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Flood risk to rice production has previously been examined in terms of river basins or administrative units, incorporating data about the flood year, inundated area, precipitation, elevation, and impacts. However, there is limited knowledge about this topic, as most flood impact studies have focused on loss and damage to people and the economy. It remains important to identify how flood risk to rice production can be better identified within a long-term, community-based, analytical framework. In addition, flood risk studies in Sri Lanka tend to focus on single-year flood events within an administrative boundary, making it difficult to fully comprehend risks to rice production. This paper aims to fill these gaps by investigating long-term flood risk levels on rice production. With this aim, we collected and analyzed information about rice production, geospatial data, and 15-year precipitation records. Temporal-spatial maps were generated using Google Earth Engine JavaScript coding, Google Earth Pro, and OpenStreetMap. In addition, focus group discussions with farmers and key informant interviews were conducted to verify the accuracy of online information. The collected data were analyzed using descriptive statistics, GIS, and linear regression analysis methods. Regarding rice production impacts, we found that floods in the years 2006–2007, 2010–2011, and 2014–2015 had significant impacts on rice production with 20.5%, 75.8%, and 16.6% reductions, respectively. Flood risk maps identified low-, medium-, and high-risk areas based on 15-year flood events, elevation, proximity to water bodies, and 15-year flood-induced damage to rice fields. High risk areas were further studied through field discussions and interviews, showing the connection between past floods and poor water governance practices in terms of dam management. Our linear regression analysis found a marginal negative correlation between total seasonal rainfall and rice production.

The present study aims to measure the technical efficiency and establish core factors affecting rice production in Cambodia. A four-year dataset generated from the central government document 'Profile on Economics and Social' of 25 entire provinces between 2012 and 2015 and the stochastic production frontier model (SFA) was applied. The results indicated that the level of output (quantity) of Cambodian rice production varied according to the different level of capital investment in agricultural machineries, total rice actual harvested area, and technical fertilizer application within provinces. Furthermore, evidence revealed that the overall mean efficiency of rice production is 78.4%, which implies that there is still room to further improve technical efficiency given the same level of inputs and technology. More importantly, the findings revealed that irrigation, production techniques and amount of agricultural supporting staff are the most important influencing factors of rice production's technical efficiency in Cambodia. In conclusion, the present study strongly recommends the development of irrigation systems and good water management practices to be considered and bring about more effective actions by the central government as well as related agencies for improving rice production in Cambodia in addition to capital investment and improving technical skills of supporting staff and rural farmers.

The objective of this study was to investigate the cost and return structures of rice cultivation in two farming systems, organic and conventional rice production, in Chachoengsao Province, Thailand, in the 2016/2017 crop year. The sample included 108 farmers selected using a purposive sampling method, consisting of 58 organic rice growers and 50 conventional rice growers. Survey questionnaires were used as the main instrument for data collection. Descriptive statistics and cost and return analysis were used for data analysis. The results indicate that the cost of production was higher, and output was lower under organic rice farming. Despite this, organic rice farming was more profitable than conventional farming, which was attributed to the higher prevailing market price for organic rice. This indicates the importance of price differences across market segments within the distribution channel and the profitability of rice production between the two rice farming systems.

Rice farmers in Vietnam face many difficulties achieving technical efficiency (TE), which can be measured by the distance to the production frontier, in rice production due to non-optimal combinations of inputs and the influence of household socioeconomic characteristics. This study investigates the TE of rice production by applying stochastic frontier analysis to raw data obtained from the Vietnamese Households Living Standards Survey 2016 (VHLSS 2016) database. In addition, organic fertilizers now demand much attention worldwide because of their environmentally friendly characteristics. Therefore, this study identifies the effects of organic fertilizer choices on the TE of rice production. The results show that farmers in Vietnam achieved 87.6 percent TE and that most factors tested had significant effects on rice production. Instead of rice monoculture, the four main factors with strong and positive effects on TE levels were intensive labor, irrigation, mixing crops instead of rice monoculture, and education. Moreover, this study also revealed that organic fertilizer plays a vital role in growing rice by applying propensity score matching (PSM) between farmers who use or do not use irrigation facilities in rice production. While in the process of building a system, the government should focus on rice producers to strive for maximum efficiency with regard to labor productivity and mixed-crop farming, and to take proper measures to improve rice productivity and quality through the use of organic fertilizers. As a result, this study revealed that the use of organic fertilizers for rice production in Vietnam does not always benefit households’ TE.

