Search Results (9)

This study examines the effects of natural disasters, such as typhoons, floods, droughts, and pest infestations, on the technical efficiency of rice production in Vietnam. Employing stochastic frontier analysis (SFA), the research estimates the technical efficiency in rice production of 2394 farmers from the 2018 Vietnam Access to Resources Household Survey (VARHS) dataset. The findings indicate that the average technical efficiency of rice production among these farmers is 78.99%. Exposure to natural disasters and pest infestations leads farmers to reduce their investments in rice production, resulting in decreased technical efficiency, lower yields, and reduced profitability. Among the various disasters, droughts have the most significant adverse impact on technical efficiency in rice production. The results highlight the limited capacity of farmers to cope with the challenges posed by natural disasters in rice production. The study emphasizes the importance of providing timely support to farmers, fostering resilience within the context of rice farming, and enhancing agricultural sustainability in Vietnam. To address these challenges effectively, policymakers are advised to prioritize facilitating farmers’ access to agricultural insurance. Additionally, encouraging income diversification among farmers becomes crucial to ensuring provisions in the case of income loss from rice production due to natural disasters or pest infestations. Moreover, measures such as promoting climate-smart agricultural practices, improving water management infrastructure, establishing early warning systems, and emphasizing pest and disease control measures can be implemented to mitigate losses resulting from natural disasters and pest infestations. Full article

This study examines Cambodian farmers’ demand for weather index insurance (WII), an innovative financial product, for managing climate change-related risks. Rice and cassava farmers in Battambang Province of Cambodia were interviewed to understand their preferences for WII. We applied a binary logistic model to quantify the factors that influence farmers’ WII demand. We discovered that farmers’ marital status and off-farm labor are crucial factors that impact the demand for WII. More importantly, we also investigated gender differences, considering the critical role of women in the agricultural sector and personality differences between men and women. Our findings indicated that for male respondents, being married and having an additional off-farm laborer increase the probability of demand for WII by 72.6% and 36.8%, respectively. For female respondents, the education level is the most significant factor in making purchase decisions. An additional year of education increases the probability of WII demand by 5.0%. Generally, our results are consistent with some prior studies but inconsistent with others. This suggests that further research is necessary to understand the barriers associated with WII schemes and how to overcome them. Regardless, our study provides valuable insights for various stakeholders in implementing WII schemes, including financial professionals, insurance companies, communities, and governments, for designing more flexible WII products, improving farmers’ financial literacy, and providing effective post-event support to enhance farmers’ resilience to climate change. Full article

The alternate wetting and drying (AWD) as water-saving technology aligns with the good agricultural practices (GAP) principles, particularly in the environmental management of water conservation. Thus, GAP adopters as farmer groups are seen as viable AWD adopters in the initial stages of scaling out the adoption in Thailand. However, the understanding of integrating AWD as water-saving management among GAP adopters remains scant. Using the case of rice GAP farmers in Thailand, the study found a higher probability of adoption intention among GAP compared to non-GAP. AWD perceived advantage, knowledge, and the suitability of rice farms for AWD adoption trials are positively associated with higher adoption intention. While higher fixed cost lowers the probability of adoption, variable cost is positively associated with higher adoption intention in the short-run production decision. In order to scale out the adoption of AWD, farmers’ understanding of the safe and proper application of AWD, together with assistance for crop insurance in the case of crop failure, will be crucial. Risks connected with the adoption decision continue to be the biggest barrier to adoption, especially among small-scale farmers. Full article

This study examines Cambodian rice farmers’ willingness to pay for the weather-indexed insurance (WII) proposed to manage the financial impact of shifting monsoon rainfall patterns in Battambang Province in north-western Cambodia. Detailed interviews are conducted in the districts of Bavel and Thma Koul. We first analyse farmer respondents’ socioeconomic and demographic characteristics, climate change perceptions and experience, risk attitudes, and awareness of insurance. The binary logistic model is used to identify factors that significantly impact farmers’ willingness to pay (WTP) for WII. Our results show that farmers in general had lower awareness of how to use innovative financial products to adapt to extreme weather. The results also demonstrate that farmer respondents’ marital status, the number of off-farm labourers, and the farm size have a positive effect, whereas the number of children in the household has a negative effect on farmers’ WTP for WII. Specifically, being married, an increase of one off-farm labourer, and an increase of one hectare (ha) of farmland increase the probability of demand for WII by 38.6%, 21.4%, and 5.1%, respectively. In contrast, an increase of one child reduces the probability of WII demand by 9.7%. We also identify challenges confronted by Cambodian farmers for participating in the proposed WII scheme and provide relevant recommendations to overcome these challenges. Full article

In this paper, we determined the factors that affect the waiting time of rice farmers’ willingness to pay the premium for the Rice Farming Insurance Program (RFIP) using survival analysis. The survival analysis method was carried out using the Cox proportional hazard model with the Efron approach. The case study in this research is rice farmers in Cibungur Village, Parungponteng District, Tasikmalaya Regency. The results of the analysis show that the predictor variables that are significant to the waiting time of rice farmers’ willingness to pay the insurance premium for RFIP are their last education, other occupations, rice production, and farming costs. The results of the research are expected to produce additional information for the government and implementers of rice farming insurance regarding the condition of farmers in the field, so that it can be improved in the future. Full article

