GIS-based EPIC Research Articles

Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields

Soybean is one of the most important agricultural commodities in the world, thus making it important for global food security. However, widely used process-based crop models, such as the GIS-based Environmental Policy Integrated Climate (GEPIC) model, tend to underestimate the impacts of extreme climate events on soybean, which brings large uncertainties. This study proposed an approach of hybrid models to constrain such uncertainties by coupling the GEPIC model and extreme climate indicators using machine learning. Subsequently, the key extreme climate indicators for the globe and main soybean producing countries are explored, and future soybean yield changes and variability are analyzed using the proposed hybrid model. The results show the coupled GEPIC and Random Forest (GEPIC+RF) model (R: 0.812, RMSD: 0.716 t/ha and rRMSD: 36.62%) significantly eliminated uncertainties and underestimation of climate extremes from the GEPIC model (R: 0.138, RMSD: 1.401 t/ha and rRMSD: 71.57%) compared to the other five hybrid models (R: 0.365–0.612, RMSD: 0.928–1.021 and rRMSD: 47.48–52.24%) during the historical period. For global soybean yield and those in Brazil and Argentina, low-temperature-related indices are the main restriction factors, whereas drought is the constraining factor in the USA and China, and combined drought–heat disaster in India. The GEPIC model would overestimate soybean yields by 13.40–27.23%. The GEPIC+RF model reduced uncertainty by 28.45–41.83% for the period of 2040–2099. Our results imply that extreme climate events will possibly cause more losses in soybean in the future than we have expected, which would help policymakers prepare for future agriculture risk and food security under climate change.

Rising agricultural water scarcity in China is driven by expansion of irrigated cropland in water scarce regions

Rising agricultural water scarcity in China is driven by expansion of irrigated cropland in water scarce regions

Scale dependence and parameter sensitivity of the EPIC model in the agro-pastoral transitional zone of north China

Under the background of rapid population growth, achieving accurate assessments of crop yields is critical to national policy adjustment and food security. Among all the evaluation methods for crop yields, the Environmental Policy Integrated Climate (EPIC) model is considered to be an effective tool. Since published, it has been widely applied on local, regional, and global scales. However, in its practical applications, the impact of scale variation on the simulation results has not received full consideration. The scale variation can introduce many uncertain effects in the simulation results. Furthermore, as a basis for parameter calibration, parameters sensitivity analyses were always been ignored at large scale studies. In this study, we used the GIS-based EPIC (GEPIC) model to assess the simulation accuracies at different scales and local sensitivity analysis was adopted to analyze the sensitivity of the crop parameters in each grid first time. The results showed that EPIC model has scale-dependence and the smaller the scale is, the more accurate the results are. The most sensitive parameter of maize and soybeans is the energy conversion to biomass factor (WA) in the agro-pastoral transitional zone of north China, whereas the harvest index (HI) is the most sensitive parameter for the three other crops of rice, spring wheat, and winter wheat. For the same crop, parameter sensitivity is heterogeneous in space due to varied input variables. In addition, we find that parameters sensitivities (Si) vary as the parameters and the variations in Si for the five parameters are different. Excluding the Si of HI for spring wheat which has the greatest change and the variation is nearly 0.5, the Si of other parameters change slightly. In the application of GEPIC model, we should consider the effects of scale variation on crop yields, particular for areas with high heterogeneity of cropland patches. In addition, this study provides a reference for setting the input parameters of the EPIC model.

Impact of Deforestation on Agro-Environmental Variables in Cropland, North Korea

Deforestation in North Korea is becoming the epitome of the environmental change occurring in the Korean Peninsula. This study estimates the agro-environmental variables of North Korea’s croplands and analyzes the impact of deforestation using the GEPIC (GIS-based EPIC (Environmental Policy Integrated Climate)) model and time-series land cover maps. To identify the changes in agricultural quality under deforestation, wind erosion, water erosion, organic carbon loss, and runoff were selected as the agro-environmental variables having an impact on cropland stability and productivity. Land cover maps spanning the past three decades showed that 75% of the forests were converted to croplands and that 69% of all converted croplands were originally forests, confirming the significant correlation between deforestation and cropland expansion in North Korea. Despite limitations in the verification data, we conducted qualitative and quantitative validation of the estimated variables and confirmed that our results were reasonable. Over the past 30 years, agro-environmental variables showed no clear time-series changes resulting from climate change, but changes due to spatial differences were seen. Negative changes in organic carbon loss, water erosion, and runoff were observed, regardless of the crop type. On newly-converted agricultural lands, runoff is 1.5 times higher and water-driven erosion and soil organic loss are more than twice as high compared to older croplands. The results showed that the agro-environment affected by deforestation had an impact on cropland stability and productivity.

