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Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

This study examines the impact of agricultural credit on rice farmers' technical efficiency (TE) in Côte d'Ivoire by considering the heterogeneity among credit sources.

A multistage sampling technique was used to collect data from 588 randomly sampled rice farmers in seven rice areas of the country. The authors use the endogenous stochastic frontier production (ESFP) model to account for the endogeneity of access to agricultural credit.

On the one hand, agricultural credit has a significant and positive impact on rice farmers' TE. Rice farmers receiving agricultural credit have an average of 5% increase in their TE, confirming the positive impact of agricultural credit on TE. On the other hand, the study provides evidence that the impact of credit on rice production efficiency differs depending on the source of credit. Borrowing from agricultural cooperatives and paddy rice buyers/processors positively and significantly influences the TE, while borrowing from microfinance institutions (MFIs) negatively and significantly influences the TE. Moreover, borrowing from relatives/friends does not significantly influence TE.

Future research can further explore the contribution of agricultural credit by including several agricultural productions and using panel data.

The study provides evidence that the impact of agricultural credit on agricultural production efficiency depends on the source of credit. This study contributes to the literature on the impact of agricultural credit and enlightens policymakers in the design of agricultural credit models in developing countries, particularly Côte d'Ivoire.

This paper aims to focus on scrutinizing the economics of greenhouse gas (GHG) emissions in Vietnam's rice production sector.

Using surveyed data from household rice producers, the smallest available production scale, the author delves into the economics of GHG emissions, constructs a data-driven bottom-up marginal abatement cost curve for Vietnam’s rice production, and evaluates the impacts of carbon pricing on production outputs and GHG emissions.

The author’s estimates reveal that the average profit earned per tonne of GHG emissions is $240/tCO2. Notably, the profit earning per tonne of GHG emissions varies substantially across producers, indicating significant opportunities for improvement among low-efficiency producers. The analysis suggests that a reasonable carbon price would yield a modest impact on the national rice output. The quantitative analysis also reaffirms that the primary driver of GHG emissions in Vietnam’s rice production stems from non-energy inputs and industrial processes rather than the utilisation of energy inputs, emphasizing the importance of improving cultivation techniques.

This research is original.

This paper examines the performance of smallholder rice farms established using improved planting technologies – broadcasting, dibbling and transplanting – under different production systems – rain-fed and irrigation – in Ghana.

Using recent cross-sectional data of 200 smallholder rice farmers from the upper east region of Ghana, this study employed multinomial logit model and descriptive and inferential statistics for the analysis.

The results revealed that rice production under irrigation system contributes significantly to increasing farm productivity and profitability. Rice farmers who adopted dibbling and transplanting technologies under both irrigation and rain-fed production system obtained higher productivity and profitability than those who used broadcasting technology. Adoption of improved rice planting technologies by smallholder farmers is significantly influenced by education, farm size, improved rice varieties, sales outlets, hired labour and percentage of paddy sold.

The sample size is relatively small, even though findings are still very important in terms of policy formulation for improved smallholder farm performance in a developing country like Ghana.

This study calls for collaborative efforts by government, donor agencies and NGOs to establish irrigation facilities and/or expand existing ones, increase sensitization and dissemination of improved planting technologies, as well as intensify the input subsidy programme in Ghana.

To the best of the authors knowledge, this is the first study that focuses on farmers' choice of rice planting technologies under irrigation and rain-fed production systems, and how these technologies impact on smallholder farm performance in Ghana.

The purpose of this paper is to analyse the effect of enhanced knowledge and technology innovations, which were resulted from training, on the simultaneous production of rice and soybean in Java, Indonesia.

A model of product possibility frontier with two outputs produced using the same resources was employed. Based on the model, supply responses of soybean and rice were derived. Aggregate data consisting of 12 regions during the ten years of 2000–2009 were compiled from relevant agricultural institutions at the provincial level.

Improvement in farmers’ capacity has been able to increase production of soybean and rice simultaneously. Farmers’ capacity increased after completion of the school. Knowledge and skill gained from the school have been applied to both rice and soybean farming. Other economic factors also affected the supply response of both commodities.

Available data covered periods 2000–2009. However, the outcomes are still relevant to the current situation because food crops are the basic necessity. This study used secondary aggregate data, which might be less accurate than primary data. However, secondary data have the advantage concerning coverage and time span.

The Government, in collaboration with non-government organisations and the private sectors, should continue to enhance farmers’ capacity to increase the production of food crops to feed people in Indonesia, and over the world in general.

An analysis of joint production using a concept of product transformation curve can measure the impact of training on multi products.

The paper investigates the effects of climate change along with ecological and carbon footprint on rice crop production in India during 1982–2016.

