Search results

This study aims to estimate the synergy effect of agricultural dual-scale management (ADM) on farmers' total household income, its heterogeneous effects and its mechanisms.

This study constructs a theoretical analysis framework based on the division of labor and synergy theory, empirically assesses the impact of ADM on farmers' income, and further discusses the heterogeneity and mechanisms using the propensity score matching (PSM) and quantile treatment effect (QTE) models. Data is collected from 1,076 households across 4 cities in Liaoning Province of China in 2021.

ADM can improve the total household income of farmers, and the impact force is greater than that of the single-scale management mode. ADM is more conducive to improving the income of farmers with low income and low labor endowment. Moreover, ADM can improve agriculture production efficiency, increase net grain production income. Nevertheless, it has no significant effect on farmers' off-farm employment income.

Previous studies have mainly focused on the income effect of land scale management or service scale management. To the best of our knowledge, this study is the first to identify the synergy effect of ADM on farmers' income in China. It provides new insights into the process of agricultural production and management mode transitions in rural China.

The agricultural sector has experienced a transformative impact through the adoption of digital technologies, particularly mobile applications designed for farmers. This study investigates the factors influencing smallholder farmers' willingness to adopt digital application tools in Ogun State, Nigeria, focusing on the IITA herbicide calculator and Akilimo mobile applications.

Data were gathered from 572 smallholder farmers participating in the Zero Hunger project. This research contributes to the limited empirical evidence in Nigeria concerning farmers' willingness to adopt digital application tools. The study analyzes the effects of education, training, access to internet services, smartphone ownership, willingness to use paid applications, awareness of application tools and the cost of digital tools on farmers' willingness to adopt. Gender differentials in willingness to adopt were also explored.

The results indicate positive and statistically significant effects of education, training, internet access, smartphone ownership, willingness to use paid applications, awareness of application tools and the cost of digital tools on farmers' willingness to adopt. However, female farmers exhibited a lower willingness to adopt digital application tools.

Policymakers are urged to create supportive policies promoting basic formal education and provide effective extension services to enhance farmers' training. Additionally, efforts should be made to reduce the cost of digital applications and improve internet accessibility in rural areas. Encouraging female farmers to adopt advanced agricultural technologies is essential. Stakeholders are advised to raise awareness of digital application tools to expedite the adoption of agricultural technologies in the country.

This study will be helpful for the government to determine the state’s readiness for digital agriculture, it will help technology developers and agricultural technology startups to understand the factors determining farmers willingness to adopt digital application tools.

This study offers insights into the readiness of Ogun State, Nigeria, for digital agriculture. It provides valuable information for technology developers and agricultural startups to understand the determinants of farmers' willingness to adopt digital application tools, contributing to the advancement of the agricultural technology landscape.

Small farmers in India are collectivized and legalized as Farmer Producer Companies (FPCs) to progress in agri-food value chains as small agribusiness enterprises. FPCs are dependent on timely information for their sustainability and profitability. Mobile apps are a cost-effective form of information and communication technology. Hence, the purpose of this study is to explore the major determinants of mobile apps adoption by FPCs.

Quantitative and qualitative data are collected by administering a semi-structured questionnaire and conducting in-depth interviews with board members of 115 FPCs, with a total membership of 30,405 farmers operating in 14 districts of the state of Kerala, India. The logit model is used for quantitative analysis, while dialog mapping is used for qualitative analysis, based on an integrated technology acceptance model and technology organization environment framework.

Logistic regression results evidence that amongst FPC characteristics, while company size and age are significantly impacting apps adoption, there is no significant association between board size, education level, multiple commodities business or export intention of companies on apps adoption. Digital literacy and technical hands-on training for FPC board members are quintessential to facilitate mobile apps adoption.

The findings are pertinent to policymakers to earmark funds for technical handholding and digital upskilling of FPCs. The need for developing comprehensive, location-centric, farmer-friendly apps by agritech companies is evidenced.

To the best of the authors’ knowledge, this is a pioneering work in the domain of mobile apps adoption from a farmers’ agribusiness enterprise perspective in an emerging market economy using a mixed-methods approach.

