypes are tested according to the following goals: same yield, harvest quality, working time and production costs. The added value approach is based on assessing nitrogen dynamic in soil, i.e. nitrogen mineralization, regarding microbial biomass and activity.

<table><caption>Table 1. Alsacian sites-system characteristics.</caption><thead><tr><th>Name</th><th>Site</th><th>Designed system</th><th>Vine age</th><th>Variety</th><th>Soil</th></tr></thead><tbody><tr><td>Ribeau_AB</td><td rowspan="2">INRA-Ribeauvillé</td><td>Organic</td><td>16</td><td rowspan="2">Riesling</td><td rowspan="2">Loamy, sandy and clayey</td></tr><tr><td>Ribeau_PI</td><td>Integrated</td><td>16</td></tr><tr><td>Rouff_PI</td><td rowspan="2">Rouffach agriculture school</td><td>Integrated</td><td>32</td><td rowspan="2">Pinot Gris</td><td rowspan="2"></td></tr><tr><td>Rouff_Piopti</td><td>Integrated-Rate-sprayingreduction</td><td>32</td></tr><tr><td>Chaten_AB</td><td>OPABA-Chatenois</td><td rowspan="2">Organic and Biodynamic</td><td>17</td><td rowspan="2">Riesling</td><td>Sandy silty and clayey</td></tr><tr><td>Inger_AB</td><td>OPABA-Ingersheim</td><td>26</td><td>Sandy loam</td></tr></tbody></table>

## 3 Results – Discussion

For all AB-systems, functional richness (Fig.1A) at bud-break is very much higher than for all PI-systems (respectively 24-25 and 16-19). For sites Ribeauvillé and Rouffach, functional richness is not significantly different between designed systems at bud-break, whereas, for both sites, it is significantly different at veraison. Comparing all sites according to designed-systems, functional richness is significantly different between designed-systems at the two vine vegetative periods.

For all Designed-systems in a same site, bacterial abundance (Fig. 1B) is never significantly different between. At bud-break, bacterial abundance is between 28 and 45 ng/g of dry soil. For sites-systems Chaten_AB and Inger_AB, bacterial abundance is statistically different than the others sites-systems (respectively between 496-527 and between 65-141 ng/g of dry soil during veraison vine). One reason may be the number of years of organic farming in Inger_AB and Chaten_AB (more than 10 years) against less than 10 years for Ribeau_AB, and the others PI_Designed_system. Comparing all sites according to designed-systems, bacterial abundance is significantly different between designed systems only for veraison period.

Nitrogen mineralization kinetic (Nmin-kinetic) does not allowed to separate either PI and AB designed-systems, neither sites-systems (Fig. 1C). The top Nmin-kinetic is for Ribeau_PI site-system whereas the slowest one are for Ribeau_ABand Rouff_PI sites-systems. Rouff_Piopti, Inger_AB and Chante_AB sites-systems have touchily the same

<footnote n="1">¹ INRA-SAD-UR-0055 ASTER. 28, rue de Herrlisheim F-68021 Colmar, France</footnote>

<footnote n="2">² CRITT-RITTMO-Agroenvironnement, 37 Rue de Herrlisheim, 68000 Colmar, France</footnote>

<footnote n="3">³ INRA-SEAV. 28, rue de Herrlisheim F-68021 Colmar, France</footnote>

<footnote n="4">⁴ INRA-UMR-1121 LAE. 28, rue de Herrlisheim F-68021 Colmar, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: marie.thiollet-scholtus@colmar.inra.fr</footnote>
Nmin_kinetic.

It is possible to separate PI and AB designed-systems according to PC2 axis of the PCA (Fig.1D): AB-systems under zero and PI-systems up to zero. Ribeauvillé-site is just on the y-axis for both systems AB and PI, i.e. there is no such difference between the two designed systems on this site. Inger_AB and Chante_AB are mainly explained by variables functional richness at bud-break and at veraison, whereas Rouff and Ribeau systems are mainly explained by bacterial abundance.

