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Efforts to reduce farmers' market risks and improve buyers' access to farm commodities have encouraged contract farming (CF) in Ghana's cashew sector in recent years. Consequently, the existence of CF shows that farmers who use it may be benefiting from it, as it is their economic responsibility to decide how to sell agricultural products. However, the magnitudes of these benefits or otherwise have been inadequately explored. This paper aims to empirically estimate the impact of CF on farm performance and welfare of smallholder cashew farmers.

The study used probit-two-stage least square (probit-2sls) as a primary estimator to account for self-selection bias and endogeneity that could arise from both observed and unobserved heterogeneities among farming households to estimate the causal effects of CF on farm performance and household welfare.

The results indicated that participation in CF contribute significantly to the gains in farm performance (price margins, yields and net farm revenue) and welfare (consumption expenditure per capita), and that the non-participants of CF would have benefited substantially if they had participated. An analysis of the farm size disaggregated into small, medium and large with regards to the outcome variables produces mixed results.

It can be concluded that participating in CF enhances farm performance and household welfare.

While many other studies do not account for changes in farm performance and welfare due to differences in farm size or other observed factors, this study fills a crucial void.

After a long observation of the high rate of rice importation and low productivity in Côte d’Ivoire, the certified rice seed was introduced and encouraged to be used by the local farmers. This study evaluates the profitability of rice production and the impact of certified seed usage on the yield and income of farmers in Côte d’Ivoire.

Data were collected from 265 rice farmers. Descriptive statistics were used to identify the challenges faced in using certified seeds. Profitability analysis was used to examine the profitability of rice production. To eliminate bias due to the counterfactuals, the endogenous switching regression was employed to investigate the impact of the certified seeds on income and yield.

The difficulties faced by the rice farmers in the procurement of certified seeds were the unavailability of seeds, the high cost of seeds and poor credit access. Furthermore, rice farmers using certified seeds get a higher net income (USD 263.74/ha) than those using farmers' seeds (USD 212.31/ha). The average treatment on the treated was 1.61 for the yield and 574.75 for the income. The average treatment on the untreated was 1.20 for the yield and 422.59 for the income. These indicate a higher yield and income among adopters of certified rice seed.

Certified rice seed usage is profitable and enhances the output and income of rice farmers. The study advocates the creation of a stronger relationship between the farmers and the extension agents to encourage the use of certified seeds and increase the profit of the farmers.

There is scant information on the profitability of certified rice seed usage and how it affect yield and income. Therefore, this study serves as empirical evidence for policymakers to develop strategies that are required to enhance certified seed usage, boost rice productivity and achieve food security.

This study aims to investigate the landscape of family-based organic farm businesses in the Kashmir Valley, India, analyzing the factors that either facilitate or hinder their adoption. The research also intends to uncover sources of information seeking. The primary purpose is to provide qualitative evidence to address existing knowledge gaps and offer insights for promoting sustainable farm practices in the region.

The research employs a qualitative approach, drawing on focus group interviews. The study thoroughly explores the background and relevant literature, utilizing a comprehensive research framework. Data is collected from family based farmers engaged in organic farming practices in the Kashmir Valley. The data is analyzed using content analysis ensuring a robust and thorough exploration of the subject matter.

This study reveals a notable transition in the agricultural landscape of the Kashmir Valley, showcasing a widespread adoption of organic farming on considerable land. The study reveals that key facilitators for organic farming among family-based farms are farm productivity, entrepreneurial intention, governance, environmental consciousness, and health concerns. The exchange of information, both through formal and informal channels, is found to be a crucial factor influencing the adoption of organic farming. The study also unveiled significant inhibitors that hinder the adoption of organic farming on commercial scales, including on-farm challenges such as difficulties in acquiring inputs and facing reduced yields, market-related concerns, and a lack of support and assistance from government agencies.

This research contributes significantly to the existing literature by advancing the understanding of organic farm business and agri-entrepreneurship. It unveils key factors that either support or hinder family-based organic farms, identifying crucial information sources and presenting valuable insights for policymakers. Furthermore, this study provides practical guidance for overcoming obstacles, enhancing infrastructure, and translating identified facilitators into successful agri-ventures in the Kashmir region.

This study aims to discuss climate change, by modifying the timing of several agricultural operations, reduce the efficiency and yield of inputs leading to a lower production level. The reduction of the effects of climate change on production yields and on farmers' technical efficiency (TE) requires the adoption of adaptation strategies. This paper analyses the impact of climate change adaptation strategies adopted on maize farmers' TE in Benin.

This paper uses an endogeneity-corrected stochastic production frontier approach based on data randomly collected from 354 farmers located in three different agro-ecological zones of Benin.

Estimation results revealed that the adoption of adaptation strategies improve maize farmers' TE by 1.28%. Therefore, polices to improve farmers' access to climate change adaptation strategies are necessarily for the improvement of farmers' TE and yield.

The results of this study contribute to the policy debate on the enhancement of food security by increasing farmers' TE through easy access to climate change adaptation strategies. The improvement of farmers' TE will in turn improve the livelihoods of the communities and therefore contribute to the achievement of Sustainable Development Goals 1, 2 and 13.

