Large-scale Farms Research Articles

Analysis of Organic Fertilizer Use in Improving Soil Quality and Agricultural Yields in Indonesia

This research investigates the impact of organic fertilizer use on soil quality and agricultural yields in Indonesia through a qualitative analysis. Ten informants, including small-scale and large-scale farmers, agricultural experts, and environmentalists, provided insights into their perceptions and experiences. The study reveals diverse farmer perspectives, with small-scale farmers exhibiting limited awareness and large-scale farmers emphasizing long-term benefits. Organic fertilizers were found to positively influence soil quality, improving structure, water retention, and microbial activity. Crop-specific responses varied, highlighting the importance of considering regional agroecological conditions. Challenges such as access, affordability, and labor intensity were identified, countered by opportunities including government programs, cooperative farming, and consumer demand. Decision-making factors encompassed economic considerations, knowledge, environmental consciousness, and social influences. The synthesis underscores the need for tailored interventions, acknowledging the complexity of factors shaping the adoption of organic fertilizers in Indonesian agriculture.

Agricultural industrial scale, price random fluctuation, and profitability levels: evidence from China’s pig industry

IntroductionPromoting the development of large-scale pig farming is a crucial measure implemented by the Chinese government to regulate the pig market.MethodsBy utilizing panel data from 30 provinces in China spanning from 2003 to 2020 and employing the PVAR model, this study examines the relationships among price random fluctuations, profftability levels, and industrial scale.Results and discussionThe findings reveal that industrial scale can effectively mitigate price random fluctuations; however, it also leads to a decrease in relative hog prices. Moreover, there exists significant heterogeneity in the impact of scaling on price random fluctuations. Increasing the proportion of farmers engaged in pig farming with a scale ranging from 500 to 9,999 heads reduces random price fluctuations, while increasing the proportion of farmers involved in pig farming with a scale exceeding 10,000 heads has no effect on stabilizing such fluctuations. Additionally, threshold effects are observed for epidemics and environmental regulations. When environmental regulations are less stringent, industrial scale enhances relative prices and stabilizes random fluctuations; nevertheless, once certain thresholds are surpassed, industrial scale diminishes relative prices and eliminates its stabilizing effect on random fluctuations. Similarly, after an epidemic surpasses its threshold level, industry scale fails to stabilize random price fluctuations. These findings provide valuable insights for governments when formulating industrial policies aimed at mitigating agricultural market risks.

Revolutionizing Energy Harvesting: Harnessing Ambient Solar Energy for Enhanced Electric Power Generation

The escalating demand for sustainable and renewable energy sources has propelled the exploration of ambient solar energy as a pivotal avenue for cleaner power generation. This review paper delves into the intricacies of solar energy harvesting technologies, providing a comprehensive analysis of principles, efficiency metrics, material advancements, and versatile applications. Focusing on the photovoltaic effect as the cornerstone of solar cells, the critical impact of semiconductor material selection and bandgap engineering on efficiency is thoroughly examined. Key performance measures such as fill factor, open-circuit voltage, and short-circuit current are emphasized for their pivotal role in evaluating system efficiency. The adaptability of solar energy solutions is exemplified through diverse applications, spanning from portable electronics to large-scale solar farms. Looking towards the future, this review envisions a promising trajectory for solar energy harvesting, driven by continual advancements in technology, material science, and efficiency measures. The insights gleaned from this comprehensive examination are poised to catalyze the development of more efficient and accessible solar energy harvesting technologies. This, in turn, promises to play a significant role in the global transition towards a greener and more sustainable energy landscape, contributing substantively to sustainability, climate mitigation, and an enhanced quality of life worldwide. Keywords: Solar energy harvesting, solar cells, material selection, panel orientation, storage techniques, system integration, case studies.

Quantification of three-dimensional added turbulence intensity for the horizontal-axis wind turbine considering the wake anisotropy

The accurate evaluation of wake turbulence intensity (TI) is of great significance for the layout optimization of wind farms and research on control strategies. However, the existing engineering wake models still have many shortcomings in evaluating the TI in the wake of horizontal-axis wind turbines (HAWTs), especially in the near wake region. In response to this, this article proposed an analytical model based on the double-Gaussian ellipse function that can describe the three-dimensional added TI distribution in the entire wake region. Firstly, the anisotropy of wake expansion is taken into account in both the horizontal and vertical directions, assuming that the added TI distribution downstream of the HAWT is a double-Gaussian elliptical shape. Secondly, taking into account the self-similarity characteristics of flow TI, the maximum added TI and its position in the entire wake region are evaluated. In addition, considering the influence of the ground effect, the vertical turbulence distribution was corrected. Finally, the proposed added turbulence model was validated and relative error analysis was conducted using large eddy simulation (LES) data, wind field measurement data, and wind tunnel experimental data. The results show that the model had good prediction accuracy in the entire wake region, and its prediction performance was significantly improved compared to traditional models, especially in the near wake region, with the lowest even relative error of 3.66%. This model has significant advantages such as low computational cost, wide applicability, and high accuracy. It can reduce energy losses in wind farms, improve wind energy utilization efficiency, and has great potential for widespread application in large-scale wind farms.

