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Highlights: [•] Inputs and crop yields under different management levels are quantified. [•] Climate and soil related yield constraints are identified for different regions. [•] Highest maize yield potential was identified for tropical regions. [•] Modest improvement in nutrient supply could double or triple maize yields. [Copyright &y& Elsevier]

Abstract: Large-scale modeling applications are associated with various assumptions and spatial resolutions. In this study, the GIS-based Environmental Policy Integrated Climate (GEPIC) model was used to examine the effects of resampling input data from a resolution of 5arcmin to 10 and 30arcmin on simulated crop grain yields. Maize cultivation in the USA was used as a case study. The biggest impact was found to be the resampling of land use datasets. Rain-fed and irrigated areas are simulated separately and the yields are subsequently weighted according to irrigated and rain-fed fractions in each grid cell. The aggregation causes some grid cells to become rain-fed and irrigated at coarser resolutions after being only rain-fed or only irrigated at 5arcmin. The estimated yield can increase or decrease largely in the affected grid cells due to the fact that irrigated areas generally have much higher yields than rain-fed agriculture in dry regions. The resampling of the climate data has a low impact on crop yields. However, changes in yield can still be large in regions with rain-fed agriculture and low precipitation. The aggregation of soil data by selecting the major soil type within neighboring grid cells has the lowest impact on crop yields. The impact of resampling input data from 5 to 10 and 30arcmin is not significant when modeled maize yields were aggregated on the US national scale, and still quite comparable at the state level. Therefore, for estimating agricultural productivity over large areas, the coarser resolutions can be considered sufficient. Fine resolutions can be important when the goal is to make spatially detailed conclusions. Model performance shows that large deviations between simulated and reported yields can occur at all spatial resolutions in grid cells with harvested areas below 5% of the total cell area. This indicates that a sampling approach that uses representative pixels is preferable to a wall to wall approach using all grid cells. [Copyright &y& Elsevier]

Abstract: Two approaches to determine pesticide (bio-)availability in soils (i) batch experiments with “extraction with an excess of water” (EEW) and (ii) the recently introduced “soil pore water (PW) extraction” of pesticide incubated soil samples have been compared with regard to the sorption behavior of the model compound isoproturon in soils. A significant correlation between TOC and adsorbed pesticide amount was found when using the EEW approach. In contrast, there was no correlation between TOC and adsorbed isoproturon when using the in situ PW extraction method. Furthermore, sorption was higher at all concentrations in the EEW method when comparing the distribution coefficients (K d) for both methods. Over all, sorption in incubated soil samples at an identical water tension (−15kPa) and soil density (1.3gcm−3) appears to be controlled by a complex combination of sorption driving soil parameters. Isoproturon bioavailability was found to be governed in different soils by binding strength and availability of sorption sites as well as water content, whereas the dominance of either one of these factors seems to depend on the individual composition and characteristics of the respective soil sample. Using multiple linear regression analysis we obtained furthermore indications that the soil pore structure is affected by the EEW method due to disaggregation, resulting in a higher availability of pesticide sorption sites than in undisturbed soil samples. Therefore, it can be concluded that isoproturon sorption is overestimated when using the EEW method, which should be taken into account when using data from this approach or similar batch techniques for risk assessment analysis. [Copyright &y& Elsevier]

Aim of this work was the development of a new non-biological factor to determine microbial in situ bioavailability of chemicals in soils. Pesticide residues were extracted from ten highly different agricultural soils that had been incubated with the 14C-herbicide isoproturon (IPU) under comparable soil conditions (water tension – 15kPa; soil density 1.3gcm−3). Two different pesticide extraction approaches were compared: (i) 14C-pesticide residues were measured in the pore water (PW) which was extracted from soil by centrifugation; (ii) 14C-pesticide residues were extracted from soil samples with an excess of water (EEW). We introduce the pesticide’s in situ mass distribution quotient (iMDQ) as a measure for pesticide bioavailability, which is calculated as a quotient of adsorbed and dissolved chemical amounts for both approaches (iMDQPW, iMDQEEW). Pesticide mineralization in soils served as a reference for real microbial availability. A highly significant correlation between iMDQPW and mineralization showed that PW extraction is adequate to assess IPU bioavailability. In contrast, no correlation exists between IPU mineralization and its extractability from soil with EEW. Therefore, it can be concluded that soil equilibration at comparable conditions and subsequent PW extraction is vital for a isoproturon bioavailability ranking of soils. [Copyright &y& Elsevier]