<figure><img src="image_33.png" /><figcaption>Fig. 1. Multicriteria results for all the sites-systems in Alsace, for 2014 vintage (France).</figcaption></figure>

## 4 Conclusions

In this paper, we show that some of the analyzed BCN indicators are interesting to assess new designed-systems in different vineyard.

*Acknowledgements.* The authors thank the funders of these research programs: the ECOPHYTO and CASDAR programs of the French Department of Agriculture and Alsace region.

## References

* Barlow, K., Bond, W., Holzapfel, B., Smith, J., and Hutton, R. (2009). Nitrogen concentrations in soil solution and surface run-off on irrigated vineyards in Australia. Australian Journal of Grape and Wine Research 15, 131-143.

* Guilpart, N., Metay, A., and Gary, C. (2014). Grapevine bud fertility and number of berries per bunch are determined by water and nitrogen stress around flowering in the previous year. European Journal of Agronomy 54, 9-20.

* Ingels, C. A., Scow, K. M., Whisson, D. A., and Drenovsky, R. E. (2005). Effects of cover crops on grapevines, yield, juice composition, soil microbial ecology, and gopher activity. American Journal of Enology and Viticulture 56, 19-29.

* Raath, P. J., and Saayman, D. (1995). Nitrogen mineralisation in vineyard soils of the Western Cape as affected by soil management practices. South African Journal of Enology and Viticulture 16, 7-13.

* Thiebeau, P., Herre, C., Doledec, A. F., Perraud, A., Panigai, L., Mary, B., and Nicolardot, B. (2005). Effect of soil covering on nitrogen availability of vineyards soils in champagne area. Journal International Des Sciences De La Vigne Et Du Vin 39, 163-177.
# Silobag Storage: A Novel Technology for Modern Farming Systems

Hernán A. Urcola *±1 & Ricardo E. Bartosik 1

## 1 Introduction

Argentina exhibits a storage capacity deficit of about 30% of the average harvest of wheat, corn, soybeans and sunflower. This deficit limits the ability to store and wait for higher prices after the harvest season and increases the fees of elevator services. Additionally, the grain hauling fleet is insufficient and during harvest time combine work have to be interrupted to wait for available hauling trucks. In recent years, the widespread silobag adoption alleviated Argentina's inefficiencies in the production, marketing and exporting channels of the main commodities. The silobag technology overcomes many of the limitations of traditional on-farm storage facilities – such as steel bins. In general, investing in permanent storage facilities is not affordable for most farmers with the main constraints including high initial outlay, scarce, expensive and short maturation loans and high levels of economic and productive uncertainty. Instead, the silobag requires a minimal initial outlay, its use can be increased, decreased or stopped completely according to productive and economic conditions and it can be set at a different location each year. This last feature turns critical in a country with a constantly increasing proportion of tenant farming that switches plots every year according to the level of rental fees. Moreover, if the bag integrity is preserved, the storage environment reduces moisture losses and insect proliferation which reduces storage maintenance costs (Young, Parker, and Klose 2009). Beyond the described advantages, preliminary evidence suggests that an additional advantage of the silobag is the ability to return control of grain sales to the grower and improving marketing opportunities and increasing farm returns (Darby and Caddick 2007; Taher et al 2013). The silobag provides the farmer the ability to competitively choose among different buyers which results in a higher selling price. Without the silobag, farmers would store the grain at a commercial elevator, and would be limited to the price bids from that elevator resigning bids from other potential buyers – other elevators or industries. On-farm bins provide similar incentives, but at a much higher cost. The economic mechanisms that improve the value of this technology over other forms of grain storage have not been thoroughly analyzed. In particular, the role of increased marketing opportunities at one point in time –i.e., cross sectional price differences- has been largely overlooked and are modelled explicitly in this paper to understand the full value of this technology. This paper develops a dynamic programming model of soybean storage that quantifies the value of the silobag to a farmer in comparison with the traditional elevator storage.