This study contributes to theoretical and empirical debate on the relationship between adaptation to climate change and farmers' TE. It also adapts a new methodology (endogeneity-corrected stochastic production frontier approach) to correct the endogeneity problem due to the farmers' adaptation decision.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

Rural tourism facilities in Poland were very keen on amateur websites to promote their hospitality services from 2000 to 2018. In most cases, the websites were nonprofessional, hosted on free servers and made by family members or friends of the holding. After search engine algorithms changed in 2015–2019, the websites started to go extinct on a large scale; they were deleted and often replaced with a more modern design and a commercial domain. These resources offered a rare opportunity to gain insight into rural tourism, rural changes and socioeconomic and cultural phenomena.

The paper’s objective is to demonstrate with an analysis of archived Polish rural tourism websites that digital cultural artefacts are generated in rural areas. The study was an analysis of selected development attributes of rural tourism websites found in the Internet Archive. The analysis involved those attributes that are important for determining whether a website or content can be considered digital cultural heritage assets.

The conclusions demonstrate that rural digital cultural heritage is a set of digital artefacts created in rural areas with their characteristics. Rural digital artefacts are records of ICT, infrastructure, environmental, cultural and socioeconomic changes.

The “digital assets” of rural areas are yet to be discussed in the context of rural cultural heritage, as a set of artefacts created in these areas and characteristic of them.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.

This paper examines the determinants of concurrent adoption of farm risk management strategies by rice growers in two different ecosystems of Telangana agriculture-irrigated and rainfed ecosystems.

The primary data have been collected from the rice growers in two different ecosystems, and after checking the variance inflation factor (VIF) for controlling multicollinearity, a multinomial logit model has been used to examine the determinants of concurrent adoption of coping strategies by rice growers.

The study finds that adopting one risk management strategy persuades farmers to embrace other strategies, reducing the risk in agriculture between the two ecosystems. Among the determinants, farmers' age, education, contact with extension services, irrigation sources, livestock income, total farm income, crop loss reasons, and crop insurance awareness significantly influence the adoption of various risk management measures. However, considerable heterogeneity is found among the driving forces across the rice ecosystems.

The major policy implications that can be drawn from the analysis are increased access to information through government-funded extension services and the provision of alternative risk management technologies, such as drought-resistant or flood-resistant seeds, farmers' field schools and increased provision of crop insurance, farmer-friendly agriculture extension services, and farm investment support, are critical for assisting farmers managing risks. In addition, however, there should be ecosystem-specific policies to tackle the ecosystem heterogeneity.

This paper is very timely and entails some relevant policy implications for the development of Indian agriculture.

Women’s empowerment remains a key development challenge in Kenya. The purpose of this study is to attempt to understand the status of women’s empowerment and the key contributors to their disempowerment in Kenya’s aquaculture sector.

A cross-sectional survey was conducted on 534 male and female fish farmers from 300 households drawn from six counties in Kenya (Kakamega, Kisumu, Kisii, Kiambu, Meru and Nyeri). The Abbreviated Women’s Empowerment in Agriculture Index (A-WEAI) was adapted to Abbreviated Women’s Empowerment in Fisheries and Aquaculture Index (A-WEFI) to suit the aquaculture and fisheries sub-sector. The adapted A-WEFI was then used to estimate and the status of women’s and men’s using five domains of empowerment (5DE) and a gender parity index (GPI). Data were analysed using descriptive statistics, Cramer’s V and sensitivity analysis as test statistics.

About 86% of the men and 80% of the women were classified as empowered. The mean score of the 5DE was 0.93 and 0.95 for women and men, respectively. In addition, 82% of the households achieved gender parity, suggesting that for such households, empowerment of men was no greater than that of women. Overall, the results suggest no major differences between the empowerment of women and men. Findings suggest areas of improvement in empowerment: when observed separately, women report lack of agency in production, resource, time-use and allocation and leadership.

This paper adapts the A-WEAI to the fisheries and aquaculture context, in bid to bridge the gap in standard women’s empowerment measurement methods in this area. Also, there are limited empirical studies on the multifaceted empowerment of women in aquaculture in Kenya. The findings are meant to serve as a point of reference for policymakers, as they develop gender-responsive intervention programmes, and in implementing gender mainstreaming in Kenya.

Unstructured data such as images have defied usage in property valuation for a long time. Instead, structured data in tabular format are commonly employed to estimate property prices. This study attempts to quantify the shape of land lots and uses the resultant output as an input variable for subsequent land valuation models.

Imagery data containing land lot shapes are fed into a convolutional neural network, and the shape of land lots is classified into two categories, regular and irregular-shaped. Then, the intermediate output (regularity score) is utilized in four downstream models to estimate land prices: random forest, gradient boosting, support vector machine and regression models.

Quantification of the land lot shapes and their exploitation in valuation led to an improvement in the predictive accuracy for all subsequent models.

The study findings are expected to promote the adoption of elusive price determinants such as the shape of a land lot, appearance of a house and the landscape of a neighborhood in property appraisal practices.