The impact of water erosion on global maize and wheat productivity

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

The effects of cropping intensity and cropland expansion of Brazilian soybean production on green water flows

As land use change alters how green water is appropriated, cropland expansion is instrumental in re-allocating green water towards agriculture. Alongside cropland expansion, agricultural intensification practices modify crop water use and land and water productivity. Particularly, one form of agricultural intensification known as multi-cropping (the cultivation of a piece of land sequentially more than once a year) can result in greater agricultural output per unit of land, as well as more productive use of the available water throughout the annual rainfall cycle. We assess the influence of these two processes, cropland expansion and agricultural intensification, in agricultural green water use in Brazilian agriculture. We applied the biophysical crop model Environmental Policy Integrated Climate (EPIC) to estimate green water use for single and double cropping of soybean (Glycine max) and maize (Zea mays) in Brazil. The first part of our study analyses changes in soybean green water use and virtual water content nationwide between 1990 and 2013, and in a second part we look into the effect of double-cropping on water use for soybean and maize in the Brazilian states of Paraná and Mato Grosso between 2003 and 2013. The results show that cropland expansion plays a more prominent effect in green water use for production of soybean than intensification, and harvested area increase was responsible for the appropriation of an additional 95 km3 of green water in 2013 when compared to 1990, an increase of 155%. We estimate that an additional green water use of around 26 km3 related to second season maize was appropriated through increase of cropping frequency, and without expansion of cropland, in 2013 in the selected states. We discuss the importance of considering multi cropping practices when assessing green water sustainability, and the importance of differentiating green water appropriation through expansion and through cropping frequency changes

Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global gridded crop model

Water erosion on arable land can reduce soil fertility and agricultural productivity. Despite the impact of water erosion on crops, it is typically neglected in global crop yield projections. Furthermore, previous efforts to quantify global water erosion have paid little attention to the effects of field management on the magnitude of water erosion. In this study, we analyse the robustness of simulated water erosion estimates in maize and wheat fields between the years 1980 and 2010 based on daily model outputs from a global gridded version of the Environmental Policy Integrated Climate (EPIC) crop model. By using the MUSS water erosion equation and country-specific and environmental indicators determining different intensities in tillage, residue handling and cover crops, we obtained the global median water erosion rates of 7 t ha−1 a−1 in maize fields and 5 t ha−1 a−1 in wheat fields. A comparison of our simulation results with field data demonstrates an overlap of simulated and measured water erosion values for the majority of global cropland. Slope inclination and daily precipitation are key factors in determining the agreement between simulated and measured erosion values and are the most critical input parameters controlling all water erosion equations included in EPIC. The many differences between field management methods worldwide, the varying water erosion estimates from different equations and the complex distribution of cropland in mountainous regions add uncertainty to the simulation results. To reduce the uncertainties in global water erosion estimates, it is necessary to gather more data on global farming techniques to reduce the uncertainty in global land-use maps and to collect more data on soil erosion rates representing the diversity of environmental conditions where crops are grown

Uncertainties, sensitivities and robustness of simulated water erosion in an EPIC-based global gridded crop model

Water erosion on arable land can reduce soil fertility and agricultural productivity. Despite the impact of water erosion on crops, it is typically neglected in global crop yield projections. Furthermore, previous efforts to quantify global water erosion have paid little attention to the effects of field management on the magnitude of water erosion. In this study, we analyse the robustness of simulated water erosion estimates in maize and wheat fields between the years 1980 and 2010 based on daily model outputs from a global gridded version of the Environmental Policy Integrated Climate (EPIC) crop model. By using the MUSS water erosion equation and country-specific and environmental indicators determining different intensities in tillage, residue handling and cover crops, we obtained the global median water erosion rates of 7 t ha-1 a-1 in maize fields and 5 t ha-1 a-1 in wheat fields. A comparison of our simulation results with field data demonstrates an overlap of simulated and measured water erosion values for the majority of global cropland. Slope inclination and daily precipitation are key factors in determining the agreement between simulated and measured erosion values and are the most critical input parameters controlling all water erosion equations included in EPIC. The many differences between field management methods worldwide, the varying water erosion estimates from different equations and the complex distribution of cropland in mountainous regions add uncertainty to the simulation results. To reduce the uncertainties in global water erosion estimates, it is necessary to gather more data on global farming techniques to reduce the uncertainty in global land-use maps and to collect more data on soil erosion rates representing the diversity of environmental conditions where crops are grown

Water productivity and footprint of major Brazilian rainfed crops – A spatially explicit analysis of crop management scenarios

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

Uncertainty in soil data can outweigh climate impact signals in crop yield simulations

Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations

Climate analogues suggest limited potential for intensification of production on current croplands under climate change