Rice-wheat rotation system (RWS) is the most important food production system in South Asia and contributes approximately 50% of the total food consumed in India. Building climate resilience of RWS in the context of global warming is critical to sustainably feeding the ever-increasing population in India. Prior research has shown a large potential for climate change adaptation in rice and wheat production through cropping calendar adjustment, but the climate resilience of rice-wheat systems remains elusive. The study aims to project coupled changes in the growing seasons of rice and wheat in RWS under global warming, and evaluate climate resilience of RWS productivity in India through crop calendar adjustment. We combined an improved Environmental Policy Integrated Climate model with satellite remote sensing data to investigate the coupling of rice and wheat growing seasons in RWS in India. Then, we shifted crop calendars in RWS to simulate optimal planting dates and potential yield changes of rice and wheat under contrasting global warming scenarios. We show that farmers will face a yield trade-off between monsoon rice and dry-season wheat under high greenhouse gas emission scenarios (4 °C warming by the end of the century) in the Indo-Gangetic Plains (IGP) due to a tighter coupling of crop growing seasons in RWS. Early planting for both monsoon rice and dry-season wheat is recommended in the current climate, but the strategy does not apply to future climate change scenarios in the IGP. Limiting global warming below 2 °C by the end of the century is likely to improve RWS productivity by 14% in irrigated systems and 21% in rainfed systems, and keep sufficient maneuvering space for resilient planting strategies in the future. Our analysis emphasizes that failure to fulfill the pledges of the Paris Agreement will result in not only higher crop damage but also more challenging climate adaptation planning in RWS in India. [Display omitted] • A tighter growing season coupling between kharif rice and rabi wheat in RWS emerges with global warming in the IGP. • The early planting strategy for kharif rice and rabi wheat in the IGP does not apply to future climate change scenarios. • Farmers in the IGP are likely to face a yield trade-off between rice and wheat in high greenhouse gas emission scenarios. • Yield potential gains by system-level calendar management in RWS are higher under RCP2.6 compared to the RCP8.5 scenario. • Limiting global warming below 2 °C can provide more flexible timing for rice and wheat planting in RWS. [ABSTRACT FROM AUTHOR]

• Quantification of water use for rainfed production of Brazilian crops, and simulation of management scenarios. • Green water is a primary resource for production of the crops, both in the present and in prospective scenarios. • We found possibilities of improvement of green water productivity, and limited options for supplemental blue water use. • Irrigation-based intensification results in lower water productivity, when compared to fertilizer-based intensification. Green water is a central resource for global agricultural production. Understanding its role is fundamental to design strategies to increase global food and feed production while avoiding further land conversion, and obtaining more crop per drop. Brazil is a country with high water availability, and a major exporter of agricultural goods and virtual water. We assess here water use and water productivity in Brazil for four major rainfed crops: cotton, maize, soybeans, and wheat. For this, we use the EPIC crop model to perform a spatially explicit assessment of consumptive water use and water productivity under crop management scenarios in Brazil between 1990 and 2013. We investigate four different land-water interactions: (i) water use and productivity for different management scenarios, (ii) the potential of supplemental irrigation for productivity improvement, (iii) changes in green water use throughout the study period, and finally (iv) potential reduction of land and water demand related to agricultural intensification. The results show that, for the studied crops, green water is the main resource for biomass production, and intensification can lead to great improvements in green water productivity. The results also suggest that, despite achieving higher yields, irrigation-based intensification tends to lower overall water productivity, compared to fertilizer-based intensification strategies. This is, however, regionally and crop-specific. Furthermore, due to higher yields and water productivity, producing the same amount of crop output in irrigated or rainfed intensification scenarios would result in the reduction of resource demand, in the order of 34–58 % for cropland, and 29–52 % for water. [ABSTRACT FROM AUTHOR]

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28). For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05 (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises. [ABSTRACT FROM AUTHOR]

Water erosion removes soil nutrients, soil carbon, and in extreme cases can remove topsoil altogether. Previous studies have quantified crop yield losses from water erosion using a range of methods, applied mostly to single plots or fields, and cannot be systematically compared. This study assesses the worldwide impact of water erosion on maize and wheat production using a global gridded modeling approach for the first time. The EPIC crop model is used to simulate the global impact of water erosion on maize and wheat yields, from 1980 to 2010, for a range of field management strategies. Maize and wheat yields were reduced by a median of 3% annually in grid cells affected by water erosion, which represent approximately half of global maize and wheat cultivation areas. Water erosion reduces the annual global production of maize and wheat by 8.9 million tonnes and 5.6 million tonnes, with a value of $3.3bn globally. Nitrogen fertilizer necessary to reduce losses is valued at $0.9bn. As cropland most affected by water erosion is outside major maize and wheat production regions, the production losses account for less than 1% of the annual global production by volume. Countries with heavy rainfall, hilly agricultural regions and low fertilizer use are most vulnerable to water erosion. These characteristics are most common in South and Southeast Asia, sub-Saharan Africa and South and Central America. Notable uncertainties remain around large-scale water erosion estimates that will need to be addressed by better integration of models and observations. Yet, an integrated bio-physical modeling framework – considering plant growth, soil processes and input requirements – as presented herein can provide a link between robust water erosion estimates, economics and policy-making so far lacking in global agricultural assessments. • Water erosion reduces maize and wheat yields by 3% on half of the global cropland. • Simulated erosion impacts are mostly driven by slope, precipitation and fertilizer rates. • Highest water erosion impacts are in Asia, sub-Saharan Africa and Latin America. • Maize and wheat losses due to water erosion account for less than 1% of the global production. [ABSTRACT FROM AUTHOR]