Simplr: Crop Systems Evaluation Platform

Simulated maize yield level changes after 35 years of continuous extractive cultivations

Farming entails a great deal of choices and uncertainties. From season to season, weather varies, price fluctuates, soil degrades, pest damages, and climate changes. Farmers everywhere must cope with these uncertainties. Throughout the history of agriculture, many options have been developed to help manage these risks, increase yields, increase efficiency, and, more recently, promote the sustainability of the overall system.

With these techniques and tools in mind, each farmer must assess their local context and analyze the costs and benefits of adopting them, such as the additional labor and/or investment required. National and international donors and policymakers share the farmers’ goal of improving food security as cost-effectively as possible. They, like farmers, must evaluate the feasibility and profitability of available strategies and policy options and decide which ones to promote and where. If reliable estimates could be made of the current and potential patterns of crop productivity under different scenarios, many agricultural development investment and policy decisions would be significantly improved or made with greater confidence.

Simplr, the crop systems simulation platform

Crop systems models mathematically describe the growth and yield of crop and its interaction with soils, climate, and management practices. As a decision-support tool, crop systems models can be particularly useful when combined with a sound understanding of the farming systems, their location-specific traits, and quality data.

Most modern crop models can quantify on a daily basis a crop's various biological processes (e.g., the amount of solar energy transformed into biomass, water and nutrient requirements, supply, stresses, and growth stages), as well as physical processes related to crops (e.g., water runoff, soil carbon sequestration, and nitrogen leaching). Since the early 1970s, agricultural scientists developed various crop models based on improved knowledge of plant photosynthesis and respiration processes. Models range from the generic and simple to the very specific and complex. Some models use response functions at their core (e.g., yield is a function of rainfall and nutrients), while others use  intricate sets of differential equations to describe the complexity of different processes and their interactions. There is no final and universal crop model—rather, crop models are selected based on the type of research question. The performance of different crop models is openly evaluated in different conditions.

Conceptual schematic of the SChEF, the HarvestChoice Spatial Characterization and Evaluation

Modeling the gridded world

Conventionally, agronomic researchers often use crop systems models to better understand the current status of farming systems at small/micro-scales and systematically test possible scenarios and estimate efficient uses of given resources before (or instead of) conducting real experiments. For example, modeling soil water and nutrient status can help make the irrigation and fertilizer application on crops more efficient.

At the meso-scale (i.e., pixelated view of the world on 10-km grids), given our best understanding of the current status of the smallholders' farming systems in Sub-Saharan Africa (SSA), we use crop systems models to understand and assess the biophysical impacts of multiple abiotic/biotic constraints and evaluate the potential benefits of adopting a range of potential intervention scenarios. Ultimately, HarvestChoice aims to better inform economic and policy analysis of the overall agriculture sector in the region through its analytical research platform, within which crop systems models play a role as the biophysical estimator of the cropping systems' productivity responses under scenarios of change, including a broad range of technology, crop management assumptions, and historic/contemporary/projected climate conditions. The overarching platform, collectively branded as Simplr (simulation platform), is a component of the overall HarvestChoice Spatial Characterization and Evaluation Framework (SChEF). The productivity assessments are made at a detailed spatial resolution across the entire SSA region.

Cumulative probability distribution of simulated maize yield under the different variety and fertilizer application practices for 30 years in major maize growing areas in Ethiopia

What is the spatial unit of simulation in the regional context?

Strategic questions often cover large areas where significant heterogeneity in crop growth conditions is found. However, crop systems models are developed for use in small unit areas where crop growth, environmental conditions, and management practices are considered homogeneous. To overcome this geographic scale limitation, Simplr implements a meso-scale analysis platform that assumes a uniformly generated grid cell as a unit area, which is currently designed as a 10x10 km grid cell (There are about 300,000 grid cells in SSA at this resolution). For each cell, a set of data required to run crop models has been compiled, including soil properties, weather/climate, and typical management practices. As different types of research questions require different levels of input data, various spatial and temporal resolutions of model input and evaluation data from multiple sources are being compiled. Input datasets introduce implicit spatial correlations across landscapes, but biophysical processes in each grid cell are independently simulated from neighboring cells. As necessary, the simulation results can be aggregated and reported to the 1st or 2nd level of administrative units in each country.