Rice production in Nigeria is vulnerable to climate risks and rice farmers over time have experienced the risks and their respective impacts on rice farming. Rice farmers have also responded to perceived climate risks with strategies believed to be climate-smart. Farmers’ perception of climate risks is an important first step of determining any action to be taken to counteract the negative effects of climate change on agriculture. Studies on the link between perceived climate risks and farmers’ response strategies are increasing. However, there are limited studies on the determinants of rice farmers’ perception of climate events. The paper therefore examined climate change perception and uptake of climate-smart agriculture in rice production in Ebonyi State, Nigeria using cross-sectional data from 347 rice farmers in an important rice-producing area in Nigeria. Principal component analysis, multivariate probit regression model and descriptive statistics were adopted for data analysis. Perceived climate events include increased rainfall intensity, prolonged dry seasons, frequent floods, rising temperature, severe windstorms, unpredictable rainfall pattern and distribution, late onset rain, and early cessation of rain. Farmers’ socioeconomic, farm and institutional characteristics influenced their perception of climate change. Additionally, rice farmers used a variety of climate-smart practices and technologies to respond to the perceived climate events. Such climate-smart practices include planting improved rice varieties, insurance, planting different crops, livelihood diversification, soil and water conservation techniques, adjusting planting and harvesting dates, irrigation, reliance on climate information and forecasts, planting on the nursery, appropriate application of fertilizer and efficient and effective use of pesticides. These climate-smart agricultural measures were further delineated into five broad packages using principal component analysis. These packages include crop and land management practices, climate-based services and irrigation, livelihood diversification and soil fertility management, efficient and effective use of pesticide and planting on the nursery. High fertilizer costs, lack of access to inputs, insufficient land, insufficient capital, pests and diseases, floods, scorching sun, high labour cost, insufficient climate information, and poor extension services were the barriers to uptake of climate-smart agriculture in rice production. Rice farmers should be supported to implement climate-smart agriculture in rice production in order to achieve the objectives of increased rice productivity and income, food security, climate resilience and mitigation. Full article

Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments. Full article

The conventional tillage based rice-wheat system (RWS) in Indo-genetic plains (IGP) of South Asia is facing diverse challenges like increase in production cost and erratic climatic events. This results in stagnated crop productivity and declined farm profitability with increased emission of greenhouse gases. Therefore, 3-year multi-location farmer’s participatory research trial was conducted to assess the impact of crop establishment and residue management techniques on crop productivity, economic profitability and environmental footprints in RWS. The aim of this study was to analyze the effect of combinations of improved agronomic technologies compared to farmer’s practices (FP) on crop productivity, profitability, resource use efficiency and environmental footprints. The experiment had six scenarios that is, S1-Farmer’s practice; Conventional tillage (CT) without residue; S2-CT with residue, S3- Reduced tillage (RT) with residue + Recommended dose of fertilizer (RDF); S4-RT/zero tillage (ZT) with residue + RDF, S5-ZT with residue + RDF + green seeker + tensiometer + information & communication technology + crop insurance and S6- S5 + site specific nutrient management. Climate smart agriculture practices (CSAPs; mean of S4, S5 and S6) increase system productivity and farm profitability by 10.5% and 29.4% (on 3 yrs’ mean basis), whereas, improved farmers practices (mean of S2 and S3) resulted in only 3.2% and 5.3% increments compared to farmer’s practice (S1), respectively. On an average, CSAPs saved 39.3% of irrigation water and enhanced the irrigation and total water productivity by 53.9% and 18.4% than FP, respectively. In all the 3-years, CSAPs with high adaptive measures enhanced the energy-use-efficiency (EUE) and energy productivity (EP) by 43%–54% and 44%–61%, respectively than FP. In our study, global warming potential (GWP), GHG emission due to consumption energy and greenhouse gas intensity were recorded lower by 43%, 56% and 59% in Climate Smart Agriculture (CSA) with high adaptive measures than farmers practices (3652.7 kg CO₂ eq. ha⁻¹ yr⁻¹, 722.2 kg CO₂ eq. ha⁻¹ yr⁻¹ and 718.7 Mg kg⁻¹ CO₂ eq. ha⁻¹ yr⁻¹). The findings of the present study revealed that CSA with adaption of innovative measures (S6) improved 3-year mean system productivity by 10.5%, profitability by 29.4%, water productivity and energy productivity by 18.3% and 48.9%, respectively than FP. Thus, the results of our 3-year farmer’s participatory study suggest that in a RW system, climate smart agriculture practices have better adaptive capacity and could be a feasible option for attaining higher yields, farm profitability, energy-use efficiency and water productivity with sustained/improved environmental quality in smallholder production systems of Eastern IGP of India and other similar agro-ecologies of South Asia. Finally, the adoption of these CSAPs should be promoted in the RW rotation of IGP to ensure food security, restoration of soil health and to mitigate climate change, the key sustainable development goals (SDGs). Full article

Advances in remote sensing technologies have enabled effective drought monitoring globally, even in data-limited areas. However, the negative impact of drought on crop yields still necessitates stakeholders to make informed decisions according to its severity. This research proposes an algorithm to combine a drought monitoring model, based on rainfall, land surface temperature (LST), and normalized difference vegetation index/leaf area index (NDVI/LAI) satellite products, with a crop simulation model to assess drought impact on rice yields in Thailand. Typical crop simulation models can provide yield information, but the requirement for a complicated set of inputs prohibits their potential due to insufficient data. This work utilizes a rice crop simulation model called the Simulation Model for Use with Remote Sensing (SIMRIW–RS), whose inputs can mostly be satisfied by such satellite products. Based on experimental data collected during the 2018/19 crop seasons, this approach can successfully provide a drought monitoring function as well as effectively estimate the rice yield with mean absolute percentage error (MAPE) around 5%. In addition, we show that SIMRIW–RS can reasonably predict the rice yield when historical weather data is available. In effect, this research contributes a methodology to assess the drought impact on rice yields on a farm to regional scale, relevant to crop insurance and adaptation schemes to mitigate climate change. Full article