No abstract

Optimization of cotton irrigation termination (IT) can lead to more efficient utilization and conservation of limited water resources in many cotton production areas across the U.S. This study evaluated the effects of three IT timings on yield, fiber quality, and irrigation requirements of irrigated cotton in southwest Oklahoma during three growing seasons. The results showed cotton yield increased with later IT dates, but this response was highly dependent on the amount and timing of late-season precipitation events. Only a few fiber quality parameters were significantly different among treatments, suggesting a more limited impact of IT on fiber quality. When averaged over the three study years, the lint yield was significantly different amongst all treatments, with an average increase of 347 kg ha−1 from the earliest to the latest IT. Additionally, the seed yield and the micronaire were similar for the two earlier IT treatments and significantly smaller than the values under the latest IT treatment. The differences in fiber uniformity and strength were also significant amongst IT treatments. Strong positive relationships were found between yield components and average late-season water content in the root zone. Lint and seed yields plateaued at an average late-season soil matric potential of about −30 kPa and had a quadratic decline as soil moisture depleted. When benchmarked against the latest IT treatment, the earlier IT treatments achieved average reductions of 16–28% in irrigation requirement. However, this water conservation was accompanied with considerable declines in yield components and micronaire and smaller declines in fiber length, uniformity, and strength.

The traditional approach to modeling productive efficiency assumes that technology is constant across the sample. However, farms in different regions may face different production opportunities, and the technologies they employ may differ due to environmental factors. Therefore, rather than using a traditional stochastic frontier model in such cases, a stochastic meta-frontier (SMF) analysis is recommended to account for environmental factors between regions. It follows that differences in environmental factors between the upland and lowland regions in Anambra State, Nigeria, may result in farmers producing rice under different production and environmental conditions. Using the SMF model, this study, for the first time, determines technical efficiency (TE) and technological gap ratios (TGRs) of rice production from the upland and lowland regions in the Awka North Local Government Area of Anambra State, Nigeria. Our data are from a cross-section sample of randomly selected rice farmers. Results reveal that lowland regional rice producers are on average, significantly more technically efficient (91.7%) than their upland counterparts (84.2%). Additionally, mean TGRs associated with lowland rice farmers are higher (92.1%) than their corresponding upland producers (84.7%). While the upland rice producers are less technically efficient and further away from their full potential, results indicate that both sets of farmers do not use advanced technologies to match the industry’s potential. We suggest that agricultural policy should focus on providing regionally specific technologies, such as improved rice varieties that fit the working environment of the lagging area, to help rice farmers improve their resource efficiency and minimize technological gaps.

While climate change threatens global food security, health, and nutrition outcomes, Africa is more vulnerable because its economies largely depend on rain-fed agriculture. Thus, there is need for agricultural producers in Africa to employ robust adaptive measures that withstand the risks of climate change. However, the success of adaptation measures to climate change primarily depends on the communities’ knowledge or awareness of climate change and its risks. Nonetheless, existing empirical research is still limited to illuminate farmers’ awareness of the climate change problem. This study employs a Bayesian hierarchical logistic model, estimated using Hamiltonian Monte Carlo (HMC) methods, to empirically determine drivers of smallholder farmers’ awareness of climate change and its risks to agriculture in Zambia. The results suggest that on average, 77% of farmers in Zambia are aware of climate change and its risks to agriculture. We find socio-demographics, climate change information sources, climate change adaptive factors, and climate change impact-related shocks as predictors of the expression of climate change awareness. We suggest that farmers should be given all the necessary information about climate change and its risks to agriculture. Most importantly, the drivers identified can assist policymakers to provide the effective extension and advisory services that would enhance the understanding of climate change among farmers in synergy with appropriate farm-level climate-smart agricultural practices.

Evaluating irrigation schemes contributes to the identification of performance gaps and this may lead to implementation of necessary improvements for enhancing agricultural productivity. In Rwanda, despite significant investments in irrigated agriculture, most of the irrigation schemes are performing far below their planned capacity. This study aimed at benchmarking the performance of Rugeramigozi 1 and Rugeramigozi 2 irrigation schemes located in Rugeramigozi marshland, Rwanda using irrigation indicators developed by the International Water Management Institute (IWMI). The study showed that land productivity for both the two irrigation schemes was generally low. Rugeramigozi 2 irrigation scheme had superior performance than Rugeramigozi 1 in terms of water productivity due to adoption of deficit irrigation strategies that promoted water conservation. The performance indicators for water service delivery showed that water use was more sufficient in Rugeramigozi 1 compared to Rugeramigozi 2 irrigation scheme. The water delivery capacity performance for both schemes revealed that the existing irrigation canals were sufficient to meet the irrigation water requirements at peak demand. The analysis of financial performance in both schemes indicated that the collected irrigation fees were inadequate to cover the operation and maintenance costs. Similarly, the gross returns on investment were low in both irrigation schemes due to low crop yields that generated low revenue for farmers. Overall, the performance indicators showed that both Rugeramigozi 1 and Rugeramigozi 2 irrigation schemes were in need of intensive management and infrastructural improvements in order to increase productivity and enhance sustainability of the schemes.