Estimated value:cost ratio of applying urea on rainfed maize fields in five East African countries

What are the productivity changes that Simplr can assess?

The most widely-used measure of cropping system productivity is crop yield; thus a primary use of Simplr is to assess crop yield variation in both space and time under different scenarios of changes in the cropping systems components. Additionally, Simplr also provides other measures of the productivity and performance of cropping systems. At the core of Simplr is a crop systems model built around the best scientific understanding of the biological, physical, and chemical processes supporting crop growth and its dynamic interaction with environment. Drawing on these hard-wired process models, Simplr generates seasonal estimates of crop growth and yield components (above and below ground biomass, yield, and residue) and tracks stocks and flows of nutrient (carbon, nitrogen, phosphorus) and water in a crop and soil profile. By comparing estimates of these indicators across different intervention scenarios, Simplr supports in-depth analysis of relevance to a range of topical agricultural development issues. Recently focused issues include crop improvement, integrated soil fertility management, small scale water management and irrigation, conservation agriculture, enhanced carbon sequestration, payment for ecosystem services, and farm scale adaptation to climate change.

What data and results has Simplr already generated?

The development of the Simplr has involved progress on several fronts:

• Climate data: The creation of a consistent historic, contemporary, and projected set of six key climate variables of sufficient spatial (10 km) and temporal (daily) resolution for landscape scale analysis of crop growth.
• Soils data: Assembly of a range of options for representing the heterogeneity of soil conditions on both a point and area basis.
• Contemporary production systems: Creation and collection of survey and expert-based evidence on key attributes of crop production systems in SSA; land holdings, cropping patterns, planting dates, input use, and market orientation.

Using evolving versions of these databases, several systematic assessments have been undertaken,  including: maize yield response to changes in planting date, varietal maturity periods and genetic potential, nitrogen application, and supplementary irrigation, potential changes in the spatial and multi-year patterns of yields and soil fertility status for maize, sorghum and cotton production systems in Mali under a range of improved soil and water management technologies, variability cereal yields based on simulation of long term (more than 40 years) of historic climate data, and projections of SSA cereal yields under a range of climate change scenarios. These results are accessible through the HarvestChoice website. On-going applications of Simplr include an evaluation of conservation agriculture technologies, an assessment of the economic cost of various biotic constraints on food staples, and a more extensive spatial comparison of the maize varietal performance.

What crop systems simulation tools are employed?

To date, HarvestChoice has employed the DSSAT (Version 4.5) and APSIM (Version 7.1) suite of models, but has also applied the ORYZA2000 rice model (lead by IRRI) and is engaged in harmonizing input datasets across different crop modeling platforms. HarvestChoice sees the further development of crop model platforms to support strategic investment and policy analysis as a core area of its on-going work. Partners in that process include the Universities of Georgia and Florida, IFDC, and CSIRO as well as CGIAR centers. Two key areas of focus will be tighter coupling of pest and disease and crop models and the assessing the potential impacts of improved technologies and practices on profitability and resource sustainability over time.

How will HarvestChoice provide outreach for its cropping system modeling capacity?

There are currently three main strategies by which we are sharing Simplr technologies and striving to enhance user capacity to leverage these outputs. First, we are providing access to input Simplr datasets and analysis outputs via the HarvestChoice website.  Second is we are providing access to a purpose built crop modeling tool Harvest Toolkit in partnership with IFDC and ICASA and, of increasing emphasis, through the development and delivery of training materials and courses with specialized partners. A particular goal is to promote the adoption and application of cropping system modeling tools by African scientists and analysts involved in relevant decision support processes in the region.