PurposeThis study investigates implications of crop and income diversifications on consumption expenditure (welfare) of rice-producing households in Ghana. It further compares diversification by three rice production systems: two-season rain-fed, two-season irrigated and one-season rain-fed rice production.Design/methodology/approachPrimary data were sourced from 225 rice farmers. Margalef index and three-stage least-squares were employed.FindingsMajority of rice-farming households in Ghana diversify livelihoods. The extent of livelihood diversification differs among two-season rain-fed, two-season irrigated and one-season rain-fed rice-producing households. Credit, distance to district capitals, production purpose and number of farming seasons influence crop and income diversifications, and consumption expenditure of rice-producing households. While crop diversification reduces consumption expenditure, income diversification increases it. Crop and income diversifications positively influence each other. Consumption expenditure reduces crop diversification but increases income diversification.Practical implicationsPolicy should be directed towards the promotion of more livelihood activities to boost rice farmers' welfare. There should be awareness creation and training programmes to enable rice farmers realize different economic activities within and outside the agricultural value chain.Originality/valueCrop and income diversifications were measured as continuous response variables, unlike previous studies that used a binary response variable. The authors established a synergy among crop and income diversifications, and consumption expenditure (welfare). The authors further compared crop and income diversifications by three rice production systems: two-season rain-fed, two-season irrigated and one-season rain-fed rice production systems.

Rice is food consumed regularly and is vital for the food security of over half the world’s population. Rice production on a global scale is predicted to rise by 58 to 567 million tonnes (Mt) by 2030. Rice contains a significant number of calories and a wide variety of essential vitamins, minerals, and other nutritional values. Its nutrients are superior to those found in maize, wheat, and potatoes. It is also recognised as a great source of vitamin E and B5 as well as carbohydrates, thiamine, calcium, folate, and iron. Phytic acid and phenols are among the phenolic compounds found in rice, alongside sterols, flavonoids, terpenoids, anthocyanins, tocopherols, tocotrienols, and oryzanol. These compounds have been positively linked to antioxidant properties and have been shown to help prevent cardiovascular disease and diabetes. This review examines recent global rice production, selected varieties, consumption, ending stocks, and the composition of rice grains and their nutritional values. This review also includes a new method of paddy storage, drying, and grading of rice. Finally, the environmental impacts concerning rice cultivation are discussed, along with the obstacles that must be overcome and the current policy directions of rice-producing countries.

The objective of this paper is to develop a simple method for analyzing the parameter uncertainty of the Japanese life cycle inventory database (LCI DB), termed the inventory database for environmental analysis (IDEA). The IDEA has a weakness of poor data quality because over 60% of datasets in IDEA were compiled based on secondary data (non-site-specific data sources). Three different approaches were used to estimate the uncertainty of the brown rice production dataset, including the stochastic modeling approach, the semi-quantitative DQI (Data Quality Indicator) approach, and a modification of the semi-quantitative DQI approach (including two alternative approaches for modification). The stochastic modeling approach provided the best estimate of the true mean of the sample space and its results were used as the reference for comparison with the other approaches. A simple method for the parameter uncertainty analysis of the agriculture industry DB was proposed by modifying the beta distribution parameters (endpoint range, shape parameter) in the semi-quantitative DQI approach using the results from the stochastic modeling approach. The effect of changing the beta distribution parameters in the semi-quantitative DQI approach indicated that the proposed method is an efficient method for the quantitative parameter uncertainty analysis of the brown rice production dataset in the IDEA.

