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Daccache, A., Ciurana, J. S., Diaz, J. A. R., & Knox, J. W. (2014). Water and energy footprint of irrigated agriculture in the Mediterranean region. Environ. Res. Lett., 9(12), 124014.
Abstract: Irrigated agriculture constitutes the largest consumer of freshwater in the Mediterranean region and provides a major source of income and employment for rural livelihoods. However, increasing droughts and water scarcity have highlighted concerns regarding the environmental sustainability of agriculture in the region. An integrated assessment combining a gridded water balance model with a geodatabase and GIS has been developed and used to assess the water demand and energy footprint of irrigated production in the region. Modelled outputs were linked with crop yield and water resources data to estimate water (m(3) kg(-1)) and energy (CO2 kg(-1)) productivity and identify vulnerable areas or `hotspots’. For a selected key crops in the region, irrigation accounts for 61 km(3) yr(-1) of water abstraction and 1.78 Gt CO2 emissions yr-1, with most emissions from sunflower (73 kg CO2/t) and cotton (60 kg CO2/t) production. Wheat is a major strategic crop in the region and was estimated to have a water productivity of 1000 tMm(-3) and emissions of 31 kg CO2/t. Irrigation modernization would save around 8 km(3) of water but would correspondingly increase CO2 emissions by around +135\%. Shifting from rain-fed to irrigated production would increase irrigation demand to 166 km(3) yr(-1) (+137\%) whilst CO2 emissions would rise by +270\%. The study has major policy implications for understanding the water-energy-food nexus in the region and the trade-offs between strategies to save water, reduce CO2 emissions and/or intensify food production.
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Webber, H., Gaiser, T., Oomen, R., Teixeira, E., Zhao, G., Wallach, D., et al. (2016). Uncertainty in future irrigation water demand and risk of crop failure for maize in Europe. Environ. Res. Lett., .
Abstract: While crop models are widely used to assess the change in crop productivity with climate change, their skill in assessing irrigation water demand or the risk of crop failure in large area impact assessments is relatively unknown. The objective of this study is to investigate which aspects of modeling crop water use (reference crop evapotranspiration (ET0), soil water extraction, soil evaporation, soil water balance and root growth) contributes most to the variability in estimates of maize crop water use and the risk of crop failure, and demonstrate the resulting uncertainty in a climate change impact study for Europe. The SIMPLACE crop modeling framework was used to couple the LINTUL5 crop model in factorial combinations of 2-3 different approaches for simulating the 5 aspects of crop water use, resulting in 51 modeling approaches. Using experiments in France and New Zeland, analysis of total sensitivity revealed that ET0 explained the most variability in both irrigated maize water use and rainfed grain yield levels, with soil evaporation also imporatant in the French experiment. In the European impact study, net irrigation requirement differed by 36% between the Penman and Hargreaves ET0 methods in the baseline period. Average EU grain yields were similar between models, but differences approached 1-2 tonnes in parts of France and Southern Europe. EU wide esimates of crop failure in the historical period ranged between 5.4 years for Priestley-Taylor to every 7.9 years for the Penman ET0 methods. While the uncertainty in absolute values between models was significant, estimates of relative changes were similar between models, confirming the utility of crop models in assessing climate change impacts. If ET0 estimates in crop models can be improved, through the use of appropriate methods, uncertainty in irrigation water demand as well as in yield estimates under drought can be reduced.
Keywords: crop model; impact assessment; crop water use; evapotranspiration; irrigation; drought; uncertainty
Area: CropM
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Bodirsky, B. L., & Müller, C. (2014). Robust relationship between yields and nitrogen inputs indicates three ways to reduce nitrogen pollution. Environ. Res. Lett., 9(11), 111005.
Abstract: Historic increases in agricultural production came at the expense of substantial environmental burden through nitrogen pollution. Lassaletta et al (2014 Environ. Res. Lett. 9 105011) examine the historic relationship of crop yields and nitrogen fertilizer inputs globally and find a simple and robust relationship of declining nitrogen use efficiency with increasing nitrogen inputs. This general relationship helps to understand the dilemma between increased agricultural production and nitrogen pollution and allows identifying pathways towards more sustainable agricultural production and necessary associated policies.
Keywords: nitrogen use efficiency; nitrogen; fertilizer; nitrogen pollution; agriculture; yields; mitigation; framework
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Mansouri, M., & Destain, M. - F. (2015). Predicting biomass and grain protein content using Bayesian methods. Stoch. Environ. Res. Risk Assess., 29(4), 1167–1177.
Abstract: This paper deals with the problem of predicting biomass and grain protein content using improved particle filtering (IPF) based on minimizing the Kullback-Leibler divergence. The performances of IPF are compared with those of the conventional particle filtering (PF) in two comparative studies. In the first one, we apply IPF and PF at a simple dynamic crop model with the aim to predict a single state variable, namely the winter wheat biomass, and to estimate several model parameters. In the second study, the proposed IPF and the PF are applied to a complex crop model (AZODYN) to predict a winter-wheat quality criterion, namely the grain protein content. The results of both comparative studies reveal that the IPF method provides a better estimation accuracy than the PF method. The benefit of the IPF method lies in its ability to provide accuracy related advantages over the PF method since, unlike the PF which depends on the choice of the sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of this sampling distribution, which also utilizes the observed data. The performance of the proposed method is evaluated in terms of estimation accuracy, root mean square error, mean absolute error and execution times.
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Knox, J., Daccache, A., Hess, T., & Haro, D. (2016). Meta-analysis of climate impacts and uncertainty on crop yields in Europe. Environ. Res. Lett., 11, 113004.
Abstract: Future changes in temperature, rainfall and soil moisture could threaten agricultural land use and crop productivity in Europe, with major consequences for food security. We assessed the projected impacts of climate change on the yield of seven major crop types (viz wheat, barley, maize, potato, sugar beet, rice and rye) grown in Europe using a systematic review (SR) and meta-analysis of data reported in 41 original publications from an initial screening of 1748 studies. Our approach adopted an established SR procedure developed by the Centre for Evidence Based Conservation constrained by inclusion criteria and defined methods for literature searches, data extraction, meta-analysis and synthesis. Whilst similar studies exist to assess climate impacts on crop yield in Africa and South Asia, surprisingly, no comparable synthesis has been undertaken for Europe. Based on the reported results (n = 729) we show that the projected change in average yield in Europe for the seven crops by the 2050s is +8%. For wheat and sugar beet, average yield changes of +14% and +15% are projected, respectively. There were strong regional differences with crop impacts in northern Europe being higher (+14%) and more variable compared to central (+6%) and southern (+5) Europe. Maize is projected to suffer the largest negative mean change in southern Europe (−11%). Evidence of climate impacts on yield was extensive for wheat, maize, sugar beet and potato, but very limited for barley, rice and rye. The implications for supporting climate adaptation policy and informing climate impacts crop science research in Europe are discussed.
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