A spatial explicit assessment of food security in Africa based on simulated crop production and distribution

The continent of Africa has the highest prevalence of hunger and poverty in the world. In this paper, an Environmental Policy Integrated Climate (EPIC) model combined with a Crop Choice Decision Model (CCDM) is applied to simulate the production and spatial distribution of rice, wheat, millet, maize, sorghum and cassava in Africa from 1993 to 2012 converted into calories. From this, we calculate the size of the undernourished population according to the Average Dietary Energy Requirement index of the Food and Agriculture Organization of the United Nations (FAO) and study the trends experienced in different countries. The results show that (1) the distribution of different crops has a horizontal zonality from north to south; (2) although the distribution of different crops in Africa did not change significantly from 1993 to 2012, the total crop planting area declined, especially in the middle part of Africa; (3) the undernourished population has increased, while the proportion of undernourished people decreased; and (4) land tenure reform and international food aid has made a great contribution to improved food security in Africa. A GIS-based EPIC (GEPIC) model combined with a CCDM can enable the spatial explicit assessment of food security and the microscopic study of food security on large scale, providing more accurate decision-making information for policy makers.

Effects of ecological and conventional agricultural intensification practices on maize yields in sub-Saharan Africa under potential climate change

Much of Africa is among the world’s regions with lowest yields in staple food crops, and climate change is expected to make it more difficult to catch up in crop production in particular in the long run. Various agronomic measures have been proposed for lifting agricultural production in Africa and to adapt it to climate change. Here, we present a projection of potential climate change impacts on maize yields under different intensification options in Sub-Saharan Africa (SSA) using an agronomic model, GIS-based EPIC (GEPIC). Fallow and nutrient management options taken into account are (a) conventional intensification with high mineral N supply and a bare fallow, (b) moderate mineral N supply and cowpea rotation, and (c) moderate mineral N supply and rotation with a fast growing N fixing tree Sesbania sesban. The simulations suggest that until the 2040s rotation with Sesbania will lead to an increase in yields due to increasing N supply besides improving water infiltration and soils’ water holding capacity. Intensive cultivation with a bare fallow or an herbaceous crop like cowpea in the rotation is predicted to result in lower yields and increased soil erosion during the same time span. However, yields are projected to decrease in all management scenarios towards the end of the century, should temperature increase beyond critical thresholds. The results suggest that the effect of eco-intensification as a sole means of adapting agriculture to climate change is limited in Sub-Saharan Africa. Highly adverse temperatures would rather have to be faced by improved heat tolerant cultivars, while strongly adverse decreases in precipitation would have to be faced by expanding irrigation where feasible. While the evaluation of changes in agro-environmental variables like soil organic carbon, erosion, and soil humidity hints that these are major factors influencing climate change resilience of the field crop, no direct relationship between these factors, crop yields, and changes in climate variables could be identified. This will need further detailed studies at the field and regional scale.

