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The ecological quality of large-scale farms is a critical determinant of crop growth. In this paper, an ecological assessment procedure suitable for agricultural regions should be developed based on an improved remote sensing ecological index (IRSEI), which introduces an integrated salinity index (ISI) tailored to the salinized soil characteristics in farming areas and incorporates ecological indices such as the greenness index (NDVI), the humidity index (WET), the dryness index (NDBSI), and the heat index (LST). The results indicate that between 2013 and 2022, the mean IRSEI increasing from 0.500 in 2013 to 0.826 in 2020 before decreasing to 0.646 in 2022. From 2013 to 2022, the area of the farm that experienced slight to significant improvements in ecological quality reached 1419.91 km2, accounting for 71.94% of the total farm area. An analysis of different land cover types revealed that the IRSEI performed more reliably than did the original RSEI method. Correlation analysis based on crop yields showed that the IRSEI method was more strongly correlated with yield than was the RSEI method. Therefore, the proposed IRSEI method offers a rapid and effective new means of monitoring ecological quality for agricultural planting areas characterized by soil salinization, and it is more effective than the traditional RSEI method.

This paper revisits the inverse farm size-productivity relationship in Kenya. The study makes two contributions. First, the relationship is examined over a much wider range of farm sizes than most studies, which is particularly relevant in Africa given the recent rise of medium-and large-scale farms. Second, we test the inverse relationship hypothesis using three different measures of productivity including profits per hectare and total factor productivity, which are arguably more meaningful than standard measures of productivity such as yield or gross output per hectare. We find a U-shaped relationship between farm size and all three measures of farm productivity. The inverse relationship hypothesis holds on farms between zero and 3 hectares. The relationship between farm size and productivity is relatively flat between 3 and 5 hectares. A strong positive relationship between farm size and productivity emerges within the 5 to 70 hectare range of farm sizes. Across virtually all measures of productivity, farms between 20 and 70 hectares are found to be substantially more productive than farms under 5 hectares. When the analysis is confined to fields cultivated to maize (Kenya’s main food crop) the productivity advantage of relatively large farms stems at least partially from differences in technical choice related to mechanization, which substantially reduces labor input per hectare, and from input use intensity.

This study examines sustainable agricultural development in Vietnam by focusing on rice production in the Mekong Delta. Vietnam is the third largest rice-exporting country in the world and more than 90% of rice for export is from the Mekong Delta. We attempt to identify changes in the rice industry, specifically examining farming mechanization, trends in farm operation, and farm household economy. The main structure of our study is based on our direct interviews with 420 farmer households, 81 registered large farm owners, 75 farming service providers, and local government members. We carried out those interviews in 2013 and 2014. We identified several important changes brought by technological advances, economic modernization, government policies, and environmental movements. One important finding was the increasing trend of agricultural land per farmer household, such as from 1.98 to 2.27 ha in 2010 to 2018, and the decreasing trend of labor force, which pushed up agricultural mechanization. Another point was the land law revised in 2013, which allowed farmers to borrow (no private land ownership in Vietnam) farms up to 30ha/household if registered as a “Trang Trai” farm. Farmers accepted the use of tractors and combine harvesters; however, rice seed sowing was not mechanized, so rice transplanters were rarely used for rice planting. Among the processes of rice production, sowing of rice seeds was the least mechanized. In order to achieve more sustainable agricultural practices, we recommend improving the mechanization of the rice seed sowing process for the Mekong Delta farmers with acceptable conditions. Another finding was positive movements among Mekong Delta researchers and farmers to improve their rice quality to be accepted as high-quality rice in the global rice market and to dispel the bad reputation of the past.

Farms in the European Union come in a wide variety of sizes and the effect of farm size on profitability (return on assets – ROA) has not been sufficiently investigated. The principal goal of this paper, therefore, is to study the determinants of farm profitability using the panels of the Farm Accountancy Data Network (FADN) on farms of different economic size between 2007 and 2018. We use a profitability function based on ratios that show the production and financial management strategies used by the farms. We also analyse the impact of subsidies under the Common Agricultural Policy (CAP). To deal with endogeneity, we run dynamic panel models using the system generalised method of moments (sys-GMM) estimators. We highlight the important role of the high level of equity turnover. An increase in production relative to the farm's equity plays a crucial role in the growth of profitability for all groups of farms, but it is especially important for smaller entities. In addition, farm managers should control the level of debt since the debt-to-asset ratio is a highly significant negative determinant of farm profitability in most of the groups. The increase in subsidy rate generally translates into higher ROA, but this variable has a negative impact across the largest holdings.

Many broadacre farmers have a time series of crop yield monitor data for their fields which are often augmented with additional data, such as soil apparent electrical conductivity surveys and soil test results. In addition there are now readily available national and global datasets, such as rainfall and MODIS, which can be used to represent the crop-growing environment. Rather than analysing one field at a time as is typical in precision agriculture research, there is an opportunity to explore the value of combining data over multiple fields/farms and years into one dataset. Using these datasets in conjunction with machine learning approaches allows predictive models of crop yield to be built. In this study, several large farms in Western Australia were used as a case study, and yield monitor data from wheat, barley and canola crops from three different seasons (2013, 2014 and 2015) that covered ~ 11 000 to ~ 17 000 hectares in each year were used. The yield data were processed to a 10 m grid, and for each observation point associated predictor variables in space and time were collated. The data were then aggregated to a 100 m spatial resolution for modelling yield. Random forest models were used to predict crop yield of wheat, barley and canola using this dataset. Three separate models were created based on pre-sowing, mid-season and late-season conditions to explore the changes in the predictive ability of the model as more within-season information became available. These time points also coincide with points in the season when a management decision is made, such as the application of fertiliser. The models were evaluated with cross-validation using both fields and years for data splitting, and this was assessed at the field spatial resolution. Cross-validated results showed the models predicted yield relatively accurately, with a root mean square error of 0.36 to 0.42 t ha−1, and a Lin’s concordance correlation coefficient of 0.89 to 0.92 at the field resolution. The models performed better as the season progressed, largely because more information about within-season data became available (e.g. rainfall). The more years of yield data that were available for a field, the better the predictions were, and future work should use a longer time-series of yield data. The generic nature of this method makes it possible to apply to other agricultural systems where yield monitor data is available. Future work should also explore the integration of more data sources into the models, focus on predicting at finer spatial resolutions within fields, and the possibility of using the yield forecasts to guide management decisions.

