Abstract:The objective of this article is to identify the determinants of the adoption and intensity of integration of these systems among Ivorian farming households, using data from the Harmonized Survey on Household Living Conditions (EHCVM, 2021). The study employs a combined econometric approach that combines a binary logit model for the adoption decision and a fractional logit model for the intensity of integration. The results indicate that agricultural experience is the most significant determinant. Livestock size and land tenure security, as well as access to infrastructure and credit, also play a significant role in the adoption decision. In contrast, intensification depends more heavily on equipment ownership. The gender of the household head has a negative effect on the intensity of adoption, revealing existing inequalities. These results call for the development of agricultural policies that combine on-the-job training, improved access to credit and equipment, and targeted measures in favor of women farmers.
Abstract:Mechanization packages for rice often assume household labor is fungible, overlooking gendered task specialization. We track time-use diaries and yield responses in paired intensification trials with and without hired machine services. Where transplanting labor is primarily female, machine transplanting shifts unpaid hours toward weeding unless complementary weed control is bundled.
Abstract:Subsurface drains shorten residence time of nitrate and dissolved phosphorus after storms. We instrument nested outlets and use high-frequency sensors to quantify event-scale loads across contrasting drainage densities. Connectivity metrics explain more load variance than fertilizer rates alone, informing where edge-of-field wetlands would intercept peak export.
Abstract:Early infestation in silos is hard to confirm without destructive sampling. We train convolutional networks on acoustic recordings from probe sensors placed in wheat and maize bulk and compare against visual inspection of trap catches. Models detect activity days earlier than trap thresholds, enabling localized fumigation instead of whole-bin treatments.
Abstract:Heat load events elevate respiration rates and reduce feed conversion in outdoor finishing systems. We deploy infrared cameras over shaded and unshaded pens and synchronize surface temperatures with weather-station wet-bulb globe temperature. Partial shade lowers peak surface temperature enough to cut panting scores during mid-afternoon peaks.
Abstract:Informal seed exchange sustains landrace diversity where formal markets supply few adapted cultivars. We map exchange ties among highland households and relate centrality to on-farm variety richness. Households bridging distant villages conserve rare types, suggesting targeted support for network hubs rather than uniform distribution campaigns.
Abstract:Cadmium accumulation in acidic tropical soils threatens food safety for leafy vegetables. We compare rice husk, coffee husk, and wood-waste biochars amended at equal carbon rates and measure extractable cadmium, plant uptake, and soil pH over two crop cycles. Alkaline coffee-husk biochar most strongly reduces edible-tissue cadmium without suppressing yield.
Abstract:Index-based drought insurance aims to reduce basis risk, yet uptake remains low when payout triggers feel opaque. We combine framed field experiments with household surveys to estimate how framing, trust in weather stations, and liquidity constraints shape purchase decisions. Transparent trigger communication raises willingness to pay more than modest premium subsidies in our sample.
Abstract:Mixed pixels in medium-resolution imagery obscure early nitrogen deficiency signals in intercropped fields. We apply linear spectral unmixing with endmembers derived from field spectrometers and validate against SPAD and leaf tissue assays. Unmixed green vegetation fractions improve stress detection lead time compared with NDVI alone across three growing seasons.
Abstract:Rice production across South and Southeast Asia is increasingly influenced by climatic variability, particularly rising temperatures and erratic rainfall patterns. This study applied a multi-step methodological approach integrating climatic trend analysis, statistical modeling, and time-series forecasting to assess the sensitivity of paddy rice yields to temperature and rainfall variability across Sri Lanka’s paddy cultivation seasons from 1952 to 2022. Special attention was given to the flowering and harvesting stages, which are highly sensitive to climatic stress. The Autoregressive Integrated Moving Average (ARIMA) model was applied to forecast future climate variables and paddy yields, offering insights into potential long-term impacts. Results revealed a consistent increase in maximum temperature (Tmax) across cultivation seasons, while annual rainfall (ARF) showed no significant long-term trend. ARIMA forecasts suggest continued increases in Tmax, fluctuating rainfall patterns and sustained yield growth. These findings highlight the need for climate-resilient rice cultivation strategies that mitigate thermal and hydrological risks through regionally informed adaptation planning.