Impacts of climate change on virtual water content of crops in China

Looming water scarcity and climate change pose big challenges for China's food security. Previous studies have focus on the impacts of climate change either on agriculture or on water resources. Few studies have linked water and agriculture together in the context of climate change, and demonstrated how climate change will affect the amount of water used to produce per unit of crop, or virtual water content (VWC). We used a GIS-based Environmental Policy Integrated Climate (GEPIC) model to analyze the current spatial distribution of VWC of various crops in China and the impacts of climate change on VWC in different future scenarios. The results show that C4 crops (e.g. irrigated maize with a VWC of 0.73m3kg−1 in baseline) generally have a lower VWC than C3 crops (e.g. irrigated wheat with a VWC of 1.1m3kg−1 in baseline), and the VWC of C4 crops responds less sensitively to the CO2 concentration change in future climate scenarios. Three general change trends exist for future VWC of crops: continuous decline (for soybean and rice without considering CO2 concentration changes) and continuous increase (for rice with considering CO2 concentration changes) and first-decline-then-increase (other crop-scenario combinations). The trends reflect the responses of different crops to changes in precipitation, temperature as well as CO2 concentration. From south to north along the latitude, there is a high-low-high distribution trend of the aggregated VWC of the crops. Precipitation and temperature changes combined can lead to negative effects on crop yield and higher VWC particularly in the far future e.g. the 2090s, but when CO2 concentration change is taken into consideration, it is likely that crop yield will increase and crop VWC will decrease for the whole China. Integrated effects of precipitation, temperature and CO2 concentration changes will benefit agricultural productivity and crop water productivity through all the future periods till the end of the century. Hence, climate change is likely to benefit food security and help alleviate water scarcity in China.

Impact of input data resolution and extent of harvested areas on crop yield estimates in large-scale agricultural modeling for maize in the USA

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.

Global-scale assessment of potential future risks of food insecurity

This paper presents an approach of combining biophysical, social, and economic factors for spatially explicit assessment of potential future risks of food insecurity at a global scale over the period of 2000–2020 under a certain scenario. In doing that, two indicators, namely per capita food availability and per capita Gross Domestic Product (GDP), were selected to cover the four dimensions of food security, with the former representing the status of food availability and stability, and the latter reflecting the situation of food accessibility and affordability. These two indicators were then linked to an integrated modeling framework. Under this framework, a GIS-based EPIC model was adopted to estimate the potential yields of different crop types under a given biophysical and agricultural management environment, a crop choice decision model was used to model the changes in crop areas through tracking the crop choice decisions, and the IFPSIM model was utilized to evaluate the crop price in the international market. Based on these two indicators, the potential risks of food insecurity were assessed with a spatial resolution of six arc-minutes. The results show that both changes in per capita food availability and changes in per capita GDP during 2000–2020 vary across regions worldwide. Some regions such as China, most eastern European countries, and most southern American countries where there is an increase in per capita food availability or an increase in the capacity to import food between 2000 and 2020 might be able to improve their food security situation. On the contrary, certain regions such as southern Asia and most African countries will likely remain hotspots of food insecurity in the future. In these regions, both the per capita food availability and the capacity of being able to import food will decrease between 2000 and 2020. Although most developed countries will also experience both a decrease in per capita food availability and a decrease in per capita GDP, these countries are likely to be food-secure due to their higher income and purchasing power.

A spatially explicit assessment of current and future hotspots of hunger in Sub-Saharan Africa in the context of global change

Hunger knows no boundaries or borders. While much research has focused on undernutrition on a national scale, this report evaluates it at subnational levels for Sub-Saharan Africa (SSA) to pinpoint hotspots where the greatest challenges exist. Undernutrition is assessed with a spatial resolution of 30 arc-minutes by investigating anthropometric data on weight and length of individuals. The impact of climate change on production of six major crops (cassava, maize, wheat, sorghum, rice and millet) is analyzed with a GIS-based Environmental Policy Integrated Climate (GEPIC) model with the same spatial resolution. Future hotspots of hunger are projected in the context of the anticipated climate, social, economic, and bio-physical changes. The results show that some regions in northern and southwestern Nigeria, Sudan and Angola with a currently high number of people with undernutrition might be able to improve their food security situation mainly through increasing purchasing power. In the near future, regions located in Ethiopia, Uganda, Rwanda and Burundi, southwestern Niger, and Madagascar are likely to remain hotspots of food insecurity, while regions located in Tanzania, Mozambique and the Democratic Republic of Congo might face more serious undernutrition. It is likely that both the groups of regions will suffer from lower capacity of importing food as well as lower per capita calorie availability, while the latter group will probably have sharper reduction in per capita calorie availability. Special attention must be paid to the hotspot areas in order to meet the hunger alleviation goals in SSA.