Climate change could pose a major challenge to efforts towards strongly increase food production over the coming decades. However, model simulations of future climate-impacts on crop yields differ substantially in the magnitude and even direction of the projected change. Combining observations of current maximum-attainable yield with climate analogues, we provide a complementary method of assessing the effect of climate change on crop yields. Strong reductions in attainable yields of major cereal crops are found across a large fraction of current cropland by 2050. These areas are vulnerable to climate change and have greatly reduced opportunity for agricultural intensification. However, the total land area, including regions not currently used for crops, climatically suitable for high attainable yields of maize, wheat and rice is similar by 2050 to the present-day. Large shifts in land-use patterns and crop choice will likely be necessary to sustain production growth rates and keep pace with demand

Impacts and Uncertainties of +2°C of Climate Change and Soil Degradation on European Crop Calorie Supply

Even if global warming is kept below +2°C, European agriculture will be significantly impacted. Soil degradation may amplify these impacts substantially and thus hamper crop production further. We quantify biophysical consequences and bracket uncertainty of +2°C warming on calories supply from ten major crops and vulnerability to soil degradation in Europe using crop modelling. The Environmental Policy Integrated Climate (EPIC) model together with regional climate projections from the European branch of the Coordinated Regional Downscaling Experiment (EURO-CORDEX) were used for this purpose. A robustly positive calorie yield change was estimated for the EU Member States except for some regions in Southern and South-Eastern Europe. The mean impacts range from +30 Gcal ha–1 in the north, through +25 and +20 Gcal ha–1 in Western and Eastern Europe, respectively, to +10 Gcal ha–1 in the south if soil degradation and heat impacts are not accounted for. Elevated CO2 and increased temperature are the dominant drivers of the simulated yield changes in high-input agricultural systems. The growth stimulus due to elevated CO2 may offset potentially negative yield impacts of temperature increase by +2°C in most of Europe. Soil degradation causes a calorie vulnerability ranging from 0 to 80 Gcal ha–1 due to insufficient compensation for nutrient depletion and this might undermine climate benefits in many regions, if not prevented by adaptation measures, especially in Eastern and North-Eastern Europe. Uncertainties due to future potentials for crop intensification are about two to fifty times higher than climate change impacts

Co-evaluating and -designing a Sustainable Agriculture Matrix for Austria in an international context

Agricultural ecosystems provide essential services mainly through food, feed, fiber and consequently income but they also contribute cultural, supporting and regulating services. In turn, farming can adversely affect ecosystem services, especially those from natural ecosystems, if farming practices are unsustainable. Recently, a Sustainable Agriculture Matrix (SAM; https://doi.org/10.1016/j.oneear.2021.08.015) of indicators across environmental, economic, and social dimensions has been developed by an international research team to coherently quantify the sustainability of countries’ farming systems globally. The focus was on indicators that can be tracked over time and relate to performance to facilitate analyzes of synergies and trade-offs. At present, this indicator system is being co-evaluated with stakeholders in ten countries within an international consortium including Austria, to elicit stakeholders’ appraisal of the framework’s applicability in their specific geographical and socioeconomic context and eventually co-design a revised matrix based on stakeholders’ requirements. A first workshop has shown that most indicators from the environmental dimension are useful for stakeholders in the Austrian context, but some need further refinements. Biodiversity, for example, is only considered via land cover change whereas threats to (agro-)biodiversity in Austria and the EU foremost occur in-situ. The economic dimension is ranking second in its usefulness for Austrian stakeholders with few indicators such as food loss being of little relevance. The indicators presently included in the social dimension are least relevant as they cover aspects such as land rights, undernourishment, and rural poverty, which do not pose major issues in Austria and more broadly the EU. General concerns of stakeholders are the directionality of indicator ratings and their scope which is in part considered too narrow. E.g., high government expenditure for agriculture is considered positive in the matrix regardless of its purpose and may cause dependencies. Human nutrition is only included via undernourishment and soil nutrient status solely as surplus, whereas in both cases also the other extreme may be adverse. Accordingly, a bell-shaped indicator and rating would be favored in such cases. A general requirement was expressed for an additional context dimension. Governance arrangements and the overall socioeconomic situation are so far deliberately not included due to the focus on performance in the existing SAM. Yet, indicators describing such framework conditions can be essential to interpret synergies and trade-offs and the effectiveness of policy measures aiming at achieving SDGs. Beyond the evaluation of existing indicators, the stakeholder process yielded comprehensive suggestions for additional indicators, covering biodiversity, research and education, self-sufficiency, as well as various aspects of resilience and stability. Overall, the co-evaluation with stakeholders highlights that only few globally defined indicators are readily applicable in a regional context where consideration of local conditions and specifics is vital. The proposed revisions are now being matched with available data across geographic scales to revise the matrix and perform further analyses on trade-offs and synergies. This will also include further context information to facilitate the evaluation of policies, ultimately allowing for improved policy-making to attain agricultural sustainability. Results will be further co-evaluated iteratively with stakeholders to eventually produce a globally applicable indicator system