Average to mildly above average crop success anticipated for much of the major maize producing areas of Southern Africa, with pockets of moderately reduced crop success in subsistence farming and livestock grazing regions.
Will Turner and Greg Husak
The Water Requirement Satisfaction Index (WRSI) is a water balance model that incorporates crop parameters such as the length of growing season and phenological information, along with soil characteristics. This model is commonly used to monitor growing seasons throughout Africa and Central America by the Famine Early Warning Systems Network (FEWSNET). Specifically, the model uses gridded precipitation and reference evapotranspiration (RefET) inputs to calculate the ratio of plant-available water to crop-specific water demand at each stage of crop development. This post describes a real-time monitoring application to examine the progress of the Southern Africa 2017-2018 maize growing season, at approximately the halfway point of the season.
The analysis combines September, October, and November final CHIRPS rainfall estimate (reference link, data link), and December CHIRPS Preliminary data (data link) to identify the to-date 2017-18 Start of Season (Figure 1). The dekad identified as the Start of Season (SOS) for a location (pixel) is defined as the first dekad with at least 25 mm of rainfall followed by a total of at least 20 mm in the next two consecutive dekads. For the example shown here, which only has data through the end of December, dekads 2 and 3 of December only use the 25 mm threshold to identify a potential start. Whether or not the second threshold was met cannot be determined until January CHIRPS Prelim is released. All areas that have not met these requirements by the third dekad of December are labeled as ‘Yet to Start’ (see the areas of Figure 1 in light gray color).
Historical CHIRPS (1981-2016) was used to identify the SOS for each of the last 36 years, and calculate the median historical SOS (Figure 2).
To examine the potential outcomes of the current season, 36 WRSI scenarios were run using the result from Figure 1 as the SOS. To get credible simulations of the season, hybrid composites of both precipitation and RefET¹ were created using season-to-date information and previous years to estimate one potential remainder to the season. The precipitation input of each scenario used 2017 CHIRPS final through November and 2017 CHIRPS Prelim for December. The RefET input used 2017 data through September. Because RefET is not available for October-December of 2017 due to the latency of the dataset, these 9 dekads were filled with average RefET. The 36 season scenarios were then run to completion using corresponding year-specific CHIRPS and RefET (i.e. Scenario #1 used 2017 data for Sep-Dec, and 1981 data for Jan-May; Scenario #2 used 2017 data for Sep-Dec, and 1982 data for Jan-May; etc.).
To build a library of historical WRSI output, the WRSI calculation was run using the 36 years of historical SOS from Part 1 of the examination and historical CHIRPS and RefET.
Investigating the difference of the median historical SOS (Figure 2) from this season’s SOS (Figure 1) reveals the timeliness of the current season (Figure 5). The median of the 36 2017-18 WRSI scenarios (Figure 3) was then compared to the WRSI median of the historical runs (Figure 4) to find the simulated WRSI anomaly (Figure 6).
Comparing the graphics shown in Figure 5 and 6 we identify some interesting patterns, and can summarize how changes in SOS may impact this season’s crop production in Southern Africa.
- Major production zones in the Maize Triangle of South Africa, Northern Zimbabwe, Central and Northern Mozambique and Malawi experienced slightly early to on-time SOS (Figure 5). As a result of this, these areas are all showing average to above-average WRSI (Figure 6) using this analysis.
- More marginal production zones in Southern Zimbabwe and Southern Mozambique, where subsistence farmers are dependent on local production for their food security, experienced more extreme SOS anomalies (early in Zimbabwe, late in Mozambique). These anomalies are leading to below-average WRSI anomalies.
- Throughout much of Botswana and West-central South Africa, there has been a lack of seasonal onsets. These delays are anticipated to have negative impacts on the crop growing season, even if rain begins in early January.
This analysis is currently experimental, but we think it should be insightful in identifying areas which are likely to experience crop production anomalies early in the season. We will continue to monitor throughout the growing season.
The next logical step would be to start including CHIRPS-GEFS forecast to get projected rainfall information, identify the chances of SOS in the upcoming dekad, and then use that to forecast rest-of-the-season progress.
¹Dataset presented at the 2017 FEWS NET Science Update Meeting. Hobbins MT (2017), A global reference evapotranspiration service for the FEWS NET Science Community: derivation, FEWS applications, next steps. FEWS NET Science Update Meeting: Agro-Climatology for Food Security Assessment, Washington, D.C., 6-8 September. (Oral)