An integrated model to simulate sown area changes for major crops at a global scale

Dynamics of land use systems have attracted much attention from scientists around the world due to their ecological and socio-economic implications. An integrated model to dynamically simulate future changes in sown areas of four major crops (rice, maize, wheat and soybean) on a global scale is presented. To do so, a crop choice model was developed on the basis of Multinomial Logit (Logit) model to model land users’ decisions on crop choices among a set of available alternatives with using a crop utility function. A GIS-based Environmental Policy Integrated Climate (EPIC) model was adopted to simulate the crop yields under a given geophysical environment and farming management conditions, while the International Food Policy and Agricultural Simulation (IFPSIM) model was utilized to estimate crop price in the international market. The crop choice model was linked with the GIS-based EPIC model and the IFPSIM model through data exchange. This integrated model was then validated against the FAO statistical data in 2001–2003 and the Moderate Resolution Imaging Spectroradiometer (MODIS) global land cover product in 2001. Both validation approaches indicated reliability of the model for addressing the dynamics in agricultural land use and its capability for long-term scenario analysis. Finally, the model application was designed to run over a time period of 30 a, taking the year 2000 as baseline. The model outcomes can help understand and explain the causes, locations and consequences of land use changes, and provide support for land use planning and policy making.

Assimilating remotely sensed LAI into GIS-based EPIC model for yield assessment on regional scale

Assimilating remotely sensed LAI into GIS-based EPIC model for yield assessment on regional scale

Modeling the role of irrigation in winter wheat yield, crop water productivity, and production in China

Irrigation plays an important role in increasing food production in China. The impact of irrigation on crop yield (Y), crop water productivity (CWP), and production has not been quantified systematically across regions covering the whole country. In this study, a GIS-based EPIC model (GEPIC) was applied to simulate Y and CWP for winter wheat (Triticum aestivum L.) in China at a grid resolution of 5 arc-minutes and to analyze the impacts of reducing irrigation water on wheat production. The findings show that irrigation is especially important in improving CWP of winter wheat in the North China Plain (NCP), the “bread basket” of China. On average, the provincial aggregate CWP was 56% higher under the irrigated than that under the rainfed conditions. The intensification of water stress and the associated increase in environmental problems in much of the NCP require critical thoughts about reducing water allocation for irrigated winter wheat. Two scenarios for irrigation reduction in the NCP provinces are presented: reducing irrigation depth (S1), and replacing irrigated winter wheat by rainfed winter wheat (S2). The simulation results show that S1 and S2 have similar effects on wheat production when the reduction in irrigation water supply is below 20% of the current level. Above this percentage, S2 appears to be a better scenario since it leads to less reduction in wheat production with the same amount of water saving.

GEPIC – modelling wheat yield and crop water productivity with high resolution on a global scale

With population growth and economic development, the agricultural sector is facing the challenge to produce more food with less water. Crop water productivity (CWP) is important for understanding water–food relationships. It also provides a basis for the assessment of water use efficiency embodied in global food trade. However, traditional methods are not sufficient for estimating CWP on a global scale considering large spatial and temporal variations across different geographical locations. In this paper, a GIS-based EPIC model (GEPIC) is developed and tested to estimate wheat ( Triticum aestivum L.) yield and CWP at a grid resolution of 30′ on the land surface. A comparison between simulated yields and FAO statistical yields in 102 countries over 10 years shows a good agreement. The simulated CWP is also mostly in line with the CWP reported in the literature. The simulation results show that compared with rainfed wheat, irrigated wheat has higher frequencies for high CWP (>0.8 kg m −3) and lower frequencies for low CWP (<0.8 kg m −3). This is likely because irrigation can provide timely water supply to crop development and the management of irrigated crops is usually more intensive than in rainfed production. A strong linear relation is found between CWP and yield. High wheat yield and CWP appear in the European countries, especially those in western and northern Europe. Low wheat yield and CWP are seen in most African countries. The simulation using GEPIC, however, shows that wheat yield and CWP in many African countries could increase substantially with sufficient water supply and fertilizer application. Variations in CWP across countries suggest that global water use could be reduced through food trade. Calculations indicate a saving of 77 × 10 9 m 3 of water in 2000 through international wheat trade as a result of relatively high CWP in major exporting countries. However, the simulation results also suggest that an overall improvement in CWP through better crop management practices in local areas could make a greater contribution to the reduction in global water use.