Mid-season assessment of maize growing conditions in Southern Africa (2018-2019) reveals reason for concern
Authors: Will Turner, Laura Harrison, Greg Husak
Editor: Juliet Way-Henthorne
- Season Precipitation Performance Probability shows a high likelihood of below-normal rainfall throughout Angola, western Mozambique, Namibia, Botswana, Zimbabwe, and central South Africa with little chance of recovery.
- Northeastern South Africa’s maize triangle does show a likelihood of normal season precipitation totals due to DJF recovery.
- Current vegetation conditions (based on NDVI) are quite poor in the aforementioned countries.
- End-of-season vegetation conditions, based on WRSI, are projected to be below average in the same countries.
This blog communicates season-to-date rainfall along with modeled and observed crop impacts for the 2018-2019 maize growing season in southern Africa. It also employs some of the new operational monitoring products available from the Climate Hazards Center.
The southern Africa agricultural season broadly extends through the October-April season. While all regions are more dependent on subsets of that interval, the main producer of the region, South Africa, is shown to be strongly responsive to the December-February (DJF) rainfall (Shukla, in review). To capture these regional conditions and, specifically, conditions for South Africa, we begin with estimated probabilities of rainfall being below normal, normal, and above normal for each of the two monitoring windows (October to April and DJF).
The Season Precipitation Performance Probability (SPPP) quantitatively evaluates the probability of a current season’s total precipitation to finish in a given tercile, corresponding to below-normal (< 33rd percentile), normal (33rd–66th percentile), and above-normal (> 66th percentile) conditions, with respect to the historical record. Below normal to-date rainfall has significantly reduced the probability of normal total season (October to February) rainfall for much of the Southern Africa (Figure 1, above). Figure 2 (below) includes the 2-pentad CHIRPS-GEFS forecast in order to provide additional certainty of season outcomes.
The DJF monitoring window (Figures 3 & 4) is largely in agreement with the full-season window for our areas of concern. In both the October to April and the DJF monitoring windows, we see a significant uptick in the likelihood of given tercile outcomes (darker hues). This supports Shukla’s findings regarding the strong response of total seasonal rainfall to the DJF subset. Notably, the two outlooks differ in the positive change in potential DJF precipitation performance in northeastern South Africa (northern maize triangle area). This region shows an increased likelihood for above-normal DJF rainfall (recovery of rains; potential for groundwater recharge).
The DJF subset shows a similar ability to capture the impact of to-date rainfall on likely season total outcomes. Figure 3 (above) is based only on to-date rainfall. Figure 4 (below) includes the 2-pentad CHIRPS-GEFS forecast.
NDVI products, which capture the greenness of the vegetation, are useful for indicating how this to-date rainfall has impacted vegetation so far (Figure 5). Percent of average NDVI during mid-to-late January (Source: NDVI-MODIS GLAM) is more than 30% below normal for approximately 20% of South Africa’s major cropping area for this season. Some areas of northeastern South Africa and Eswatini show closer to normal or above normal NDVI; these correspond to areas that the CHIRPS monitoring products (above) showed recent recovery in rains.
Figure 5: Percent of normal NDVI during mid to late January (Source: NDVI-MODIS GLAM). Shades of green indicate areas of above normal vegetation greenness, while shades of brown indicate areas of below normal.
While useful for categorizing projected seasonal rainfall, the SPPP product’s unidimensional nature does limit its ability to wholly provide context to current/projected agricultural performance. Specifically, plant-water availability is not strictly defined by total precipitation; the other half of the equation, which reduces this availability, is evapotranspiration. With this in mind, it is prudent for us to use additional monitoring products that take this evaporative demand into account.
For this, the Famine Early Warning Systems Network (FEWS NET) commonly uses the Water Requirement Satisfaction Index (WRSI) to monitor growing seasons throughout Africa and Central America. The WRSI uses gridded precipitation (PPT) and reference evapotranspiration (RefET) inputs, along with crop phenological parameters, to calculate the ratio of plant-available water to crop-specific water demand at each stage of crop development. In so doing, the index puts specific interest on the timing of the rainfall (as opposed to just the total). Below, we show the results of WRSI model simulations that provide more details about current maize impacts.
Overall, lackluster rainfall early in the 2018-2019 season likely delayed the start of the crop growing season (Figure 7; shown in shades of red). Parts of central South Africa still have not received sufficient rainfall for germination (Figure 7; shades of brown). This late onset of rains could also be influencing the negative NDVI anomalies seen before, as vegetation may not be fully mature this year, while in previous years it is nearly mature by this time.
Figure 6: 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. Areas colored gray have yet to receive sufficient rainfall for a season start.
Figure 7: 2018-19 start of season anomaly with respect to the 1981-2017 mode SOS. Areas colored in shades of blue indicate regions where the current season started earlier than the historical mode, while areas colored in shades of red started late.
The lightest beige color indicates areas that have not yet started but are not beyond the mode start date. Darker hues of brown indicate areas that are past the mode SOS for that location and still have yet to receive sufficient rainfall for a season start.
In similar fashion to the SPPP calculation, we can use precipitation and RefET historical records to run out the remainder of the season and provide a collection of scenarios for the end of season WRSI. The median of these scenario outcomes (WRSI Outlook) is then compared to the median historical WRSI. We find that while starts did eventually occur for most of the region, the delay was followed by continued below average rainfall, and thus the outlook WRSI anomaly (Figure 8) and percent of normal (Figure 9) are well-below average throughout the aforementioned dry areas in the SPPP plots.
Figure 8. 2018-19 simulated end of season WRSI anomaly with respect to the 1981-2017 median WRSI. Hues of red indicate below average WRSI, while shades of blue indicate above average. Gray indicates average WRSI.
Figure 9: 2018-19 WRSI as a percent of normal, with respect to the 1981-2017 median WRSI. Green hues indicate above average WRSI, whereas orange/purple hues indicate below average. Pink denotes areas that have yet to receive sufficient rainfall for a start, and are beyond the mode SOS.
Additionally, the poor rainfall outlook for the 3rd dekad of January results in significant worsening of the WRSI Outlook (Figures 10 & 11).
2018-19 simulated WRSI anomaly (Figure 10, above) and percent of normal (Figure 11, below) including CHIRPS-GEFS forecast data for the 3rd dekad of January.
In agreement with our SPPP product, we see similar areas of concern in Angola, western Mozambique, Namibia, Botswana, Zimbabwe, and South Africa. As with the DJF monitoring window, there remains a potential for recovery to average crop conditions in northeastern South Africa.
As previously stated, most of these areas of concern are predominantly driven by DJF rainfall, so confidence in these observations should continue to increase over the next few weeks. More to this point, as season progress continues (Figure 12) and crop phenological cycle advances, crop-water demand will increase, and rainfall performance will have an increasing impact on the overall crop-water satisfaction. This is evident in the dramatic increase in severity of the negative WRSI anomaly and percent of normal when including CHIRPS-GEFS forecast for the third dekad of January.
Figure 12: The 2018-19 Season Progress combines the current SOS and the spatially varying LGP map (from GeoWRSI) to calculate how far along the season is at the current time as a percentage of the LGP.
This collection of products allows for in-depth monitoring of (1) the current state of rainfall-dependent growing seasons and (2) the impact to-date conditions could have on end-of-season agricultural productivity. While not as accurate as post-harvest assessments, these precipitation estimates, NDVI, and crop-water models help to provide sources of information for early warning and timely action.