• Energy Research

Recently, an active debate has emerged around greenhouse gas emissions due to indirect land use change (iLUC) of expanding agricultural areas dedicated to biofuel production. In this paper we provide a detailed analysis of the iLUC effect, and further address the issues of deforestation, irrigation water use, and crop price increases due to expanding biofuel acreage. We use GLOBIOM – an economic partial equilibrium model of the global forest, agriculture, and biomass sectors with a bottom-up representation of agricultural and forestry management practices. The results indicate that second generation biofuel production fed by wood from sustainably managed existing forests would lead to a negative iLUC factor, meaning that overall emissions are 27% lower compared to the “No biofuel” scenario by 2030. The iLUC factor of first generation biofuels global expansion is generally positive, requiring some 25 years to be paid back by the GHG savings from the substitution of biofuels for conventional fuels. Second generation biofuels perform better also with respect to the other investigated criteria; on the condition that they are not sourced from dedicated plantations directly competing for agricultural land. If so, then efficient first generation systems are preferable. Since no clear technology champion for all situations exists, we would recommend targeting policy instruments directly at the positive and negative effects of biofuel production rather than at the production itself.

We have estimated soil organic carbon and crop yield changes under distinct climate change scenarios for the Kočín farm in Slovakia. Two regional climate change scenarios, i.e. the A2 and B2 SRES emission scenarios, and a reference climate scenario have been included into the bio-physical process model EPIC to simulate the effects on the topsoil organic carbon stocks and crop yields for the period of 2010-2050. In addition, we have used the data from several fields of the Kočín farm including the soil data, crop rotational and management data as well as topographical data. The topsoil organic carbon stocks show a decreasing trend for the period of 2010-2050. Among all crop rotation systems and soil profiles, the losses over the period are 9.0%, 9.5%, and 10.7% for the reference, A2, and B2 climate scenarios, respectively. Increasing temperatures accelerate the decomposition of the soil organic carbon particularly when soils are intensively managed. The soil organic carbon changes are crop-rotation specific, which is partly due to the climate scenarios that affect the crop biomass production differently. This is shown by comparison of the crop yields. We conclude that EPIC is capable to reliably simulate effects of climate change on soil organic carbon and crop yields.

Potential climate-related impacts on future crop yield are a major societal concern. Previous projections of the Agricultural Model Intercomparison and Improvement Project's Global Gridded Crop Model Intercomparison based on the Coupled Model Intercomparison Project Phase 5 identified substantial climate impacts on all major crops, but associated uncertainties were substantial. Here we report new twenty-first-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5% to -6% (SSP126) and from +1% to -24% (SSP585)-explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9% shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The 'emergence' of climate impacts consistently occurs earlier in the new projections-before 2040 for several main producing regions. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

Over the next decades mankind will demand more food from fewer land and water resources. This study quantifies the food production impacts of four alternative development scenarios from the Millennium Ecosystem Assessment and the Special Report on Emission Scenarios. Partially and jointly considered are land and water supply impacts from population growth, and technical change, as well as forest and agricultural commodity demand shifts from population growth and economic development. The income impacts on food demand are computed with dynamic elasticities. Simulations with a global, partial equilibrium model of the agricultural and forest sectors show that per capita food levels increase in all examined development scenarios with minor impacts on food prices. Global agricultural land increases by up to 14% between 2010 and 2030. Deforestation restrictions strongly impact the price of land and water resources but have little consequences for the global level of food production and food prices. While projected income changes have the highest partial impact on per capita food consumption levels, population growth leads to the highest increase in total food production. The impact of technical change is amplified or mitigated by adaptations of land management intensities.

Uncertainty and variability are key challenges for climate change adaptation planning. In the face of uncertainty, decision-making can be addressed in two interdependent stages: make only partial ex ante anticipative actions to keep options open until new information is revealed, and adapt the first-stage decisions with respect to newly acquired information. This decision-making approach corresponds to the two-stage stochastic optimization (STO) incorporating both anticipative ex ante and adaptive ex post decisions within a single model. This paper develops a two-stage STO model for climate change adaptation through robust land use and irrigation planning under conditions of uncertain water supply. The model identifies the differences between decision-making in the cases of perfect information, full uncertainty, and two-stage STO from the perspective of learning about uncertainty. Two-stage anticipative and adaptive decision-making with safety constraints provides risk-informed decisions characterized by quantile-based Value-at-Risk and Conditional Value-at-Risk risk measures. The ratio between the ex ante and ex post costs and the shape of uncertainty determine the balance between the anticipative and adaptive decisions. Selected numerical results illustrate that the alteration of the ex ante agricultural production costs can affect crop production, management technologies, and natural resource utilization.

Abstract Potential climate-related impacts on future crop yield are a major societal concern first surveyed in a harmonized multi-model effort in 2014. We report here on new 21st-century projections using ensembles of latest-generation crop and climate models. Results suggest markedly more pessimistic yield responses for maize, soybean, and rice compared to the original ensemble. Mean end-of-century maize productivity is shifted from +5 to -6% (SSP126) and +1 to -24% (SSP585) — explained by warmer climate projections and improved crop model sensitivities. In contrast, wheat shows stronger gains (+9 shifted to +18%, SSP585), linked to higher CO2 concentrations and expanded high-latitude gains. The ‘emergence’ of climate impacts — when the change signal emerges from the noise — consistently occurs earlier in the new projections for several main producing regions before 2040. While future yield estimates remain uncertain, these results suggest that major breadbasket regions will face distinct anthropogenic climatic risks sooner than previously anticipated.

Traditional agricultural extension services rely on extension workers, especially in countries with large agricultural areas. In order to increase adoption of sustainable agriculture, the recommendations given by such services must be adapted to local conditions and be provided in a timely manner. The AgroTutor mobile application was built to provide highly specific and timely agricultural recommendations to farmers across Mexico and complement the work of extension agents. At the same time, AgroTutor provides direct contributions to the United Nations Sustainable Development Goals, either by advancing their implementation or providing local data systems to measure and monitor specific indicators such as the proportion of agricultural area under productive and sustainable agriculture. The application is freely available and allows farmers to geo-locate and register plots and the crops grown there, using the phone’s built-in GPS, or alternatively, on top of very high-resolution imagery. Once a crop and some basic data such as planting date and cultivar type have been registered, the application provides targeted information such as weather, potential and historical yield, financial benchmarking information, data-driven recommendations, and commodity price forecasts. Farmers are also encouraged to contribute in-situ information, e.g., soils, management, and yield data. The information can then be used by crop models, which, in turn, send tailored results back to the farmers. Initial feedback from farmers and extension agents has already improved some of the application’s characteristics. More enhancements are planned for inclusion in the future to increase the application’s function as a decision support tool.