Drivers of maize yield variability at household level in Northern Ghana and Malawi

Figures & data

Figure 1. Map of the study showing the location of the survey households and zones with relatively similar rainfall patterns. The rainfall zones were generated from long-term (2014–2020) aggregation of annual TerraClimate satellite rainfall estimates.

Figure 2. The average Palmer Drought Severity Index (PDSI) values for the growing seasons of Malawi and Ghana in 2013–2014 and 2018–2019. The growing season for Ghana spans from April to October, while for Malawi, it takes place between October and April of the following year.

Table 1. Variables used in the models.

Class	Continuous variables	Categorical variables
Demographics	Household size (Hhsize), age of the household head (Headage), the number of education years for the household head (Headedu), maximum years of adult education (Edumax), average years of adult education (Meanedu) and plots fully managed by female (FempltsF).	Female headed households (Femhead)
Production practices	Area intercropped (intercropHa), fertilizer kg/ha (FertHa), pesticide value/ha (PestHa), area under maize cultivation (MaizeA), land size (Landsize), number of plots owned (Nplots), livestock units (TLU), number of crops farmed (Ncrop), crop diversity (CropD), livestock diversity (LvstD) and total income (Totincome).	Practice intercropping (Intercrop), applies manure (Manure), uses hired labour (Labour), uses pesticides (Pest), any residue left on farm (Residue), practices crop rotation (Rotation), can get credit (Credit), applies fertilizer (Fertilizer), practice fallowing (Fallow), soil erosion (Erosion).

The text in brackets indicate how the variables are referred to in various figures.

Table 2. Gridded environmental and weather data used in the model.

	Variable name and reference	Original spatial resolution (m)	Rationale/hypothesis
Vegetation indices	Monthly Enhanced Vegetation Index (EVI MODIS; janEVI, febEVI, marEVI, aprEVI, julEVI, junEVI, augEVI, octEVI, novEVI, decEVI - Didan 2015). The intext annotations are;,	250	Vegetation indices are essential in agriculture as they assess plant health and vigor. They are sensitive to changes in growth and development, which directly affect crop yield. Among the vegetation indices, EVI is particularly significant in predicting maize yield (Zhang et al. 2019; Meng et al. 2021) because it can more accurately capture changes in vegetation in varying atmospheric conditions and soil backgrounds.
Hydro-meteorological	Monthly precipitation, soil moisture (Soilm) and PDSI (pdsi) (TerraClimate Abatzoglou et al. 2018).	4000	To support growth, maize roots need a proper amount of soil moisture to absorb nutrients effectively. Low soil moisture levels can lead to drought stress, causing reduced yields. Similarly, high soil moisture levels can also be harmful, as it may lead to waterlogging and a decrease in oxygen availability to the roots, resulting in lower yields. Hence, maintaining an appropriate level of soil moisture is crucial for optimizing maize yield.
Topography	Elevation (DEM - Farr et al. 2007).	30	Elevation has a significant impact on maize yield as it affects temperature and rainfall levels. Generally, higher elevations have cooler temperatures that can affect the rate of maize development and the length of the growing season. Moreover, higher elevations usually receive more rainfall, which is advantageous for maize growth and development, but excessive or irregular rainfall can cause waterlogging or drought stress and lower the maize yield.
Socio-economic	Livestock density (LivDens) - (https://www.fao.org/livestock-systems/global-distributions/en/). Access to markets (MarketAccess) - (Cattaneo et al. 2021).	10000	Livestock can have both positive and negative impacts on maize yield. On the one hand, their manure can provide essential nutrients that have been linked to increased maize productivity. However, when livestock density is too high, it can result in competition for crop residue. In such cases, most of the residue is consumed by the livestock, leaving only a small amount of organic matter in the soil, which can lower soil nutrient levels and ultimately reduce maize yield.
Soil properties	Cation carbon exchange (CEC), organic carbon (OrgCab), soil ph (Soilph), soil bulk density (Soilbulk), clay, silt and sand (iSDA - Hengl et al. 2021).	30	Soil structure, organic matter, and key soil nutrients like nitrogen, phosphorus, and potassium are crucial for maize growth and yield. High levels of organic matter improve soil structure, water-holding capacity, and fertility, leading to higher maize yields. Deficiencies in any of these nutrients can reduce yield, emphasizing the importance of managing soil properties for optimal maize production.

The text in brackets anotate how the variables are referred to in various figures.

Figure 3. Boxplots showing the distribution of the maize yield data for Ghana and Malawi with (a) and without (b) outliers were removed. The blue text is the number of households per cluster. The clusters are as described in Figure 1.

Table 3. The number of retained predictor variables after the VSURF thresholding step.

Model	D1	D2	D3	D4
All predictors	49	51	52	52
Categorical HH + satellite data	37	39	39	42

Table 4. Model performance metrics when all predictors were used and when only the categorical household survey was used.

	Ghana				Malawi
	All		Categorical		All		Categorical
	D1 μ = 712.28	D2	D1	D2 μ = 820.43	D3 μ = 1663.03	D4	D3	D4 μ = 1210.15
RMSE	415.88	407.20	426.89	418.72	906.48	635.99	1038.53	703.88
nRMSE	0.58	0.57	0.52	0.51	0.54	0.52	0.62	0.58
R^2	0.11	0.15	0.06	0.10	0.35	0.24	0.14	0.07

Figure 4. Variable importance and partial dependence plots for Ghana in 2013. (a) and (b) All predictor and categorical variables importance plots respectively. (c) and (d) Partial dependence plots for the top 6 predictors with all predictors and only with categorical variables respectively.

Figure 5. Variable importance and partial dependence plots for Ghana in 2019. (a) and (b) All predictor and categorical variables importance plots respectively. (c) and (d) Partial dependence plots for the top 6 predictors with all predictors and only with categorical variables respectively.

Figure 6. The spatial maize yield prediction and yield gain for Ghana in 2013 and 2019 when fertilizer use was incorporated as a useful agronomic practice. (a) When no agronomic practice was used. (b) Fertilizer use and (c) yield gain/loss (Figure 6b – Figure 6a).

Figure 7. Variable importance and partial dependence plots for Malawi in 2013. (a) and (b) All predictor and categorical variables importance plots respectively. (c) and (d) Partial dependence plots for the top 6 predictors with all predictors and only with categorical variables respectively.

Figure 8. Variable importance and partial dependence plots for Malawi in 2019. (a) and (b) All predictor and categorical variables importance plots respectively. (c) and (d) Partial dependence plots for the top 6 predictors with all predictors and only with categorical variables respectively.

Figure 9. The spatial maize yield prediction and yield gain for Malawi in 2013 and 2019 when fertilizer use was incorporated as a useful agronomic practice. (a) When no agronomic practice was used. (b) Fertilizer use and (c) yield gain/loss (Figure 9b – Figure 9a).

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Data availability statement

All the R programming scripts and excel files used in fitting the models are available in github repository (https://github.com/Muthono19/Predicting-Maize-Yield-in-Ghana-and-Malawi.git). The remote sensing data used is open source and can be downloaded through GEE.