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This study examines the adoption drivers of scale-appropriate mechanization in Nepal's maize-based farming systems. The authors also assess the contribution of scale-appropriate mechanization to the United Nations Sustainable Development Goals (SDGs) of zero hunger (SDG2) and no poverty (SDG1).

Propensity score matching and doubly robust inverse probability-weighted regression adjusted methods were applied to estimate the effects of mini-tiller adoption. These methods control the biases that arise from observed heterogeneities between mini-tillers users and nonusers.

The study findings show that farm size, labor shortages, draft animal scarcity, market proximity, household assets and household heads' educational level influence the adoption of mechanization in Nepal. Mechanized farms exhibited enhanced maize productivity, profits and household food self-sufficiency. Reduced depth and severity of poverty were also observed. Nevertheless, these effects were not uniform; very small farms (≤0.41 ha) facing acute labor shortages benefited the most.

The study results suggest that policymakers in developing nations like Nepal may wish to expand their emphasis on scale-appropriate mechanization to improve farm productivity and household food security, reduce poverty and contribute to the SDGs.

This first-of-its-kind study establishes the causal effects between scale-appropriate farm mechanization and SDG1 (no poverty) and SDG2 (zero hunger) in a developing nation.

Agriculture in Ethiopia relies heavily on traditional farm power sources and is designated by the lowest farm machinery access, in contrast to other Sub-Sahara African (SSA) countries. The purpose of this research is to analyze the heterogeneity of mechanization service transactions and factors determining farmers' cooperation in mechanization clusters and willingness to accept land consolidation.

The authors conducted a cross-sectional survey of producer households in major crop production areas in the Oromia, SNNPR, Amhara and Tigray regions. The sampling design involved three stages: districts were selected using a stratified sampling approach accompanied by simple random samples of kebele units and producer households in the second and final stages, respectively.

This study’s results show that mechanization service costs, service relationships, clustering and land consolidation exhibit significant heterogeneity across the study areas. Cluster farming was found to be advantageous against diseconomies, rationalized by upgrading the mechanization scale. The probit model parameterization of the probability distributions reveals that household, land, crop, mechanization service, remoteness and location-related factors determine participation in mechanization clusters and willingness to accept land consolidation.

Fostering cooperation by focusing on constraints and demand of users is suggested to reduce transaction costs and expand hired mechanization services to unaddressed areas. The findings are relevant to most SSA countries where mechanization development is hampered by land fragmentation.

Limited information is available on agricultural mechanization development for smallholder farmers, particularly in Ethiopia, and this study adds empirical evidence about the synergy between cluster farming and mechanization, horizontal coordination and alternative supply models.

Due to global trade and transportation, the COVID-19 pandemic has rapidly reached all corners of the world; it has most impacted the poor communities and rural areas with limited or no access to recovery. This paper aims to understand the pandemic's impact on the market supply of vegetables (agrifood) in Ethiopia and other countries in sub-Saharan Africa and to implement scientifically based recommendations that can improve the challenges caused by the pandemic and improve the livelihoods of vegetable producers.

This study assessed the major incidents that follow the occurrence of the COVID-19 pandemic in Ethiopia with the factors that significantly influence onion market supply in the Mecha district of Northwestern Ethiopia using linear multiple regression.

The study revealed that producers, processors, local collectors, cooperatives, wholesalers, retailers and consumers are the main actors. The most important difference that happened on the onion market channel after the outbreak of coronavirus was brokers were removed from the market channel after the pandemic and this saved the cost producers pay as a commission. The pandemic disrupted input distribution, extension and cooperative services and created labor scarcity. Access to market, postharvest value addition, price and marketing contract were significant factors that influence the market supply of onion. The study identified the need for policy interventions by the government to overcome the postpandemic challenges and ensure the sustainable development of onion production and marketing in the Mecha district.

The limitations of the study are primarily related to the methodology as data are collected at a single moment in time. However, the study observes that those changes after the pandemic are better understood if we collect data at different time. Therefore, the future study needs to provide longitudinal data to examine stability of response and to observe performance of the market that occur over time.

