Abstract:Maize is an important staple crop for poverty reduction and global food security in Sub Saharan Africa. Food insecurity can be combated through adoption of productivity improving technologies, which include improved maize varieties. In that endeavour, South Africa has promoted various improved maize varieties which include open pollinated varieties (OPVs), hybrids, and genetically modified (GM) varieties. Despite this, the traditional landrace varieties have also been dominating in the country. However, the ho… Show more
“…Maize yields were below the national average, which often fluctuates between 4000 kg/ha and 7000 kg/ha [61][62][63]. Low yields in ORTDM, and other smallholder farming areas in South Africa, are also reported by Chimonyo et al [64] and by Kambanje et al [65].…”
Section: Relationship Of Yield With Rainfall and Ndvimentioning
Adverse weather is one of the most prevalent sources of risk in agriculture. Its impacts are aggravated by the lack of effective risk management mechanisms. That is why resource-poor farmers tend to respond to weather risks by adopting low-capital investment, low-return, and low-risk agricultural practices. This challenge needs to be addressed with innovative risk management strategies. One of the tools that is gaining traction, especially in the developing countries, is weather-index-based insurance (WII). However, WII uptake is still low because of several constraints, one of which is basis risk. This study attempts to address this problem by evaluating the suitability of TAMSAT, CHIRPS, MODIS, and Sentinel-2 data for WII. We evaluated the first three datasets against in situ rainfall measurements at different spatial and temporal scales over the maize-growing season in a smallholder farming area in South Africa. CHIRPS had higher correlations with in situ measured rainfall data than TAMSAT and MODIS NDVI. CHIRPS performed equally well at 10 km and 25 km spatial scales, and better at monthly than daily and 16-day time steps (maximum R = 0.78, mean R = 0.72). Due to the lack of reliable historical yield data, we conducted yield surveys over three consecutive seasons using an objective crop cut method. We then assessed how well rainfall and NDVI related with maize yield. There was a poor relationship between these variables and maize yield (R2 ≤ 0.14). The study concludes by pointing out that crop yield does not always have a linear relationship with weather and vegetation indices, and that water is not always the main yield-limiting factor in smallholder farming systems. To minimize basis risk, the process of designing WII must include identification of main yield-limiting factors for specific localities. Alternatively, insurers could use crop water requirement methods to design WII.
“…Maize yields were below the national average, which often fluctuates between 4000 kg/ha and 7000 kg/ha [61][62][63]. Low yields in ORTDM, and other smallholder farming areas in South Africa, are also reported by Chimonyo et al [64] and by Kambanje et al [65].…”
Section: Relationship Of Yield With Rainfall and Ndvimentioning
Adverse weather is one of the most prevalent sources of risk in agriculture. Its impacts are aggravated by the lack of effective risk management mechanisms. That is why resource-poor farmers tend to respond to weather risks by adopting low-capital investment, low-return, and low-risk agricultural practices. This challenge needs to be addressed with innovative risk management strategies. One of the tools that is gaining traction, especially in the developing countries, is weather-index-based insurance (WII). However, WII uptake is still low because of several constraints, one of which is basis risk. This study attempts to address this problem by evaluating the suitability of TAMSAT, CHIRPS, MODIS, and Sentinel-2 data for WII. We evaluated the first three datasets against in situ rainfall measurements at different spatial and temporal scales over the maize-growing season in a smallholder farming area in South Africa. CHIRPS had higher correlations with in situ measured rainfall data than TAMSAT and MODIS NDVI. CHIRPS performed equally well at 10 km and 25 km spatial scales, and better at monthly than daily and 16-day time steps (maximum R = 0.78, mean R = 0.72). Due to the lack of reliable historical yield data, we conducted yield surveys over three consecutive seasons using an objective crop cut method. We then assessed how well rainfall and NDVI related with maize yield. There was a poor relationship between these variables and maize yield (R2 ≤ 0.14). The study concludes by pointing out that crop yield does not always have a linear relationship with weather and vegetation indices, and that water is not always the main yield-limiting factor in smallholder farming systems. To minimize basis risk, the process of designing WII must include identification of main yield-limiting factors for specific localities. Alternatively, insurers could use crop water requirement methods to design WII.