## 2 Materials and Methods

The model represents a farmer who has to decide at harvest whether to sell his production inmediately or whether to store it using one of two storage alternatives: a silobag or a commercial elevator. If storage is initiated, the selling decision is made once a month (in 12 decision nodes) with the objective of maximizing net returns, i.e., revenue which is a random price times quantity sold minus selling commission, hauling and storage costs. Soybean prices follow a random markow process transitioning from a given state in the current time period to another state in the next time period according to a probability transition matrix of 12 intervals conditional of the grain sold in each period. The transition matrix is estimated using a series of deflated detrended soybean prices, centered at mean price of 250 u$s t⁻<fnref n="1" />, to represent current conditions. The cost of setting the silobag is a single quantity paid at the begining of the storage, while the cost of the elevator storage is a monthly fee paid at the time of marketing the grain. Selling commission and hauling costs are the same for the silobag and the elevator. The silobag increases the bargaining capacity of producers allowing them to negotiate either lower marketing costs or higher prices. For silobag users, such effect is modelled as cross-sectional price differences that represent a set of bids from different potential buyers that the producer can choose from. For each decision node, cross-sectional differences are generated sampling 5 prices from a normal distribution whose mean is the soybean price of that node and a standard deviation of 1 u$s t⁻<fnref n="1" />, as a base for comparisons. The model is solved and 500 Monte Carlo simulations are run to represent the different price paths.

## 3 Results - Discussion

The optimal strategy under the two storage alternatives differs substantially. Using the silobag, it is optimal to store for a longer time period than using the commercial elevator (Table 1). For the low harvest price scenario and storing in the silobag the weighted storage length is of 9.8 months, while storing at the elevator the storage length is of 6.6 months. This is expected since storing in the silobag requires a fixed upfront cost to set up the bag, while the elevator charges a monthly fee per stored ton. Therefore, with the silobag a long storage has no additional costs compared to a short

<footnote n="1">¹ National Institute of Agricultural Technology, E.E.A. Balcarce, CC 276, (7620) Balcarce, Argentina</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: urcola.hernan@inta.gob.ar</footnote>
storage, which allows waiting for prices to increase later in the year. For both storage alternatives and for the low and medium low price scenarios (the price intervals occuring more often at harvest time), it is better to store longer than for the intermediate price scenarios. This occurs because the lower the price, the higher the probabilities that the price increases through the year. For intermediate and higher harvest prices, it is better not to store and selling all the production at harvest. This occurs because the expected price change conditional on the intermediate price levels is either negative or positive but too small to offset the costs of storing under either technology. Because the silobag allows long storage at low cost, using this technology permits capturing higher prices several months after harvest which results in higher selling price and higher net benefits (Table 1). With the silobag, the ton-weighted selling price is up to 2.3% higher and the net benefit is up to 2.8% higher than storing at a commercial elevator.

<table><caption>**Table 1.** Comparison of Storage Strategies using the Silobag and the Elevator Storage for Different Price Scenarios and Producer Bargaining Power.</caption><thead><tr><th></th><th></th><th>Price at Harvest</th><th>Storage Length (months)</th><th>Selling Price (u$s/t)</th><th>Mean Net Benefit (u$s/t)</th></tr></thead><tbody><tr><td rowspan="6">Panel A:<br>Same Price</td><td rowspan="3">Silobag</td><td>Low</td><td>9.8</td><td>234.4</td><td>194.0</td></tr><tr><td>Medium Low</td><td>9.0</td><td>246.8</td><td>206.1</td></tr><tr><td>intermediate</td><td>0.0</td><td>246.7</td><td>212.3</td></tr><tr><td rowspan="3">Elevator</td><td>Low</td><td>6.6</td><td>232.9</td><td>188.8</td></tr><tr><td>Medium Low</td><td>1.8</td><td>241.3</td><td>204.2</td></tr><tr><td>intermediate</td><td>0.0</td><td>246.7</td><td>212.3</td></tr><tr><td rowspan="3">Panel B:<br>Higher Silobag Price</td><td rowspan="3">Silobag</td><td>Low</td><td>8.8</td><td>249.3</td><td>208.5</td></tr><tr><td>Medium Low</td><td>6.8</td><td>259.7</td><td>218.7</td></tr><tr><td>intermediate</td><td>5.7</td><td>263.8</td><td>222.8</td></tr></tbody></table>

When the producer is able to negotiate lower marketing costs or higher selling price, the optimal storage strategy, the final selling price and the net benefit change. It is now optimal to store during a shorter period of time for the low and medium low price scenarios and it is convenient to set up the silobag and store under the intermediate price scenario (Table 1, Panel B). When the producer can improve the average selling price by 1 u$s t$^{-1}$, the weighted selling price increases by about 7% for the three price scenarios compared to the elevator storage and the net benefit increases by 10% and by 4.9% for the low and intermediate price scenarios, respectively.

