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Recent calls for a global food transformation have centered on simultaneously improving human and environmental health, recognizing that food and nutrient diversity have declined over time while food systems have exacted a heavy climate and ecological toll. Grain legumes and coarse grain crops provide important human nutrition and environmental benefits, but the production and consumption of many of these crops remains relatively low compared to major commodities, such as maize, wheat, rice, and soy. Outstanding hurdles to scaling up these “minor commodity” crops include (among other things) their relatively lower yields, and lower farmer adoption, based partly on actual or perceived profitability and marketability. We hypothesize that these limitations are attributable in part to unequal funding for these crops' research and development (R&D) both on a national and global scale. In the United States, we show that investment patterns for a snapshot of USDA-funded research grants from 2008 to 2019 consistently favor major commodity crops, which received 3 to 4.5 times more funding and 3 to 5 times as many grants than the minor commodity crop groups. This current USDA funding allocation poses a barrier to food system transformations. Achieving nutritious diets for planetary health requires more public agricultural investment toward minor commodity crops and increased collaboration between public health, nutrition, agriculture, and environmental sectors.

Global ecosystem changes have multiple drivers, including both natural variability and anthropogenic climate and environmental change [...]

Land-use change affects both the quality and quantity of soil organic carbon (SOC) and leads to changes in ecosystem functions such as productivity and environmental regulation. Future changes in SOC are, however, highly uncertain owing to its heterogeneity and complexity. In this study, we analyzed the outputs of simulations of SOC stock by Earth system models (ESMs), most of which are participants in the Land-Use Model Intercomparison Project. Using a common protocol and the same forcing data, the ESMs simulated SOC distribution patterns and their changes during historical (1850–2014) and future (2015–2100) periods. Total SOC stock increased in many simulations over the historical period (30 ± 67 Pg C) and under future climate and land-use conditions (48 ± 32 Pg C for ssp126 and 49 ± 58 Pg C for ssp370 ). Land-use experiments indicated that changes in SOC attributable to land-use scenarios were modest at the global scale, in comparison with climatic and rising CO _2 impacts, but they were notable in several regions. Future net soil carbon sequestration rates estimated by the ESMs were roughly 0.4‰ yr ^−1 (0.6 Pg C yr ^−1 ). Although there were considerable inter-model differences, the rates are still remarkable in terms of their potential for mitigation of global warming. The disparate results among ESMs imply that key parameters that control processes such as SOC residence time need to be better constrained and that more comprehensive representation of land management impacts on soils remain critical for understanding the long-term potential of soils to sequester carbon.

The impact of climate change on agricultural crops is a major concern and threats to the global food security. It also limits the potential of crops and cropping system in a given area. Therefore, the present study was aimed to assess the combined effect of positive (CO2 fertilization, lesser temperature and higher rainfall) and negative (higher temperature, lower rainfall) impacts of the futuristic climatic scenarios on productivity of soybean using APSIM (Agricultural Production Systems sIMulator) model. We have followed the Climate-Crop Modeling Project (C3MP) methodology and generated ninety-nine sensitive test to achieve each test’s temperature, rainfall and CO2 concentration range. Using 30 years of climate data (1980-2010) of Central India as base, the simulation results showed that increasing CO2 concentrations alone resulted in increased soybean yield. Similarly, reduction in rainfall amount indicated negative impact on it. This effect further compounded with increase in temperature and thus, reduced soybean yield. Increasing the temperature with 10% decrease in rainfall declined the soybean yield by 10%. Whereas, increase in temperature along with increase in rainfall also not resulted favorably soybean growth. Decreasing the temperature from the base by 1oC and increasing the rainfall by more than 10% benefitted the soybean productivity, whereas increasing the temperature by 1oC with no change in rainfall resulted decline in soybean productivity by 10-15%.

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.