Rice is one of the most important staple foods for 70 percent of the population of the world. It is among the main cereal crops grown in different regions of Pakistan as food crop. Pakistan has very much potentials for growing the crop and the potential rice production sown area is estimated to be about 2724 thousand hectares. The purpose of this study is to examine the linkage between fertilizer consumption and rice production in Pakistan from 1984 to 2014. For checking the stationarity of the data, this study incorporated Augmented Dickey Fuller (ADF) and Phillips Perron (PP) unit root tests. Furthermore, the Johenson Co-integration test is used to detect the long-term relationship among the series. Likewise, on the basis of annual time series data the Autoregressive Distributed Lag (ARDL) model is employed to evaluate the impact of fertilizer consumption on the production of rice in Pakistan up to now. The results of ADF and PP unit root tests reveal that fertilizer consumption and water availability are integrated at I(0) whereas area and rice production are integrated at I(1).The empirical findings of Autoregressive Distributed Lag (ARDL) model indicate that area and fertilizer consumption for rice has a significant effect on the rice production in both short-run and long-run. In contrast, water availability has a significant effect on the rice production in the long-run but it was statistically insignificant in the short-run. The estimated equation remains stable from the period of 1984 to 2014 as showed by stability tests.

Rainfall pattern and temperature variation are changing due to global warming, affecting crop production. The purpose of this study is to investigate the impact of temperature and rainfall on rice production during the rice growth phases over the Punjab province, Pakistan, for the period of 1981–2017. The impact of climate on rice production is analyzed using the statistical techniques. Results show that maximum temperature negatively affects rice plant resulting in decrease in the number of plants at replantation stage. The positive impact of minimum temperature on rice production is also observed that may cause growth of plant, which affects the rice crop at replantation stage during vegetative phase. Results revealed that number of tillers and rice plant diet increase with the positive impact of rainfall at tillering stage. Maximum temperature has negative impact on rice crop at tillering and stem elongation stages. It is observed that rainfall has negative impact on rice plant at heading and flowering stages. Substantial reduction in rice production is observed due to damage of reproductive cells at heading and flowering stages during the reproductive phase. The negative effect of rainfall on rice production is also observed at milking stage during the ripening phase. The negative significant impact of rainfall is observed on gross domestic product per capita during the reproductive phase. This study also predicts that temperatures in the Punjab will rise in the near future; therefore, it is recommended that breeders should introduce rice varieties that consume less water and are more productive in high temperature.

In recent years, environmental change has arisen as a ubiquitous problem and gained environmentalist’s attention across the globe due to its long-term harmful effects on agricultural production, food supply, water supply, and livelihoods of rural households. The present study aims to explore the asymmetrical dynamic relationship between climate change and rice production with other explanatory variables. Based on the time series data of India, covering the period 1991–2018, the current study applied the nonlinear autoregressive distributed lag (NARDL) model and Granger causality approach. The results of the NARDL reveal that mean temperature negatively affects rice production in the long run while positively affecting it in the short run. Furthermore, positive shocks in rainfall and carbon emission have negative and significant impacts on rice production in the long and short run. In comparison, negative rainfall shocks significantly affect rice production in the long and short run. Wald test confirms the asymmetrical relationship between climate change and rice production. The Granger causality test shows feedback effect among mean temperature, decreasing rainfall, increasing carbon emission, and rice production. While no causal relationship between increasing temperature and decreasing carbon emission. Based on the empirical investigations, some critical policy implications emerged. Toward sustainable rice production in India, there is a need to improve irrigation infrastructure through increasing public investment and to develop climate-resilient seeds varieties to cope with climate change. Along with, at the district level government should provide proper training to farmers regarding the usage of pesticides, the proper amount of fertilizers, and irrigation systems.