## 4 Conclusions

This paper develops a dynamic model of soybean storage quantifying the value of the silobag to a farmer in comparison with the traditional elevator storage. The model considers that the use of silobag can generate higher bargaining power by which producers can obtain higher selling margins. Results presented indicate that the use of silobag provides more flexibility to wait for higher prices during the storage season and can generate higher economic benefits, even if no extra bargaining power is considered. When lower marketing costs or higher selling prices are obtained the silobag's economic benefits increase considerably.

Beyond the described economic benefits, the use of silobags is able to generate advantages that can enhance the whole farm operation and extend its benefits to regional- and national-level logistics. First, the silobag provides storage capacity requiring minimal capital investment in facilities and equipment. Making this technology apt to high-uncertainty environments. Second, the silobag use reduce harvest delays and provides variable storage capacity that can be varied from year to year and set in different places every year. Thus, alleviating regional- or national-level storage and hauling capacity deficits. Third, if the silobag integrity is preserved, the storage environment reduces moisture losses and insect proliferation. Because of these advantages, the silobag is being rapidly adopting by producers and grain processing industries throughout the world, both in developed and developing countries.

*Acknowledgements.* This study has received funding from the National Program for the Development and Sustainability of the Territories, INTA Argentina, through Project PNSEPT 1129023.

## References

* Darby, J. A., and L. P. Caddick. 2007. Review of Grain Harvest Bag Technology under Australian Conditions. CSIRO Entomology 105. Australia.

* Young, M., R. Parker, and S. Klose. 2009. Economics of Grain Storage Bags in the Coastal Bend and Upper Gulf Coast of Texas. *Farm Assistance Focus. Texas AgriLife Extension Service, Texas A&M University System*.

* Taher, H.; Urcola, H.; Cardoso, L.; Bartosik, R. 2014. Percepción de los productores sobre beneficios y problemáticas en el almacenamiento en silo bolsa. En Gastón, A.; Abalone, R.; Bartosik, R. [Eds] Memorias del Primer Congreso Internacional de Almacenamiento de Granos en Silo Bolsa (ISBN 978-987-3662-06-5). Del 13 al 16 de Octubre de 2014, Mar del Plata/Balcarce, Argentina. Disponible en: http://inta.gob.ar/documentos/1er-congreso-internacional-de-silo-bolsa
# The effects of different tillage methods on soil temperature, water content, bulk density, and yield of winter wheat in southern Henan of China

De-qi Zhang<sup>±1</sup> & Xiang-dong Li<sup>1</sup>

## 1 Introduction

No-tillage can promote soil porosity and improve infiltration, soil structure and decomposition of organic matter. Its soil biological activities were regulated by earthworms (Edwards, 1992; Hendrix, 1992). Soil tillage by soil disturbance or straw cover changed the soil surface, thereby affected the heat flux condition of the soil (Mark & Mahdi, 2005). No-tillage and its impacts on root distribution of winter wheat and soil temperature were studied in arid and semi-arid regions (Chen et al. 2009). The objectives of this study were to explore the effects of different tillage modes on soil temperature and root system, realize the innovation of regional application and technology and provide feasible theoretical basis for agricultural production.

## 2 Materials and Methods

The experiment was conducted at Xiping, China, from 2007 to 2014. The content of total N, available-phosphate, available- potassium and organic matter were 0.94 g/kg, 89 mg/kg, 105 mg/kg and 1.19 mg/kg, respectively. Zhengmai 366, a strong gluten wheat cultivar, was used in this study. Five treatments including with different tillage methods were conducted (Table 1).