In this paper we develop a typology of ‘non-commercial’ approaches to farming, based on a survey of a representative sample of farmers in Scotland, United Kingdom. In total, 395 (16.6% of the sample) farmers indicated that they do not seek to make a profit on their farms. We estimate that these non-commercial approaches to farming are utilised on at least 13% of agricultural land in Scotland. As such, non-commercial farming (NCF) is not a marginal practice, nor are NCF limited to small-scale ‘hobby’ farms: NCF exist across the scale of agricultural holding sizes and are operated by a wide range of socio-demographic cohorts. We identify 6 types of NCF: agricultural residences, specialist smallholdings, horsiculture holdings, mixed smallholdings, amenity mixed farms, and large farms or estates. These types were differentiated primarily by the scale of farm size, presence of diversification activities and types of animal present. The analysis demonstrates a number of emergent patterns of land management: de facto land abandonment, transition towards ‘horsiculture’, and management differences between retiring and new entrant NCF. We argue that the types identified reflect a number of intersecting issues in contemporary agrarian transitions, particularly the aging farmer population; generational renewal; and gendered implications of agricultural restructuring.

During the last decade, post-harvest losses (PHL) reduction has been topping the agenda of governments as a pathway for addressing food security, poverty, and nutrition challenges in Africa. Using survey data from 579 households, we investigated the factors that affect farmers’ decisions to adopt post-harvest technologies: mechanized shelling, drying tarpaulins, and airtight storage validated for reducing PHL in Tanzania’s maize-based systems, and the impacts on households’ food security and welfare. Mechanized shelling addressed a labor issue, while tarpaulins and airtight storage addressed product quality and quantity concerns. The results revealed large farm sizes and location in higher production potential zones (proxies for higher production scale) and neighbors' use of the technologies as universal drivers for adoption. Access to credit and off-farm income were unique determinants for airtight storage, while group membership increased the probability of adopting drying tarpaulin and airtight storage. The technologies have positive impacts on food security and welfare: drying tarpaulins and airtight storage significantly increased food availability (18–27%), food access (24–26%), and household incomes (112–155%), whereas mechanized shelling improved food and total expenditures by 49% and 68%, respectively. The share of total household expenditure on food decreased by 42%, 11%, and 51% among tarpaulin, mechanized shelling, and airtight storage adopter households, signaling significant improvements in food security and reductions in vulnerability. The results point to the need for policy support to enhance the adoption of these technologies, knowledge sharing among farmers, and financial resources access to support investments in the technologies.

The relevance and the impact of experience in insurance markets are underinvestigated. From Italian farm-level data we estimate a dynamic discrete-choice model of participation to investigate the role of experience. The methodology, coupled with exploratory analysis, allows one to compare how different sources of experience influence the crop insurance decision making process. We found that direct experience is a catalyst for insurance participation for medium and large farms. The experience indirectly acquired is also relevant, especially for small farms. Policy implications include the importance of information campaigns and of bolstering uptake to exploit the advantages of the inertia and spillover effects that emerge from experience.

Life cycle assessment (LCA) had proven to be an appropriate assessment tool for analysis of agro-ecosystems by identifying, quantifying, and evaluating the resources consumed and released into the environment. In order to assess the relevant environmental impacts of rice agro-ecosystems due to a specific process, using LCA method, two factors concerned with resource utilization and contaminant emissions were calculated in north of Iran during 2016 and 2017. All the management practices/inputs were monitored and recorded with the help of local experts without interference in farmer’s practices. After preliminary evaluation, 100 paddy fields were selected in three planting systems (low input, conventional, and high input) which were predicted in two planting methods (semi-mechanized and traditional) in small, medium, and large farm size levels. Functional unit was considered as one ton paddy yield. The finding revealed that in both regions, all the impact categories and environmental pollutant were almost same and farmer’s management practices are close to each other. Also, climate change (CC) in Amol and Rasht regions was 277.21 and 275.79 kg CO 2  eq., respectively. The most CC, global warming potential (GWP 100a), and cumulative energy demand (CED) in both regions were observed in high-input system for semi-mechanized method. Furthermore, the result for the impact categories of terrestrial acidification (TA), freshwater eutrophication (FE), marine eutrophication (ME), agricultural land occupation (ALO), water depletion (WD), metal depletion (MD), and fossil depletion (FD) was similar to the CC, GWP, and CED where the highest amounts in both regions statistically went to high-input system, traditional planting method, and small farms. Moreover, in both regions, high-input and conventional systems emitted higher heavy metals than low-input system. Furthermore, the most heavy metal emission in the air was achieved in small farm, and medium farm got the next rank. Additionally, the high consumption of chemical inputs, such as fossil fuels and fertilizers, in the high-input and conventional systems led to an increase of environmental pollutant in comparison with low-input systems. Therefore, to increase the sustainability of agro-ecosystems, as well as to reduce the environmental impacts of pollutant, reforming the pattern of chemical input consumption and reducing the use of non-renewable energy sources are essential.