This original research is the first to study the impacts of the COVID-19 pandemic in Northwestern Ethiopia. The data used in the analysis are primary.

This paper aims to investigate how climatic, market and policy factors interact to determine food production in Togo. Specifically, we estimate acreage and yield response to market prices, weather and policy changes for maize and rice.

We use panel data estimators in a Seemingly Unrelated Regressions Equation (SURE) model with region-level data from the Food and Agriculture Organization statistics department and the National Oceanic and Atmospheric Administration (NOAA) of the US Department of commerce.

We found lower fertilizer price and higher grain price effects on maize acreage and yield. In addition, we found a positive effect of expected rice price on both its acreage and yield. As expected, rainfall during planting months has a significant impact on both maize (April) and paddy (May) acreage allocations. Similarly, total rainfall during the growing season has a positive impact on both maize and paddy yields. Moreover, recent agricultural policy initiative designed to boost domestic food production has significantly increased acreage and yield for maize, and yield for paddy, especially the strategy for agricultural growth.

The dataset includes region-level observations from 1991 to 2012 which limits the observation span. However, we had enough variability in key variables to determine the estimated coefficients.

Although the dataset is limited in time (1991–2012) and uses national-level output prices, this investigation reveals that cropland allocation to maize and rice is sensitive to fertilizer and grain prices, weather expectations and policy interventions. These findings provide evidence for sustainable food production and productivity enhancement in Togo.

Understanding drivers of cropland allocation and cereal yield contribute to better food security and poverty reduction in developing countries, especially Togo.

Prior to this study, little was known on the effect of price, climate and policy on cropland allocation in Togo. This investigation contributes significantly to filling this knowledge gap and provides insights for effective interventions.

With the development of social productive forces and the advancement of agricultural practices since the founding of New China, the connotation of agricultural modernization with Chinese characteristics has undergone a process from formation to continuous expansion and deepening.

Its evolution can be roughly divided into four stages: the exploration stage, the formation stage, the establishment stage and the deepening stage. The historical evolution of the connotation of agricultural modernization with Chinese characteristics demonstrates four typical characteristics, namely increasingly scientific logical premise, continuously diversified orientations, increasingly improved core contents and progressively maturing strategies of development.

The achievements of agricultural modernization have laid a solid foundation for China's industrial modernization and the rapid development of the national economy. Meanwhile, the authors have identified through practical exploration a path of agricultural modernization with Chinese characteristics. In recent years, academic research on the connotation of agricultural modernization with Chinese characteristics has gradually heated up, and relevant achievements have emerged constantly.

The Communist Party of China (hereinafter “CPC”) has placed considerable emphasis on agricultural issues and has been committed to promoting agricultural modernization since the founding of New China. Through long-term persistence and unremitting efforts, China has made remarkable achievements in agricultural development: significantly improved agricultural production conditions and agricultural output capacity, constantly optimized agricultural structure and steadily increased the income of farmers.

The purpose of this study is to investigate the interactive relationship between non-farm employment and mechanization service expenditure.

The study employs an innovative two-stage probit least squares (2SPLS) model to analyze the survey data collected from 1,148 rural households in China. This model not only simultaneously estimates the impact of non-farm employment on mechanization service expenditure and the impact of mechanization service expenditure on non-farm employment, but also addresses endogeneity issues associated with these two activities.

The empirical results show that non-farm employment and mechanization service expenditure are jointly determined. In particular, the study finds that non-farm employment significantly increases mechanization service expenditure, and vice versa. The results are confirmed by an estimation that captures a dichotomous decision of mechanization service usage. The interactive effects of non-farm employment on mechanization service expenditure are heterogeneous between male and female household heads and among households with different member sizes. Further analyses reveal that (1) mechanization service expenditure increases with increasing non-farm working time; (2) local non-farm employment, rather than provincial non-farm employment, has a larger impact on mechanization service expenditure; and (3) the number of household members employed in non-farm works does not affect mechanization service expenditure significantly.