<table><caption>Table 1. Experiment treatments</caption><thead><tr><th>Treatment</th><th>T<sub>1</sub></th><th>T<sub>2</sub></th><th>T<sub>3</sub></th><th>T<sub>4</sub></th><th>T<sub>5(ck)</sub></th></tr></thead><tbody><tr><td>Specific measures</td><td>Straw mulching and pre-sowing deep plough for corn + straw mulching and non-tillage for wheat</td><td>Pre-sowing deep plough for corn + non-tillage for wheat</td><td>Straw mulching for corn + non-tillage for wheat</td><td>Straw mulching and non-tillage for wheat</td><td>Traditional cultivation</td></tr></tbody></table>

## 3 Discussion

<table><caption>Table 2. Effects of different tillage methods on soil water content and bulk density</caption><thead><tr><th rowspan="3">Treatments</th><th colspan="6">Before sowing</th><th colspan="3">Harvest</th></tr><tr><th colspan="3">soil water content(%)</th><th colspan="3">soil bulk density(g/cm³)</th><th colspan="3">soil bulk density(g/cm³)</th></tr><tr><th>0-10cm</th><th>10-20cm</th><th>20-40cm</th><th>0-10cm</th><th>10-20cm</th><th>20-40cm</th><th>0-10cm</th><th>10-20cm</th><th>20-40cm</th></tr></thead><tbody><tr><td>T1</td><td>23.2</td><td>22.0</td><td>20.3</td><td>1.41</td><td>1.41</td><td>1.44</td><td>1.29</td><td>1.40</td><td>1.44</td></tr><tr><td>T2</td><td>20.8</td><td>20.5</td><td>17.9</td><td>1.30</td><td>1.38</td><td>1.47</td><td>1.44</td><td>1.44</td><td>1.47</td></tr><tr><td>T3</td><td>19.4</td><td>19.5</td><td>20.3</td><td>1.45</td><td>1.41</td><td>1.54</td><td>1.49</td><td>1.55</td><td>1.64</td></tr><tr><td>T4</td><td>20.6</td><td>22.6</td><td>21.2</td><td>1.48</td><td>1.52</td><td>1.56</td><td>1.20</td><td>1.55</td><td>1.57</td></tr><tr><td>CK</td><td>19.4</td><td>19.5</td><td>20.3</td><td>1.34</td><td>1.40</td><td>1.41</td><td>1.32</td><td>1.35</td><td>1.46</td></tr></tbody></table>

There were about 25-30 days from maize harvest to wheat seeding in southern Henan of China. The results showed that soil water content before sowing was 2% higher in straw mulching treatments (T1 and T4) than that of other non straw mulching treatments (Table. 2).

As it was shown in Table. 2, straw mulching treatment has a significant impact on the bulk density of the soil from 0-10cm soil layer. Deep plough treatments significantly reduced the bulk density of the soil from 0-40cm soil layer.

The difference of the average temperature of the soil among different treatments was less than 0.3 °C during the wintering stage, and was less than 0.6 °C during turning green stage(Fig. 1). The soil temperature of the treatment T1 and T2 were 0.1 °C and 0.2 °C higher than that of the control, respectively. The soil temperature of T3 and T4 was 0.1 °C and 0.3 °C lower than that of control respectively. There were no significant difference in the soil temperature in wintering and turning greening stage of winter wheat among different treatments.

As it was shown in Fig. 2, the root dry matter density of control (CK) in soil layer 0-10cm in wintering period, turning green stage and filling stage was the lowest, but that of which in soil layer 10-20cm was the maximum. Compared with control (CK), the root dry matter density of the soil layer 0-10cm in the treatment of T1, T2, T3 and T4 at filling stage were increased by 0.12kg/m³, 0.13 kg/m³, 0.11 kg/m³ and 0.09 kg/m³, respectively. And the root dry matter density of the soil layer 0-20cm on T1, T2, T3 and T4 treatments were increased by 7.0%、9.3%、7.0%、4.7%, respectively.

