Assessing nutritional water productivity of selected African leafy vegetables using the agricultural production systems simulator model.

Abstract

Food and nutrition insecurities are regarded as one of the main challenges in the Sub-Saharan region. While substantial progress has been made to address food and nutrition challenges, this progress has varied across the region and over time in response to climate change hazards. Agriculture has been used as the main driver to improve food and nutrition security; however, productivity in these marginalised communities remains low. African leafy vegetables (ALVs) provide an unprecedented opportunity to ensure food security, lessen poverty and diversify farming systems while improving human health and increasing income. Crop modelling can generate information about the crop's growth, development, water, and nutritional needs. The primary objectives of this study were (i) to assess the growth and productivity of selected ALVs (amaranth (Amaranth spp), cowpea (Vigna unguiculata), sweet potato (Ipomoea batatas) and wild mustard (Sinapis arvensis)) under different management practices, and (ii) assess water productivity (WP) and nutritional water productivity (NWP) of the selected ALVs. Desktopbased research was conducted to achieve the mentioned objectives. Here, information on the studied crops' agronomy secondary data was gathered through a careful literature search. This secondary information was then used to model growth and productivity and quantify nutritional water productivity at different management practices. The Agricultural Production Systems Simulator (APSIM) was used to simulate growth and productivities under different management scenarios of planting date, plant density, fertiliser application and irrigation. We used the soil and climatic data from the University of KwaZulu-Natal's research farm (Ukulinga Research Farm) situated in Pietermaritzburg, South Africa (29°37′S; 30°16′E; 775 m a.s.l.), to calibrate the model. All data analysis was done using descriptive statistical analysis (R software). All mean values were subjected to a t-test set at p<0.05 significance. The results showed that depending on crop species. Different management practices can be relevant to achieve optimum growth and productivity for various purposes. The investigated ALVs were found to have high nutrient content. Compared to one another, amaranth was more nutrient-dense and wild mustard the least dense crop. On the other hand, NWP was comparatively high on both amaranth and cowpea.