Suchergebnisse

Crop foraging or crop raiding concerns wildlife foraging and farmers' reactions and responses to it. To understand crop foraging and its value to wildlife or its implications for humans requires a cross-disciplinary approach that considers the behavior and ecology of wild animals engaging in this behavior; the types and levels of competition for resources between people and wildlife; people's perceptions of and attitudes toward wildlife, including animals that forage on crops; and discourse about animals and their behaviors and how these discourses can be used for expressing dissent and distress about other social conflicts. So, to understand and respond to conflicts about crop damage, we need to look beyond what people lose, i.e., crop loss and economic equivalence, and focus more on what people say about wildlife and why they say it.

At present, losses to the millet crop of Sahelian subsistence farmers are seldom adequately monitored, yet an assessment of such losses is essential in evaluating the effects of and need for different farming inputs and methods. Millet Crop-Loss Assessment Methods offers a range of assessment techniques, each presented as a sequence of steps, including sampling, calculation interpretation and comparative accuracy. Choice of the most appropriate method will depend on government or farmer needs, time constraints and available skills. This publication will be of interest to all those involved in practical agricultural research and extension work in semi-arid areas, either at the level of the individual farmer or village or at the regional and national level of policy evaluation.

Agrarian distress in rainfed areas refers to the challenges faced by farmers who rely on rainfall for their agricultural activities. These areas are particularly vulnerable to climate change and variability, which can result in droughts, floods, and other extreme weather events that impact crop yields and livelihoods. One of the primary reasons for agrarian distress in rainfed areas is the lack of irrigation facilities. These areas rely on rainfall for their agricultural activities, and a lack of adequate rainfall can result in crop failure and financial losses for farmers. In addition, soil degradation and erosion, which can be caused by deforestation and unsustainable agricultural practices, can further reduce the productivity of rainfed areas. Furthermore, farmers in rainfed areas often face challenges in accessing credit and markets. They may also lack knowledge and resources and assets to adopt sustainable agricultural practices and diversify their income streams. This can result in a cycle of poverty and indebtedness, which leaves farmers struggling to make ends meet. Addressing agrarian distress in rainfed areas requires a comprehensive approach that includes improving irrigation facilities, promoting sustainable agricultural practices, providing access to credit and markets, and strengthening government support for farmers. This can help to improve agricultural productivity, enhance resilience to climate change, and promote sustainable livelihoods for farmers in rainfed areas. Government of India implementing crop insurance scheme to compensate for crop losses since last five decades in one form or other.

Loss to cultivated crops by wild pigs (Sus scrofa) is widespread and can jeopardize low-income farmers. In India, although there is lot of political interest in the problem, efforts to understand the patterns, correlates, and underlying reasons for wild pig conflict continue to be minimal. We quantified loss of wheat (Triticum aestivum) to wild pigs and assessed the spatial patterns of damage in a forest settlement of Van Gujjar (Haridwar, India), which is a dairy-based pastoralist community. We chose a 4-km2 cultivated area comprising 400 farmlands (each measuring 0.8 ha and belonging to a family) and assessed crop damage by wild pigs through field surveys during the harvest season. We interviewed 159 respondents who manage 219 of the total 400 farmlands in the study area to compare actual crop loss with perceived losses. Wild pigs damaged 2.29 tonnes (2,290 kg) of wheat, which was about 2.6% of the potential yield in the study area. A total of 39 farmlands (9.5%), managed by 28 respondents, suffered losses during the survey period at an average loss of about 58.8 kg (SD ± 89.5, range = 0.7–388 kg). During interviews, 81 respondents managing 155 farmlands (70.7%) reported having suffered wild pig-related crop loss during the survey period. They also perceived losing about 23.4% of the potential yield of wheat due to wild pigs. The perceived losses were much higher than actual losses. Actual losses measured through field surveys underscore the dichotomy between actual and perceived crop loss due to wild pigs. About 81% of recorded wild pig-related damage to wheat occurred within 200 m from the forest edge. The crop protection measures aimed at stopping wild pigs from entering the fields were mostly reactive. Although overall crop losses due to wild pigs seem low at the settlement level, for affected individual families, the losses were financially significant. Such recurrent crop losses can cause families to go into debt, trigger animosity toward conservation, and lead to retaliation measures, which may be indiscriminate and have the potential to affect other endangered mammals in conservation priority landscapes. Because crop losses by wild pigs are severe along the narrow band of fields along the edge of the forest, channeling monetary benefits through insurance-based compensation schemes can help assuage losses to farmers. Further, because crop damage by wild pigs is seasonal, experimenting with mobile fences that can be dismantled and packed away after use would be beneficial.

AbstractThis study provides a novel empirical framework for estimating the effects of temperature on the production of several tree crops in California by using underused insurance data and a variable selection technique. We utilize a Bayesian variable selection technique to select relevant temperature variables. We then use the selected temperature variables to assess the temperature effects on crop losses. We find that a greater length to freeze exposure increases crop losses and relatively smaller roles of chill hours and heating hours. We also find the heterogeneity of the effects across insurance coverage levels.

Identifying crop loss at field parcel scale using satellite images is challenging: first, crop loss is caused by many factors during the growing season; second, reliable reference data about crop loss are lacking; third, there are many ways to define crop loss. This study investigates the feasibility of using satellite images to train machine learning (ML) models to classify agricultural field parcels into those with and without crop loss. The reference data for this study was provided by Finnish Food Authority (FFA) containing crop loss information of approximately 1.4 million field parcels in Finland covering about 3.5 million ha from 2000 to 2015. This reference data was combined with Normalised Difference Vegetation Index (NDVI) derived from Landsat 7 images, in which more than 80% of the possible data are missing. Despite the hard problem with extremely noisy data, among the four ML models we tested, random forest (with mean imputation and missing value indicators) achieved the average AUC (area under the ROC curve) of 0.688±0.059 over all 16 years with the range [0.602, 0.795] in identifying new crop-loss fields based on reference fields of the same year. To our knowledge, this is one of the first large scale benchmark study of using machine learning for crop loss classification at field parcel scale. The classification setting and trained models have numerous potential applications, for example, allowing government agencies or insurance companies to verify crop-loss claims by farmers and realise efficient agricultural monitoring.

Other written product issued by the General Accounting Office with an abstract that begins "Pursuant to a legislative requirement, GAO reviewed the Commodity Credit Corporation's (CCC) new rule on the 1998 Single-Year and Multi-Year Crop Loss Disaster Assistance Program. GAO noted that: (1) the rule sets forth the terms and conditions of the 1998 Single-Year and Multi-Year Crop Loss Disaster Assistance Program; (2) the purpose of the program is to provide payments to eligible producers who suffered losses because of an eligible disaster in crop year 1998, or in at least three of the crop years from 1994 through 1998; and (3) CCC complied with the applicable requirements in promulgating the rule."