<footnote n="1">¹ Wheat Research Institute, Henan Academy of Agricultural Sciences; National Laboratory of Wheat Engineering; Key Laboratory of Wheat Biology and Genetic Breeding in Central Huang-Huai Region; Ministry of Agriculture & Henan Provincial Key Laboratory of Wheat Biology</footnote>

<footnote>* Speaker</footnote>

<footnote>‡ Corresponding author: dqzhang3@163.com</footnote>
<figure><img src="image_34.png" /><figcaption>Fig. 1. The average soil temperature during wintering stage and turning green stage</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>Fig. 2. The distribution of root system</figcaption></figure>

From 2007 to 2013, the average yields of T1, T2, T3 and T4 were 1.44%, 4.20%, 5.50% and 3.99% lower than that of CK, respectively (Table 3). The yields of T1 treatment was increased by 2.02% compared with that of control in 2010-2011. Compared with control, from 2010 to 2013, the yields of T1 was 2.02%, 2.83%, 10.93% higher than that of CK, respectively. With the increase of the experimental years, no-tillage and straw cover technology can slightly improve the yield. At the same time, the cost reduction and ecological effect is obvious.

<table><caption>Table 3. Yield under different treatments</caption><thead><tr><th rowspan="2">Treatments</th><th colspan="6">Yield(kg·hm<sup>-2</sup>)</th></tr><tr><th>2007-2008</th><th>2008-2009</th><th>2009-2010</th><th>2010-2011</th><th>2011-2012</th><th>2012-2013</th></tr></thead><tbody><tr><td>T1</td><td>7315.5a</td><td>7480.5a</td><td>7124.5a</td><td>9033.0a</td><td>8172.0a</td><td>4367.7a</td></tr><tr><td>T2</td><td>7374.0a</td><td>7225.5b</td><td>6729.5b</td><td>8779.5ab</td><td>7671.0bc</td><td>3413.0c</td></tr><tr><td>T3</td><td>7125.0a</td><td>7243.5b</td><td>6657.0b</td><td>8479.5b</td><td>7354.5c</td><td>3677.3bc</td></tr><tr><td>T4</td><td>7233.0a</td><td>7122.0b</td><td>6994.5ab</td><td>8800.5ab</td><td>7483.5c</td><td>3502.7c</td></tr><tr><td>CK</td><td>7465.5a</td><td>7545.0a</td><td>7230.0a</td><td>8854.5ab</td><td>7947.0ab</td><td>3937.5b</td></tr></tbody></table>

## 4 Conclusions

Better soil moisture was provided by straw mulching in the sowing period of winter wheat. Deep plough reduced the soil bulk density. No-tillage technology increased root dry matter density of the soil layer 0-10cm in filling stage. For no-tillage and deep loosening, one or two seasons of straw returning have influenced the distribution of root system. There were no significant difference of the changes of the soil temperature under turning green and jointing stage between no-tillage and conventional tillage treatment. The difference of the soil temperature was not sufficient to affect the growth and development of the winter wheat.

The seeding quality still needs to be improved to improve seed germination rate. Compared with control(CK), No-tillage treatments reduced the yield slightly at the first three years, but slightly increased yields in the later years.

*Acknowledgements.* This work was financially supported by the National Key Technology R&D Program (2011BAD16B07, 2012BAD14B08 and 2012BAD04B07), and Special Fund for Agro-scientific Research in the Public Interest (201203033), and Special Research Development Fund Project of Henan Academy of Agricultural Sciences(201218301).

## References

* Edwards W M. (1992). Role of lumbriens terrestrials burrows on quality of infiltration water. *Soil Biol Biochem*, 24(2): 1555-1561.

* Hendrix P F. (1992). A bundance and distribution of earthworm in relation to landscape factors on the Georgia Piedmont. *Soil Boil Biochem*, 24(12): 1357-1361.

* Mark A L & Mahdi A. (2005). Strip- tillage effect on seedbed soil temperature and other soil physical properties. *Soil & Tillage Research*, 80:233-249.