Downscaling and Wind Resource Assessment of Climatic Wind Speed Data Based on Deep Learning: A Case Study of the Tengger Desert Wind Farm

Analyzing historical and reanalysis datasets for wind energy climatic characteristics offers crucial insights for wind farms and short-term electricity generation forecasting. However, large-scale wind farms in Chinese deserts, the Gobi, and barren areas often lack sufficient wind measurement data, leading to challenges in assessing long-term power generation revenue and introducing uncertainty. This study focuses on the Tengger Desert as the study area, processes the Coupled Model Intercomparison Project Phase 6 (CMIP6) data, and analyzes and compares wind energy’s future characteristics utilizing a developed deep learning (DL) downscaling algorithm. The findings indicate that (1) the Convolutional Neural Network (CNN) downscaling model, with the Weather Research and Forecasting Model (WRF) numerical simulation results as the targets, exhibits spatial distribution consistency with WRF simulation results in the experimental area. (2) Through testing and validation with three practical wind measurements, the annual average wind speed error is below 4%. (3) In the mid-term future (~2050), the average wind speed in the experimental area remains stable with a multi-year average of approximately 7.00 m·s−1. The overall wind speed distribution range is significant, meeting the requirements for wind farm development.

A dynamic individual method for yak heifer live body weight estimation using the YOLOv8 network and body parameter detection algorithm

Live body weight (LBW) is one of the most important parameters for supervising the growth and development of livestock. The yak (Bos grunniens) is a special species of cattle that lives on the Qinghai-Tibetan Plateau. Yaks are more untamed than regular cattle breeds, thus it is more challenging to measure their LBW. In this study, a YOLOv8 yak detection and LBW estimation models were used to automatically estimate yak LBW in real-time. First, the proper posture (normal posture) and individual yak identification was confirmed and then the YOLOv8 detection model was used for LBW estimation from 2-dimensional (2D) images. Yak LBW was estimated through yak body parameter extraction and a simple linear regression between the estimated yak LBW and the actual measured yak LBW. The results showed that the overall detection performance of yak normal yak posture was described by precision, recall, and mean Average Precision 50 (mAP50) indicators, reaching 81.8, 86.0, and 90.6%, respectively. The best yak identification results were represented by precision, recall, and mAP50 values of 97.8, 96.4, and 99.0%, respectively. The yak LBW estimation model achieved better results for the 12 mo old yaks with shorter hair with R2, root mean square error (RMSE), mean absolute percentage error (MAPE), and Multiple R values of 0.96, 2.43 kg, 1.69%, and 0.98, respectively. The results demonstrate that yak LBW can be estimated and monitored in real-time using this approach. This study has the potential to be used for daily yak LBW monitoring in an unstressed manner and to save considerable labor resources for large-scale livestock farms. In the future, to reduce the limitations caused by the impacts of yak hair and light condition data sets of dairy cows and yaks of different ages will be used to improve and generalize the model.

The Effect of Hog Futures in Stabilizing Hog Production

China’s large-scale hog farmers are playing an increasingly important role in promoting the stable development of the hog industry. Taking large-scale hog enterprises as samples, based on hog sales data from January 2019 to July 2022, this paper adopts a two-way fixed-effects model to test the impact, mechanism, and heterogeneity of hog futures on the production stability of large-scale hog farmers. The study found that hog futures help promote stable production of large-scale farmers. This finding still holds after a series of robustness tests. The mechanism analysis found that, first, hog futures help large-scale farmers expand their risk management factor inputs. Second, hog futures help reduce the impact of hog price risk on production. Finally, hog futures help stabilize farmers’ production expectations. The moderating effects analysis found that the stabilizing effect of hog futures will enhance as farmers’ share of hog farming operations increases. Heterogeneity analysis found that when hog prices fluctuate negatively, hog futures help promote the stable production of large-scale farmers. When hog prices fluctuate positively, the production stabilization effect of hog futures is not obvious. Therefore, hog enterprises should be encouraged to participate in hog futures hedging transactions to promote stable hog production.

Analyzing Food Loss in Banana Pepper (Capsicum annuum) Production: Causes, Impact, and Challenges, A Case Study of Large-Scale Farming in the Hot District, Chiang Mai, Thailand