The autoregressive distributed lag (ARDL), canonical cointegration regression (CCR) and fully modified ordinary least square (FMOLS) models are used in the paper.

A long-run relationship is found between climate change and rice production in India. Results report that ecological footprint and carbon footprint spur long-term rice production. While rainfall boosts rice crop productivity in the short term, it has a negative long-term impact. Further, the findings of ARDL models are validated by other cointegration models, i.e., the FMOLS and CCR models.

This study provides insights into the role of ecological footprint and carbon footprint along with climate variables in relation to rice production.

In the literature, the effects of ecological and carbon footprint on rice production are missing. Therefore, this is the first study to empirically examine the impact of climate change along with ecological footprint and carbon footprint on rice production in India.

This 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.

Primary data were sourced from 225 rice farmers. Margalef index and three-stage least-squares were employed.

Majority 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.

Policy 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.

Crop 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.

The purpose of this paper is to investigate the productivity of rice production by decomposing the growth of total factor productivity (TFP) into four components: technological change, scale effects, technical and allocative efficiencies.

This study employed an econometric approach to decompose TFP growth into four components: technological change, technical efficiency, allocative efficiency and scale effect. Unbalanced panel data used in this study were surveyed in 1994, 2004 and 2014 from 360 rice farming operations. The model used the stochastic frontier transcendental logarithm production technology to estimate the technology parameters.

The results indicate that the primary sources of TFP growth were technological change and allocative efficiency effects. The contribution of technical efficiency was low because it grew sluggishly.

This study has several shortcomings, such as very low R² and the insignificant elasticity of labour presented in the findings. Another limitation is the limited time period panel covering long interval, which resulted in unbalanced data.

The government should improve productivity growth by allocating more areas for rice production, which enhances the scale and efficiency effects and adjusting the use of capital and material inputs. Extension services should be strengthened to provide farmers with training on improved agronomic technologies. This action will enhance technical efficiency performance and lead to technological progress.

As Indonesian population is still growing at a significant rate and the fact that rice is the primary staple food for Indonesian people, the productivity of rice production should increase continually to ensure social security at a national level.

The productivity growth is decomposed into four components using the transcendental logarithm production technology based on farm-level data. The measure has not been conducted previously in Indonesia, even in rice-producing countries.

The purpose of this paper is to establish correlations among the input variables of production within themselves and input variables of consumption within themselves and to forecast the production and consumption of the rice.

The production and consumption of rice crop is governed by diverse variables. In the present study five key input variables for production of rice based on literature review and the authenticated data available from agricultural sources have been selected. These variables are area sown, agricultural workers (AW), area irrigated, growth rate and yield per hectare. On similar basis four key input variables responsible for consumption of rice are considered, namely, price of rice, population, poverty ratio and per capita net national product (NNP).

Correlation analysis showed that priority wise production of rice depends upon yield per hectare, percentage irrigation, AW and area sown. The growth rate is found to be having insignificant correlation with other variables of production and hence was omitted from subsequent study. Correlation analysis also showed that priority wise consumption depends upon whole sale price per ton, population and the per capita NNP. The poverty ratio is found to be having insignificant correlation with other variables of consumption and hence was omitted from subsequent study. The outcomes of the correlation analysis are utilized for designing rule base for fuzzy inference system (FIS) to forecast the production and consumption of the rice. Subsequently Bayesian technique is used to forecast production and consumption and its results are compared with the results of fuzzy inference analysis.

There are many techniques used for forecasting purpose but FIS and Bayesian technique outperform others. In the present study, the authors therefore focussed on these two techniques. Bayesian technique takes into account the expert opinion at the current conditions whereas FIS uses previously designed rule base. Besides discussing the appropriateness of these two techniques for forecasting production and consumption of rice, their forecasting outcomes will help in logistical and operational planning of the resources at national level, farmers’ level and traders’ level.

The purpose of this paper is to study the production and efficiency of rice growers in drought prone areas with special attention given to economic and financial factors.

The authors use a parametric stochastic frontier approach and a non-parametric data envelopment analysis.

The study found that financial and liquidity constraints negatively influence production efficiency while off-farm work positively influences efficiency in drought prone areas.

Many biotic and abiotic factors affect the production efficiency of rice growers. Among abiotic stress, drought is the strongest constraint affecting nearly one third of the total rice area in Asia and causing significant economic losses. Farmers’ economic conditions and financial constraints further exacerbate the situation. However, very few studies have analyzed the efficiency in drought prone areas and the influence of economic and financial factors. This study contributes to in this regard by augmenting economic and financial factors in the efficiency estimation of drought prone areas using parametric and non-parametric approach.