“…Over the 3-year period between 2018 and 2020, maize yields ranged between 367.10 and 7449.13 kg/ha with an average of 3259.16 kg/ha (Table 2), which reveals that most farmers produced below the national average, which often ranges from 4700 to more than 7000 kg/ha [49][50][51]. Other studies also reported that maize yields in ORTDM and the Eastern Cape province at large are low and less than potential [52][53][54]. The most important input influencing yield was seed variety.…”
Weather extremes pose substantial threats to food security in areas where the main source of livelihood is rain-fed crop production. In most of these areas, agricultural index insurance (AII) is recognized as being capable of securitizing food production by providing safety nets against weather-induced crop losses. Unfortunately, however, AII does not indemnify farmers for non-weather-related crop losses. This study investigates how this gap can be filled by exploring strategies through which AII can be linked with non-weather factors that influence crop production. We do this by using an improvised variable ranking methodology to identify these factors in the O.R. Tambo District Municipality, South Africa. Results show that key agrometeorological variables comprising surface moisture content, growing degree-days, and precipitation influence maize yield even under optimal weather conditions, while seed variety, fertilizer application rate, soil pH, and ownership of machinery play an equally important role. This finding is important because it demonstrates that although AII focuses more on weather elements, there are non-weather variables that may expose farmers to production risk even under optimal weather conditions. As such, linking AII with critical non-weather, yield-determining factors can be a better risk management strategy.
“…40 Although their yields are claimed to be low, Landrace maize cultivars are widely produced in rural South Africa for a variety of reasons to include low seed cost and tolerance to harsh agro-ecology. 4 …”
Section: Improved Open Pollinated Varietiesmentioning
confidence: 99%
“…Previous studies also noted high usage of Landrace maize cultivars in rural areas because of their adaptability to local conditions and yield stability. 4 , 31 , 32 Figure 2. Production status of maize cultivars from the study area.…”
Section: Maize Cultivars Grown From the Study Areamentioning
confidence: 99%
“…Despite all these challenges, there is a consensus that maize productivity from this subsector is very low. 4 Efforts to improve the low maize productivity seem to suggest the use of improved maize cultivars (genetically modified [GM] maize, conventional hybrids and improved Open Pollinated Varieties) as one of the options among others. 5 Unfortunately, improved maize cultivars do not perform very well under the agro-ecological conditions of most smallholder farmers and this is worsened by their high seed costs.…”
Maize plays a significant role among rural farming households in South Africa. The study therefore estimated the drivers of maize cultivars’ selection choices among rural farming households focusing on the commonly grown cultivars from the study area (Landrace and genetically modifies [GM] maize). A cross-sectional survey of 650 respondents randomly selected from Port St Johns and King Sabata Dalindyebo Local Municipalities of the Eastern Cape Province of South Africa were used. Descriptive results revealed that a significant number of the respondents from the study area (65%) were growing Landrace maize cultivars, followed by GM maize (31%), improved OPVs (3%) and conventional hybrids (1%). Multivariate Probit regression estimates show that GM maize cultivars’ selection is positively influenced by the amount of rainfall (1% level), household size (5% level), education (1% level), size of arable land (10% level) and access to cell phones (5% level), whilst negatively influenced by the employment status (5%). Conversely, the selection of Landrace maize cultivars is negatively influenced by the of amount rainfall (1% level), education (1% level), income (10% level), access to cell phone (10% level) and radio (10% level) whilst positively influenced by the number of livestock (5% level). The study therefore argues that GM maize cultivars may be meaningfully promoted in high rainfall areas, focusing on arable land sizes and targeted awareness campaigns. The promotion of Landrace maize cultivars may be targeted in low rainfall areas in a mixed farming setting to enhance the complementarity of maize and livestock.
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