Climate Change and Variability: Producers\u27 Perceptions and Determinants of Adaptation Strategies in Rice Farming in Chitwan, Nepal

Nepalese agriculture is highly vulnerable to climate change. Rice is the most important cereal crop (as a staple food) in terms of area cultivated each year in Nepal. This study analyzes rice producers’ perceptions of climate change, its impacts on rice farming, and factors influencing the adoption of adaptation strategies of the producers. The analysis of survey data of 359 rice-producing households in the South-Central Terai region of Nepal indicated that rice producers perceived changes in local weather patterns as compared to the past year. Trend analysis (1981-2018) of weather data (temperature and rainfall) from a local meteorological station strongly supports producers’ perceptions. Results revealed that producers are adopting a number of adaptation strategies to reduce the impacts of climate change in rice farming, they were adjustment in crop calendar (72.9%), varietal selection (64.9%), investment in improved irrigation (60.7%), adoption of integrated pest management (22.1%), and adoption of direct-seeded rice (15.6%). Results indicated that 76.6% of producers were adopting at least one adaptation strategy, and 10% of producers were adopting all those five strategies. Lack of adequate information, limited technical know-how, and credit constraints are major factors that impede adaptation to climate change. Results indicates that the average rice yield of adopters was 227 Kilogram/Hectare higher than that of non-adapters. A multivariate probit model (MVP) and an ordered probit model (OPM) were used to examine factors influencing the rate of adoption of adaptation strategies and to estimate drivers of the intensity of adoption, respectively. Results of a multivariate probit model revealed complementarities among adaptation strategies indicating their adoption is inter-related. This result has important policy implications that enhancement of one strategy can have spillover effects on other strategies. Results revealed that human capital formation through education, literacy programs, formal and informal training help in making a better farming decision, increase awareness about the impacts of climate change, and can further enhance adoption of adaptation strategies on rice farming. Results of the intensity of adoption indicated that male headed household, education, awareness about climate change, use of Extension, training, and information are significant to improve intensity of adoption

Three Essays Evaluating Long-Term Agricultural Projections

This dissertation consists of three essays that evaluate long-term agricultural projections. The first essay focus on evaluating Congressional Budget Office’s (CBO) baseline projection of United States Department of Agriculture (USDA) mandatory farm and nutrition programs. The second essay examine USDA soybean ending stock projections, and the third essay investigate impact of macroeconomic assumptions on USDA’s baseline farm income projections. The three essays are summarized as follows: Essay1: AnEvaluation of Congressional Budget Office’s Baseline Projections of USDA Mandatory Farm and Nutrition Programs.The CBO projections of USDA’s mandatory farm and nutrition program outlays play a vital role in shaping agricultural policy and in agricultural policy debates. Using CBO projections and observed outcomes from 1985 through 2020, we examine the degree to which projections of farm, supplemental nutrition assistance, and child nutrition program outlays are unbiased, efficient, and informative. We find that projections for farm program and child nutrition program outlays are unbiased. Supplemental nutrition assistance program outlays are unbiased at short horizons but are downward bias beyond a three-year horizon. We find that all three series of projections are inefficient. The projections for supplemental nutrition assistance program and child nutrition program outlays are informative up to a five-year projection horizon, but the farm program outlay projections are informative for only a one-year horizon. Disaggregated farm program outlay projections since 2008 further suggest that the uninformativeness principally stems from conservation program projections. The findings may provide valuable insights for CBO to improve future projections and for projection users, including policymakers, to adjust expectations and future Farm Bill discussions. Essay 2: Evaluation of USDA’s soybean ending stock baseline projection.The carryover inventory of a specific commodity – ending stock, which summarizes a commodity market’s supply and demand components – is an important measure of level of scarcity in a market. Using USDA baseline projections and realized values from 1997 through 2020, we examine bias and informativeness of soybean ending stock projection. We decompose soybean ending stock projection errors using a machine learning algorithm, Extreme Gradient Boosting (XGBoost). Our results indicate that soybean ending stock projections are unbiased (except for nowcast), however ending stock projections are informative for only one-year horizon. The decomposition of ending stock projection indicates that demand components (crushing, seed and residual, and exports) are primary sources of ending stock projection error for a majority of projection horizons. US soybean market is directly linked with global soybean import and exports, thus, we further investigate USDA projection of soybean export and import country-by-country. Results indicate that USDA’s foreign soybean import projections are barely informative for most of countries including China whereas export projection from Argentina, Brazil, and Other South American countries are informative for four years, two years, and not informative at all, respectively, under our conservative estimates. Finally, an analysis of US soybean export projection error to foreign export/import destinations indicates that errors on export projection from Brazil and other South American Countries (except Argentina) cost USDA the most. Results may help market participants form expectation when making plan and business decisions

Demand-driven governance

Last ned gratis This Working Paper reviews demand-driven governance as a concept, as  an element of international donor policy, and as a set of principles which have characterised aid and development interventions in certain post-conflict contexts. Particular attention is being paid to the case of Nepal, and to the interplay between scales of governance, community-state relations and donor coordination if this model is to succeed in facilitating sustainable development in a post-conflict context. Tilknyttet prosjekt Demand driven governance and community interventio

Maternal satisfaction regarding perinatal care and influencing factors in tertiary hospitals of western, Nepal

Satisfaction has been widely recognized as an important indicator to evaluate health care quality and to provide patient centered care. The study was carried out to assess satisfaction level from a hospital based perinatal care. A descriptive cross sectional study was carried out among 428 postnatal women in two tertiary hospitals of western Nepal via structured interview schedule. Maternal satisfaction was measured by 25 item scale. Overall 45.1% of mothers were satisfied with the perinatal care. The level of satisfaction in public hospital (mean 3.44±0.65 out of 5) was greater as compared to private hospital (mean 3.27±0.59 out of 5). The satisfaction score was lower in the physical environment (mean 3.01±0.87 out of 5) and highest in privacy maintained (mean 4.37±0.92 out of 5). Determinants of satisfaction were type of hospital, religion, education, parity, number of living children, mode of delivery, gestational age at birth, maternal condition after delivery, newborn health condition, and duration of stay at the hospital and the gender of the provider (p [Med-Science 2017; 6(3.000): 471-5