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Doctoral Degrees (Crop Science)

Permanent URI for this collectionhttps://hdl.handle.net/10413/6576

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Browsing Doctoral Degrees (Crop Science) by Author "Chimonyo, Vimbayi Grace Petrova."

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    Crop suitability mapping for underutilized crops in South Africa.
    (2022) Mugiyo, Hillary.; Mabhaudhi, Tafadzwanashe.; Chimonyo, Vimbayi Grace Petrova.; Kunz, Richard Peter.
    Several neglected and underutilised species (NUS) provide solutions to climate change and create a Zero Hunger world, the Sustainable Development Goal 2. However, limited information describing their agronomy, water use, and evaluation of potential growing zones to improve sustainable production has previously been cited as the bottlenecks to their promotion in South Africa's (SA) marginal areas. Therefore, the thesis outlines a series of assessments aimed at fitting NUS in the dryland farming systems of SA. The study successfully mapped current and possible future suitable zones for NUS in South Africa. Initially, the study conducted a scoping review of land suitability methods. After that, South African bioclimatic zones with high rainfall variability and water scarcity were mapped. Using the analytic hierarchy process (AHP), the suitability for selected NUS sorghum (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth and taro (Colocasia esculenta) was mapped. The future growing zones were used using the MaxEnt model. This was only done for KwaZulu Natal. Lastly, the study assessed management strategies such as optimum planting date, plant density, row spacing, and fertiliser inputs for sorghum. The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multicriteria decision-making (MCDM) methods such as AHP (14.9%) and fuzzy methods (12.9%), crop simulation models (9.9%) and machine-learning-related methods (25.7%), are gaining popularity over traditional methods. The review provided the basis and justification for land suitability analysis (LSA) methods to map potential growing zones of NUS. The review concluded that there is no consensus on the most robust method for assessing NUS's current and future suitability. South Africa is a water-scarce country, and rainfall is undoubtedly the dominating factor determining crop production, especially in marginal areas where irrigation facilities are limited for smallholder farmers. Based on these challenges, there is a need to characterise bioclimatic zones in SA that can be qualified under water stress and with high rainfall variation. Mapping high-risk agricultural drought areas were achieved by using the Vegetation Drought Response Index (VegDRI), a hybrid drought index that integrates the Standardized Precipitation Index (SPI), Temperature Condition Index (TCI), and the Vegetation Condition Index (VCI). In NUS production, land use and land classification address questions such as “where”, “why”, and “when” a particular crop is grown within particular agroecology. The study mapped the current and future suitable zones for NUS. The current land suitability assessment was done using Analytic Hierarchy Process (AHP) using multidisciplinary factors, and the future was done through a machine learning model Maxent. The maps developed can contribute to evidence-based and site-specific recommendations for NUS and their mainstreaming. Several NUS are hypothesised to be suitable in dry regions, but the future suitability remains unknown. The future distribution of NUS was modelled based on three representative concentration pathways (RCPs 2.6, 4.5 and 8.5) for the years between 2030 and 2070 using the maximum entropy (MaxEnt) model. The analysis showed a 4.2-25% increase under S1-S3 for sorghum, cowpea, and amaranth growing areas from 2030 to 2070. Across all RCPs, taro is predicted to decrease by 0.3-18 % under S3 from 2050 to 2070 for all three RCPs. Finally, the crop model was used to integrate genotype, environment and management to develop one of the NUS-sorghum production guidelines in KwaZulu-Natal, South Africa. Best sorghum management practices were identified using the Sensitivity Analysis and generalised likelihood uncertainty estimation (GLUE) tools in DSSAT. The best sorghum management is identified by an optimisation procedure that selects the optimum sowing time and planting density-targeting 51,100, 68,200, 102,500, 205,000 and 300 000 plants ha-1 and fertiliser application rate (75 and 100 kg ha-1) with maximum long-term mean yield. The NUS are suitable for drought-prone areas, making them ideal for marginalised farming systems to enhance food and nutrition security.
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    Quantifying productivity and water use of sorghum intercrop systems.
    (2015) Chimonyo, Vimbayi Grace Petrova.; Modi, Albert Thembinkosi.
    Rural sub-Saharan Africa (SSA) faces the challenge of achieving food security under water scarcity amplified by climate change and variability. Under these conditions, it is necessary to adopt cropping systems that have a potential to improve productivity. The aim of the study was to assess the feasibility of a sorghum-cowpea-bottle gourd intercrop systems with a view to determine the resource use efficiencies. This was achieved through a series of studies which included conducting critical literature reviews, quantifying water use and water use efficiency of sorghum-cowpea-bottle gourd, and modelling such systems using Agricultural Production Systems Simulator (APSIM). Field trials were conducted at the University of KwaZulu–Natal’s, Ukulinga Research Farm over two seasons (2013/14 and 2014/15) under varying water regimes [full irrigation (FI), deficit irrigation (DI) and rainfed (RF)]. Intercrop combinations considered were sole sorghum, cowpea and bottle gourd as well as intercrops of sorghum–cowpea and sorghum–bottle gourd. Data collected included soil water content, plant height/vine length, leaf number, tillering/branching, leaf area index, relative leaf water content, stomatal conductance and chlorophyll content index as well as biomass accumulation and partitioning. Yield and yield components, water use (WU) and WUE were calculated at harvest. Extinction coefficient, intercepted photosynthetic active radiation (IPAR) and radiation use efficiency (RUE) for biomass and grain were also determined. Land equivalent ratio (LER) was used to evaluate intercrop productivity. Growth, yield and water use (ET) of the sorghum–cowpea intercrop system were simulated using APSIM. The validated model was then used to develop best management practices for intercropping. The review showed that aboveground interactions within intercrop systems have thoroughly been investigated while belowground interactions were mostly limited. The review showcased the potential of bottle gourd as a versatile food crop. The field trials established that sorghum yields were stable across different water regimes. This was mainly achieved through facilitative interaction within the intercrop systems which allowed for greater eco-physiological adaptation resulting in improved water capture and use. Improved water capture and use also increased WUE (50.68%) and RUE (8.96%). The APSIM model was simulated growth, yield and WU of an intercrop system under varying water regimes satisfactorily. The model over–estimated biomass (6.25%), yield (14.93%) and WU (7.29%) and under–estimated WUE (-14.86%). Scenario analyses using APSIM showed that the development of best management practices should be agro–ecology specific to ensure dynamic climate change adaptation strategies and increase resilience. It was concluded that intercropping results in improved productivity, especially under water–limited conditions. As such, it that can be used by farmers located in semi-arid and arid regions as an adaptation strategy for increased productivity. Dynamic agronomic management practices should be adapted to further increase the system’s resilience to predicted climatic uncertainties. Future studies on intercropping should consider root interactions and possibly different plant populations and planting geometry as factors that might influence resource capture and use. Decision support systems should be promoted within farming communities to better manage risks associated with on-farm decision making.