* Chen J K, Zhang Y, Chen J S, Chen F & Zhang H L. (2009). Characteristics of soil temperature and its response to air temperature under different tillage systems-characteristics of soil temperature and thermal properties. *Scientia Agricultura Sinica*, 42 (8): 2747-2753.
# T2. Assessing performances and services of farming systems (farm level surveys and databases)

Chair: Jean-Marc Barbier, INRA

Co-chair: Wei-li Liang, Hebei Agricultural University
# Adaptations of farming systems to cities: periurban farms beyond the short food-supply chains

Marraccini Elisa ±<fnref n="1" />,<fnref n="2" />, Houdart Marie <fnref n="3" />, Debolini Marta <fnref n="4" />, Filippini Rosalia <fnref n="2" />, Lardon Sylvie <fnref n="5" />& Bonari Enrico <fnref n="2" />

## 1 Introduction

There is an increasing attention to periurban agriculture, mainly related to the services provided by maintaining agriculture nearby the city (Zasada, 2011) and to the conflicts related to the use of the resources (Darly & Torre, 2014). However, few attention is given to the farm management in periurban areas (Aubry & Soulard, 2013), looking more on its environmental impacts or on the contribution of farmers to the development of short food-supply chains. Following Nahmia & Le Caro (2012), we defined periurban farms as all the farms that are located within an urban planning area, e.g. urban municipalities or inter-municipalities. This means that all these farms, besides their farming systems and their way of marketing their produce, are under the influence of the city. This influence has been considered as a factor of adaptation of different farming systems (Soulard & Thureau, 2009; Houdart et al., 2012) including those which are not oriented short food supply chains (Capillon & David, 1996). In this research, we aimed at identifying the main types of periurban farming systems to highlight their different adaptations to city.

## 2 Materials and Methods

We study the adaptation of periurban farming systems to cities through the elaboration of a statistical-based farm typology according to Landais (1998). We applied our method to the urban region of Pisa (Italy), a medium-sized city leading an inter-municipal area of almost 200,000 inhabitants which experienced a diffuse urbanization since the eighties (Marraccini et al., 2013). The input data for the typology have been acquired through on-farm surveys on a sample derived from the individual Land Parcel Identification System database according to the dominant land use, the farm size and the farmland distance to the city. Starting from this sample, 55 farms were selected and surveyed using a semi-structured interview. The main items of this interview were the farm location and history, the land tenure, the management of crops, livestock and semi-natural habitats, the commercial practices, the farmer and its family and the farm perspectives. The variables used in the typology described the farm territory and the farm management, the land use intensity and the individual farmer characteristics.

<figure><img src="image_36.png" /><figcaption>**Fig. 1.** PCA of the set of variables (see codes in the text, UBGT= livestock units, Org= organic farm, Nat= presence of SNH). Each symbol represent a farm with a dominant land use type, e.g. empty circles are the industrial crop farms.</figcaption></figure>

<footnote n="1">¹ UP 2012-10-103 PICAR-T, Institut Polytechnique LaSalle Beauvais, France</footnote>

<footnote n="2">² Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy</footnote>

<footnote n="3">³ UMR 1273 Métafort, Irstea, Aubière, France</footnote>

<footnote n="4">⁴ UMR EMMAH, INRA, Avignon, France</footnote>

<footnote n="5">5 UMR 1273 Métafort, INRA &AgroParisTech, Aubière, France</footnote>

<footnote>* Speaker</footnote>

<footnote>± Corresponding author: elisa.marraccini@lasalle-beauvais.fr</footnote>
These variables were selected on an initial set of 48 static and dynamic ones using a descriptive analysis followed by a PCA and explained 65% of the farm sample variance (Fig. 1). Finally, a non-hierarchical cluster analyses (CA) on the final set of 10 variables and on the 51 finally considered farms allowed obtaining 7 different farm types (Ward's method, Euclidean distance). All the variables used appeared significantly different among the farm types (Kruskal-Wallis test).