Long-term forecasts about commodity market indicators play an important role in informing policy and investment decisions by governments and market participants. Our study examines whether the accuracy of the multi-step forecasts can be improved using deep learning methods.

We first formulate a supervised learning problem and set benchmarks for forecast accuracy using traditional econometric models. We then train a set of deep neural networks and measure their performance against the benchmark.

We find that while the United States Department of Agriculture (USDA) baseline projections perform better for shorter forecast horizons, the performance of the deep neural networks improves for longer horizons. The findings may inform future revisions of the forecasting process.

This study demonstrates an application of deep learning methods to multi-horizon forecasts of agri-cultural commodities, which is a departure from the current methods used in producing these types of forecasts.

Financial inclusion and digital finance go side by side and help enhance agricultural activities; however, the magnitude of digital financial services varies across countries. In line with this argument, this study aims to examine whether financial inclusion enhances agricultural participation and decompose the significance of the difference in determinants of agricultural participation between financially included – not financially included households and digital finance – no digital finance households.

This study uses Pakistan’s household integrated economic survey 2018/19 to test hypotheses. The logit model is used to examine the effect of financial inclusion on agriculture participation. Moreover, this study employs a nonlinear Fairlie Oaxaca Blinder technique to investigate the difference in determinants of agricultural participation.

This study reports that financial inclusion positively influences agricultural participation, meaning households may have access to financial services and participate in agricultural activities. The results suggest that the likelihood of participating in agriculture in households with mobiles and smartphones is higher. Moreover, household size, income, age, gender, education, urban, remittances from abroad, fertilizer, pesticides, wheat, cotton, sugarcane, fruits and vegetables are the significant determinants of agricultural participation. To distinguish the financially included – not financially included households’ gap, this study employs a nonlinear Fairlie Oaxaca Blinder decomposition and finds that differences in fertilizer explain the substantial gap in agricultural participation. Likewise, this study tests the digital finance – no digital finance gap and finds that the difference in fertilizer is a significant contributor, describing a considerable gap in agricultural participation.

Empirically identified that various factors cause agricultural participation including financial inclusion and digital finance. Regarding the research limitation, this study only considers a developing country to analyze the findings. However, for future research, scholars may consider some other countries to compare the results and identify their differences.

The accessibility of fertilizer can reduce the agricultural participation gap. However, increased income level, education and cotton and sugar production can also overcome the differences in agriculture participation between digital finance and no digital finance households.

This is the first study to decompose the difference in determinants of agricultural participation between financially and not financially included households.

The aim of this study is to discern the role of digital finance in driving rural industrial integration and revitalization. Specifically, it intends to shed light on how the deep development of digital finance can contribute to the optimization and transformation of the rural industrial structure. The research further explores the particular effects of this financial transformation in the central and western regions of China.

This research studies the influence of digital finance on rural industrial integration across 30 Chinese provinces from 2011 to 2020. Utilizing the entropy weight method, a comprehensive evaluation index system is established to gauge the level of rural industrial integration. A two-way fixed effects model, intermediary effect model, and threshold effect model are employed to decipher the relationship between digital finance and rural industrial integration.

Findings reveal a positive relationship between digital finance and rural industrial integration. A single threshold feature was identified: beyond a traditional finance development level, the marginal effect of digital finance on rural industrial integration increases. These effects are more noticeable in central and western regions.

Empirical outcomes contribute to policy discourse on rural digital finance, assisting policymakers in crafting effective strategies. Understanding the threshold of traditional finance development provides a new perspective on the potential of digital finance to drive rural industrial integration.

This study aims to examine the effect of structural transformation on poverty alleviation in Sub-Saharan Africa (SSA) countries with a higher share of services as a percentage of gross domestic product (GDP). The study specifically focuses on the value-added share as a percentage of GDP in the agricultural, manufacturing, industrial, and service sectors using time series data from 1988 to 2019.

The study utilizes the autoregressive distributive lag (ARDL) bound test framework for estimation, based on the conclusions drawn from the augmented Dickey-Fuller and Phillips–Perron unit root tests, which provide evidence of a mixed order of integration.

