Fine-scale multi-temporal and spatial analysis of agricultural drought in agro-ecological regions of Zimbabwe

Figures & data

Figure 1. Agroecological regions of Zimbabwe (Vincent and Thomas, 1961) overlaid on Sentinel 2016 landcover map (European Space Agency, 2016)

Table 1. The rainfall and agriculture potential of the five agro-ecological regions of Zimbabwe.

Region	Area extent (million hectares)	% Total land area	Annual rainfall	Agricultural Productivity
I	0.62	1.6	>1000	Suitable for dairy farming, forestry, tea, coffee, fruit, beef and maize production
II	7.31	18.8	750-1000	Suitable for intensive farming based on maize, tobacco, cotton and livestock
III	6.85	17.6	650-800	Semi-intensive farming region characterised by mid-season dry spells. Suitable for livestock, fodder, and cash crop production.
IV	12.84	33.0	450-650	Semi-intensive agricultural region characterised by periodic seasonal droughts and severe dry spells during the rainy season. Suitable for farming systems based on livestock and resistant fodder crops.
V	11.28	11.28	<450	Extensive farming region. Suitable for extensive cattle or game ranging

Source: Adapted from Moyo (2000)

Table 2. The name and location of Meteorological stations where rainfall used for validating Vegetation Condition Index was derived as well as the Agro-ecological regions across a rainfall gradient in Zimbabwe.

Station name	Location (LatLon)	Agro-ecological region
Beitbridge	−22.217, 30.000	V
Kadoma	−18.317, 29.883	IIb
Masvingo	−20.067, 30.867	III
Mvurwi	−17.033, 30.850	IIa
Mutoko	−17.400, 32.217	IV
Nyanga	−18.217, 32.733	I

Table 3. Metrics used to validate the satellite derived drought index based on SPI calculated from six rainfall stations across Agro-ecological regions of Zimbabwe.

Measure	Derivations
Sensitivity	TPR = TP / (TP + FN)
Specificity	SPC = TN / (FP + TN)
Accuracy	ACC = (TP + TN) / (P + N)
F1 Score	F1 = 2TP / (2TP + FP + FN)
Matthews Correlation Coefficient	TP*TN - FP*FN / sqrt((TP + FP)*(TP + FN)*(TN + FP)*(TN + FN))
True Skills Statistic	TPR + TNR −1.

*TP = True Positive; FN = False Negative; TN = True Negative; FP = False Positive.

Figure 2. Frequency of drought occurrence for the months of a) October, b) November, c) December, d) January, e) February, f) March and g), April.

Figure 3. Spatial variation in drought frequency at the annual scale across Agro-ecological regions of Zimbabwe for the years a) 2000, b) 2001, c)2002, d) 2003, e) 2004, f) 2005, g) 2006, h)2007, i) 2008, and, j) 2009.

Figure 4. The spatial variation in the frequency of drought occurrence at the annual scale across Agro-ecological regions of Zimbabwe for the years a) 2010, b) 2011, c) 2012, d) 2013, e) 2014, f) 2015, g) 2016, h) 2017 and, i) 2018.

Figure 5. The spatial variations in drought frequency across Zimbabwe in the first and second part of season from the year 2000 to 2018.

Figure 6. Overall drought frequency from the year 2000 to 2018.

Figure 7. Probability of drought occurrence based on dekadal data from 2000 to 2018.

Table 4. Validation of vegetation condition index based on one month standardised precipitation index derived from six stations distributed across agro-ecological regions of Zimbabwe.

Measure	Beitbridge	Kadoma	Masvingo	Mvurwi	Mutoko	Nyanga	All six stations
Sensitivity	0.6383	0.8769	0.4516	0.8000	0.8333	0.7857	0.7195
Specificity	0.5085	0.5741	0.6364	0.6709	0.6506	0.6264	0.6452
Accuracy	0.5660	0.7395	0.5882	0.7143	0.7059	0.6639	0.6681
F1 Score	0.5660	0.7862	0.3636	0.6531	0.6316	0.5238	0.5719
Matthews Correlation Coefficient	0.1468	0.4784	0.0793	0.4450	0.4446	0.3502	0.3375
True Skills Statistic	0.1468	0.451	0.088	0.4709	0.4839	0.4121	0.3647

Source: Meteorological Services Department of Zimbabwe.

Table 5. Validation of vegetation condition index based on 3-month standardised precipitation index derived from six stations distributed across agro-ecological regions of Zimbabwe.

Measure	Beitbridge	Kadoma	Masvingo	Mvurwi	Mutoko	Nyanga	All six stations
Sensitivity	0.3750	0.5000	0.5333	0.5000	0.2727	0.5833	0.4556
Specificity	0.8000	0.9500	0.7727	0.9333	0.8214	0.8462	0.8582
Accuracy	0.5385	0.7500	0.6757	0.8095	0.6667	0.7632	0.7013
F1 Score	0.5000	0.6400	0.5714	0.6000	0.3158	0.6087	0.5430
Matthews Correlation Coefficient	0.1845	0.5164	0.3148	0.4985	0.1049	0.4402	0.3470
True Skills Statistic	0.175	0.45	0.306	0.4333	0.094	0.4295	0.3138

Figure 8. The relationship between maize yield (in 1000 metric tonnes) and Vegetation Condition Index (a) for each of the years from 1998 to 2018 and b) for the 20-year period.

Figure 9. Variations in monthly SPI based on rainfall measured from 1981 to 2020 at Beitbridge weather stations in Zimbabwe.

Figure 10. Variations in monthly SPI based on rainfall measured from 1981 to 2020 at Kadoma weather stations in Zimbabwe.

Figure 11. Variations in monthly SPI based on rainfall measured from 1981 to 2020 at Masvingo weather station in Zimbabwe.

Figure 12. Variations in monthly SPI based on rainfall measured from 1981 to 2020 Mutoko weather station in Zimbabwe.

Figure 13. Variations in monthly SPI based on rainfall measured from 1981 to 2020 at Mvurwi weather station in Zimbabwe.

Figure 14. Variations in monthly SPI based on rainfall measured from 1981 to 2020 at Nyanga weather station in Zimbabwe.

ESA. 2016. ESA Climate Change Initiative Land Cover Sentinel-2 Prototype Map of Africa 2016 at 20 meters. Accessed 20 April 2021. http://2016africalandcover20m.esrin.esa.int/download.php

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Moyo S. 2000. The political economy of land acquisition and redistribution in Zimbabwe, 1990-1999. J Southern African Stud. 26(1):5–28.

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Data availability

NDVI datasets used in this study are readily available at http://www.vito_eodata.be.