## 3 Results – Discussion

Seven types were identified on our sample of 51 farms (data not shown). Three types (T1, T2 and T4) are composed of cereal farms differing on farm size (UAA), farmland fragmentation (Nblocks) and crop rotation length (Nsp), whereas two of them (T5 and T6) are forage and livestock-oriented farms differing for the farm size (UAA), the share of forage surface (SUAAf) and the marketing of produce in alternative or conventional food chains (Nfc). One type concerned organic olive groves farms (T7) and the last one (T3) little and conventional market-oriented vegetable farms. Our results underlined that 1) the farm types are not always linked to the main productive orientation of farms, except in the case of the specialized farms (olive groves and vegetables) and 2) not all the farm types located in the urban region have functional relationships to the city, e.g. the big conventional arable farms. These functional relationships are revealed not only by the local marketing of farm products but also by the fragmentation of land tenure, the bonds of the farmer, the multifunctionality of the farm, the intensity of the agricultural practices (Fig. 2). Through a spatial analysis of the farm types field blocks' location (data not shown) we found a statistically significant negative correlation between types having a functional relation to the city linked to short food-supply chains and the distance of the farm to the city, suggesting that the farms mostly oriented to urban market find their farmland farer from the cities than the other farms were there are less constraints and conflicts for their activities.

<figure><img src="image_37.png" /><figcaption>**Fig. 2.** Examples of features of the periurban farm types (T1 to T7, see text) in the urban region of Pisa (Italy).</figcaption></figure>

## 4 Conclusions

By studying the farm types in periurban areas, we showed 1) the high diversity of farming systems even though within a same dominant land use type and 2) the different adaptations of these farms to the city. Understanding these points can contribute to a better understanding of the resilience of farming systems in periurban, linked to the main issues at stake in this area: the protection and management of water resources, agricultural land and of the local food system.

*Acknowledgements.* This work has been carried out within the research project DAUME (Durabilité de l'agriculture urbaine en Méditerranée) funded by the French National Research Agency under the contract n°ANR-2010-STRA-007-01.

## References

Capillon, A. & David, G. (1996). Exploitations de grandes cultures et espaces périurbains dans le Vexin français. *Cahiers Agriculture*, **5**,77-82.

Darly, S. & Torre, A. (2013). Conflicts over farmland uses and the dynamics of “agri-urban” localities in the Greater Paris Region: An empirical analysis based on daily regional press and field interviews. *Land Use Policy*, **33**,90-99.

Houdart, M., Loudiyi, S., Gueringer, A. (2012). L'adaptation des agriculteurs au contexte périurbain. Une lecture des logiques agricoles à partir du cas de Billom-Saint-Dier. *Norois*, **3**, 35-48.

Landais, E. (1998). Modelling farm diversity. New approaches to typology building in France. *Agricultural Systems*, **58**,505-527.

Lange, A., Piorr, A., Siebert, R. & Zasada, I. (2013). Spatial differentiation of farm diversification: how rural attractiveness and vicinity to city determine farmers reponse to the CAP. *Land Use Policy*, **31**,136-144.

Marraccini, E., Lardon, S., Loudiyi, S., Giacché, G. &Bonari, E. (2013). Durabilité de l'agriculture dans les territoires périurbains méditerranéens: enjeux et projets agriurbains dans la région de Pise (Toscane, Italie). *Cahiers Agriculture*, **22**,517-525.

Milestad, R., Dedieu, B., Darnhofer, I. & Bellon, S. (2012). Farms and farmers facing change: the adaptive approach. In: Darnhofer, I., Gibbon, D. & Dedieu, B. (Eds), *Farming System Research into the 21st Century: the new dynamic*, Springer, 365-385.

Nahmias, P., Le Caro Y. (2012). Pour une définition de l'agriculture urbaine : reciprocité fonctionelle et diversité des formes spatiales. *Urban Environment*, **6**,1-16.

Soulard, C., & Thareau, B. (2009). Les exploitations agricoles péri-urbaines: diversités et logiques de développement. *Innovations Agronomiques*, **5**,27-40.

Zasada, I., Loibl, W., Kostl, M. & Piorr, A. (2013). Agriculture under human influence: a spatial analysis of farming systems and land use in European rural-urban regions. *European Countryside*, **1**,71-88.