Although mechanization service markets are rapidly growing in many developing and transition countries, little is known about how service purchasing interacts with farmers' decisions to work in the non-farm sector. This study makes the first attempt by investigating the interactive effects of non-farm employment on mechanization service expenditure in rural China. The findings provide significant evidence for policymakers in China and other countries in their efforts to generate non-farm work opportunities and promote agricultural mechanization, with the aim of boosting rural development and improving farm economic performance.

The objective of this study was to examine the impact of the pandemic on sustainable agricultural practices (SAP) adoption such as: organic fertilizers, minimal use of tillage, crop rotation, soil burning and crop association in the frame of family farming systems in Ecuador.

The present research employed probit models' estimation with pooled data from 2018 to 2020. The study combined three sources of information with The Survey on Surface and Agricultural Continuous Production, as the main. This study also proposed the analysis of six regions: Coast, Coast Mountains, Northern Highlands, Central Highlands, Southern Highlands and the Amazon.

The authors see a lower adoption in the year 2020, where the pandemic was one of the causes. The only exception was the use of organic fertilizer. The adoption of these sustainable practices differed across the six regions. The findings also reveal that the employment generated by agricultural enterprises had a negative influence on the adoption of three sustainable practices, and that for the remaining practices the effect was positive.

The data set lacks information on the acceptance and the application of the practices promoted by agricultural technical assistance, which could provide insights into the effectiveness of the learning process. The limited observation period does not allow for investigating long-term effects on sustainable practices adoption.

This study helps to understand the implications of the COVID-19 pandemic in the adoption of SAP. Additionally, this research can help with the scalability of the practices starting from the regions that are most likely to adopt each of them.

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

China is the world's largest consumer of pesticides. To increase the use efficiency and achieve more sustainable and environmentally friendly use of pesticides in China, it is crucial to understand why Chinese farmers use such a large amount of pesticides.

The relationship between farm size and pesticide use was investigated by using national household-level panel data from 1995 to 2016.

Farms that are small and fragmented lead to the use of large amounts of pesticides in China. For a given crop type, three factors contribute to a negative relationship between farm size and pesticide use: the spillover effect from the use of pesticides by other farmers in the same village, the level of mechanization and the management ability of farmers. The first two factors play important roles in the cultivation of grain crops, while the last factor is the main reason why farmers with larger plots of land use fewer pesticides in the cultivation of vegetables. In addition, the effect of agricultural machinery services on reducing the use of pesticides is currently limited, and the service system in China is still insufficient, which has been pointed out that it is also due to the prevalence of small and fragmented farms.

The authors investigate and compare the farm size–pesticide use relationship in both grain and cash crop production. Moreover, the authors systematically explore and explain how farm size is related to a reduction in pesticide use in the cultivation of grain crops and cash crops. These results can help to better understand the role of land scale in pesticide use, lay a foundation for the formulation of policies to reduce pesticide use and provide valuable knowledge about pesticide use for other developing countries around the world.

This study assesses the extent to which integrated extension services contribute to the adoption of climate-smart agriculture (CSA) innovations within the cotton value chain in Burkina Faso.

To address the research question, a probit multivariate econometric model with sample selection is utilized. The model is applied to a random sample of farmers (n = 510), and the endogeneity is addressed through a control function approach.

The study highlights the central role of value chains, particularly in the cotton sector, in overcoming resource scarcity through integrated extension services. Findings show that smallholder farmers who benefit from sound extension services are more willing to adopt and diversify CSA technologies. These include improved seeds, conservation techniques, adapted planting dates and mechanization. This study confirms the synergistic nature of these technologies and emphasizes that effective climate risk mitigation depends on the combined adoption of CSA technologies.

The use of cross-sectional data limits the analysis of long-term farmer behavior, and due to data limitations, the focus was primarily on the contributions of cotton companies and farmers to climate risk mitigation. Future research using panel data across the value chain could provide a more robust insights for policy decision-making.

The study contributes to the existing body of knowledge by emphasizing the crucial role of integrated extension services within the cotton value chain in developing countries. This highlights the critical benefits for farmers and emphasizes the need to diversify modern technologies to effectively combat climate change and its variability in agriculture.