Global estimation of crop productivity and the impacts of global warming by GIS and EPIC integration

Abstract

In this paper, a new methodology for global estimation of crop productivity is proposed. This methodology integrates Erosion Productivity Impact Calculator (EPIC) model with Geographic Information System (GIS) and Inference Engine (IE) technique. EPIC was developed by USDA to analyze the relationship between soil erosion and agricultural productivity just at field level. With the integration of GIS, EPIC can be extended to the application of global or regional level. In this integration, IE is developed to determine possible crop combinations, the optimum starting and ending dates of growth cycle for each crop type and grid cell, in order to ensure best possible crop yields for both rain-fed and irrigated conditions. A case of global crop productivity estimation is tested with GIS-based EPIC in 2000. National averages are computed to be comparable to yields in FAO statistics. The comparison indicates that the GIS-based EPIC is able to simulate crop productivity at global level. In addition, with the global climate change data provided by the Intergovernment Panel on Climate Change (IPCC) from the first version of the Canadian Global Coupled Model (CGCM1), GIS-based EPIC is run for scenarios of future climate in the year of 2010, 2020, 2030, 2040, and 2050 to predict the effects of global warming on main crop yields. Results show the global warming will be harmful for most of the countries, and an efficient adaptation to alternative climates tends to reduce the damages.

Introduction

A large number of research groups have focused on estimation of all damages of global warming (Adams et al., 1993, Easterling et al., 1992, Dixon et al., 1994). From their exhaustive literature analysis, globally about one-fifth of all damages will occur in agriculture (Pearce et al., 1996). Together with damages from sea level rise, increasing mortality and increasing energy demand, food production is ranking at the top of the vulnerable sectors. However, all these researches are often very rough, because highly aggregated studies suffer from the fact that sub-national or sub-regional differences are not sufficiently taken into account. For example, international productivity differences, socioeconomic environments, or the climate microstructures within a country are neglected. Consequently, as the Intergovernment Panel on Climate Change (IPCC1) states in its report, future work regarding the impacts of climate change on agriculture should be focused on regional models, which carefully paying attention to local features (Reilly, 1996). That means that a useful model to estimate different crop productivities at global level is the key to predict the impact of climate on farming.

In the past two decades, a wide variety of approaches have been developed to estimate land productivity at global level. We can classify these approaches into three categories. One is used to assess land productivity potentials, such as FAO method (FAO, 1978–1980), which determines the ecological potential of land resources for crop production using the agroecological zone approach, and estimates land suitability with soil moisture conditions and other climate characteristics. However, current FAO global models have many problems. For example, the key measures of productivity have not yet blended soils and climate together; the effect of each is computed independently. Nor is it clear how rightly climate zones can predict which crops should be grown or what their yields will be potentially. The second approach to estimate global land productivity is the regression analysis method, which explicitly links climatic conditions to land and water resources and to production, trade, and consumption throughout the world (Kjaiswal and Saha, 1993, Moen et al., 1994, Franke et al., 1990, Trouslard-Kerdiles and Grondona, 1997). The key disadvantage of this method is the poor accuracy. The third approach is based on a field-based crop model which runs at individual fields to obtain regional or national results on crop yields (Saarikko, 2000, Priya and Shibasaki, 2001). Field-based models often have a good accuracy to estimate crop yields at field level. But due to the uncertainties associated with the yield estimates in a global assessment, such as uncertainties in climate change, model errors, cropping systems, and management practices, there is no published approach regarding the application of field-based model at global level.

There have been lots of field-based crop modeling studies around agroecosystems, such as DSSAT (Tsuji et al., 1994), YIELD (Burt et al., 1981), CENTURY (Parton et al., 1988) and DNDC (Li et al., 1992). DSSAT simulates crop growth with impacts of climate and soil conditions. The limitation with DSSAT is that it does not provide one model to simulate crops instead it has models for all specific crops. YIELD is able to simulate seasonal crop yield, crop water use, and length of growing season for 11 crops with relatively simple functions (Mahmood, 1998). CENTURY and DNDC are focused on element and material cycles. They pay more attention to soil processes, such as decomposition, nitrification, and denitrification instead of crop growth (Zhang et al., 2002). Erosion Productivity Impact Calculator (EPIC) is another very popularly used model to simulate crop yields at field level. It was developed by USDA to analyze the relationship between soil erosion and agricultural productivity. The model integrates the major processes that occur in the soil–crop–atmosphere–management system, including: hydrology, weather, erosion, nutrients, plant growth, soil temperature, tillage, plant environmental control, and economics (Williams et al., 1990, Williams, 1995). There are many test investigations about EPIC, performed using different data and parameters (Roloff et al., 1998, Bryant et al., 1992, Edwards et al., 1994). It seems that EPIC is well suited for relative comparisons of soils, crops, and management scenarios and has a good accuracy to estimate field yields (Bouzaher et al., 1993).

In this paper, a new global food production estimation methodology is proposed to estimate the impact of global warming on crop productivity. This methodology integrates Geographic Information System (GIS) with the EPIC and Inference Engine (IE). GIS have emerged as powerful tools in the management and analysis of large amount of basic data and information. They are being applied increasingly to a variety of situations, including the simulation of crop productivity and the evaluation of land resources (FAO/IIASA, 1991). This paper is organized into five sections. After a brief background introduction in Section 1, the methodology of integrating GIS with EPIC is discussed in Section 2; then the material and data required in our research are introduced in Section 3; a test of global land productivity for main crops and result analysis will be given in Section 4, followed by the conclusions in Section 5.