The result reveals that agriculture value-added (AVA), manufacturing value-added (MVA), industrial value-added (IVA), and services value-added (SVA) have a positive and significant impact on poverty alleviation in both the short and long run. However, the agriculture sector is found to be more effective in reducing poverty compared to the other sectors examined in this study. Additionally, this study challenges the notion that SSA countries have undergone an immature structural transformation. Instead, it reveals a pattern of stagnant structural transformation, as indicated by the lack of growth in the industrial and manufacturing value-added shares of GDP.

To enhance productivity and reduce poverty, SSA economies should adopt a development strategy that prioritizes heavy manufacturing and industrial sectors, leading to a transition from the agricultural to the secondary and tertiary sectors.

The study contributes to the emerging literature on structural transformation by investigating which sector is more efficient in reducing poverty in SSA countries, using the value-added share as a percentage of GDP for agricultural, manufacturing, industrial, and service sectors. The study also aims to determine if SSA countries have experienced immature structural transformation due to the growing share in the service sector.

The study examines the synthesized influences of institutional governance and the scale effects of agricultural credits on the sustainability of microfinance institutions (MFIs) in Sub-Saharan Africa.

Using a sample of 840 MFIs (300 independent and 540 networked), the study applied Generalized Method of Moments (GMM) and Lewbel’s estimator.

Results indicate positive effects of financial structure, efficiency and agricultural credit scale on sustainability, with a non-linear U-shaped relationship between agricultural credit size and microfinance sustainability. Depending on institutional governance quality, a threshold is identified where agricultural credit scale significantly enhances the quality of Portfolio at Risk (lnPAR) in independent MFIs and Returns on Assets (lnROA) in networked MFIs.

Study suggests strengthening governance for transparency and operating within optimal size for enduring sustainable performance. While focused on Sub-Saharan Africa, future research could expand to various economies or introduce additional variables for a comprehensive analysis.

MFIs can achieve sustainability by implementing management guided by better institutional norms, innovative financial transformations better suited to financing agricultural activities and techniques and an organizational structure more aligned with their performance targets.

Broader and more reliable access to financial services, particularly in the agricultural sector, can stimulate production and alleviate poverty.

The study’s originality lies in its contribution to the literature by examining the role of institutional governance in microfinance institution performance and evaluating microfinance in a broader Sub-Saharan African context, proposing threshold limits where agricultural microcredit compromises performance.

The purpose of this paper is to assess the adoption of climate-smart agriculture (CSA) and its implication on improving the farming household food security status, their resilience and livelihood risk management of farmers.

This systematic review has followed procedures to accomplish the review, including literature searches, screening studies, data extraction, synthesis and presentation of the data.

Based on the result of the review, the determinants of CSA adoption can be categorized into five categories, including demographic factors (age, sex, family size, dependency ratio, education), economic factors (land size, household income, livestock ownership), institutional factors (extension services, training access, credit services, farm input, market distance), environmental factors (agroecology, change in precipitation, slope of land) and social factors (cooperatives membership, farmers perception). The result also shows that applying CSA practices has an indispensable role on increasing productivity, food security, income, building resilient livelihoods, minimizing production risk and alleviating poverty. This concluded CSA practice has a multidimensional role in the livelihood of agrarian population like Ethiopia, yet its adoption was constrained by several factors.

This review mainly emphasizes on the most commonly practiced CSA strategies that are examined by different scholars.

This paper investigates the ways digital media applications in rural areas have transformed the influence of social networks (SN) on farmers' adoption of various climate change mitigation measures (CCMM), and explores the key mechanisms behind this transformation.

The study analyzes data from 1,002 farmers’ surveys. First, a logit model is used to measure the impact of SN on the adoption of different types of CCMM. Then, the interaction term between digital media usage (DMU) and SN is introduced to analyze the moderating effect of digital media on the impact of SN. Finally, a conditional process model is used to explore the mediating mechanism of agricultural socialization services (ASS) and the validity of information acquisition (VIA).

The results reveal that: (1) SN significantly promotes the adoption of CCMM and the marginal effect of this impact varies with different kinds of technologies. (2) DMU reinforces the effectiveness of SN in promoting farmers' adoption of CCMM. (3) The key mechanisms of the process in (2) are the ASS and the VIA.

This study shows that in the context of DMU, SN’s promotion effect on farmers' adoption of CCMM is strengthened.