This study aimed to assess both quantitative and qualitative food loss in banana pepper production at the agricultural level. Its objectives included employing the Supply Chain Operations Reference Model (SCOR Model) for analysis, identifying root causes of losses and evaluating quantity and quality. Food loss here refers to diminished quantity and quality due to supply chain decisions and actions. Data collection and analysis included an extensive literature review, compilation of secondary data, primary data gathering via questionnaires and measurements of lost banana pepper yield during harvesting. Qualitative insights were acquired through in-depth interviews and focus group discussions. The study identified weather variability as the primary driver of food loss, with many farmers lacking access to climatic measuring tools. Additional losses, assessed visually, comprised damage from diseases, pests, stunted growth, mechanical issues and other factors, accounting for 13.68, 10.13, 6.95, 4.03 and 7.01 % of the total loss, respectively. Remarkably, only 57.93 % of the produce met quality and sale readiness criteria. The loss of banana peppers was estimated at 55.21 %, equivalent to 1,189.62 kg/rai, resulting in an economic loss of 35,688.58 Baht/rai. The study also noted significant nutrient losses, including energy, carbohydrates, fiber, protein, potassium, phosphorus, vitamin B1 and vitamin C, amounting to 321,197 kcal/rai and 57,102, 38,068, 17,844 g/rai and 3,045,427, 559,121, 487,744, 166,547 mg/rai, respectively. Farmers, while aware of food loss issues, often struggle to quantify them accurately. This study’s findings can raise farmers’ awareness and aid in designing training programs to mitigate such losses. However, addressing the inherent variability and unpredictability of on-farm food loss remains a challenge. Future research should prioritize a comprehensive analysis of banana pepper losses on farms. HIGHLIGHTS Weather variability was identified as the primary driver of food loss, exacerbated by limited access to climatic measuring tools Various factors contribute to food loss, including damage from diseases, pests, stunted growth and mechanical issues, accounting for 68 to 7.01 % of total loss Significant economic and nutrient losses, with a 21 % loss of banana peppers, resulting in an economic loss of 35,688.58 Baht/rai and substantial nutrient losses in various categories GRAPHICAL ABSTRACT

Acoustic presence and demographics of sperm whales (Physeter macrocephalus) off southern New England and near a US offshore wind energy area

Abstract Construction in the southern New England wind energy area (WEA), a large-scale offshore wind farm on the east coast of the United States, started in June 2023. Baseline data was collected from 2020 to 2022, with six passive acoustic recorders (SoundTraps) deployed at shallow (<60 m) sites in the vicinity of Nantucket Shoals and Cox’s Ledge. Data were analysed for sperm whale presence, and demographic composition was assessed using interclick intervals. Presence varied by site, season, and year. Sperm whales were detected year-round but the majority (78%) of days with acoustic occurrences were between May and August. Three demographic classes (putative social groups, adult males, and midsize animals) were detected across multiple seasons, with social groups detected most frequently. Sound propagation tests were conducted at two sites and predicted detection ranges within 20–40 km indicate that sperm whales were likely in proximity to the WEA. These results provide a baseline that will be used to assess ongoing sperm whale presence, especially that of social groups which may be more sensitive to disturbance. This study highlights why sperm whales, classed as endangered in US waters, should be considered in mitigation plans and permitting efforts for offshore wind energy.

An Ecological Quality Evaluation of Large-Scale Farms Based on an Improved Remote Sensing Ecological Index

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.

Integrating Multiscale Geospatial Analysis for Monitoring Crop Growth, Nutrient Distribution, and Hydrological Dynamics in Large-Scale Agricultural Systems

Monitoring crop growth, soil conditions, and hydrological dynamics are imperative for sustainable agriculture and reduced environmental impacts. This interdisciplinary study integrates remote sensing, digital soil mapping, and hydrological data to elucidate intricate connections between these factors in the state of Ohio, USA. Advanced spatiotemporal analysis techniques were applied to key datasets, including the MODIS sensor satellite imagery, USDA crop data, soil datasets, Aster GDEM, and USGS stream gauge measurements. Vegetation indices derived from MODIS characterized crop-specific phenology and productivity patterns. Exploratory spatial data analysis show relationships of vegetation dynamics and soil properties, uncovering links between plant vigor, edaphic fertility, and nutrient distributions. Correlation analysis quantified these relationships and their seasonal evolution. Examination of stream gauge data revealed insights into spatiotemporal relationships of nutrient pollution and stream discharge. By synthesizing diverse geospatial data through cutting-edge data analytics, this work illuminated complex interactions between crop health, soil nutrients, and water quality in Ohio. The methodology and findings provide actionable perspectives to inform sustainable agricultural management and environmental policy. This study demonstrates the significant potential of open geospatial resources when integrated using a robust spatiotemporal framework. Integrating additional measurements and high-resolution data sources through advanced analytics and interactive visualizations could strengthen these insights.

Metagenome-Based Analysis of the Microbial Community Structure and Drug-Resistance Characteristics of Livestock Feces in Anhui Province, China.

We analyzed metagenome data of feces from sows at different physiological periods reared on large-scale farms in Anhui Province, China, to provide a better understanding of the microbial diversity of the sow intestinal microbiome and the structure of antibiotic-resistance genes (ARGs) and virulence genes it carries. Species annotation of the metagenome showed that in the porcine intestinal microbiome, bacteria were dominant, representing >97% of the microorganisms at each physiological period. Firmicutes and Proteobacteria dominated the bacterial community. In the porcine gut microbiome, the viral component accounted for an average of 0.65%, and the species annotation results indicated that most viruses were phages. In addition, we analyzed the microbiome for ARGs and virulence genes. Multidrug-like, MLS-like, and tetracycline-like ARGs were most abundant in all samples. Evaluation of the resistance mechanisms indicated that antibiotic inactivation was the main mechanism of action in the samples. It is noteworthy that there was a significant positive correlation between ARGs and the total microbiome. Moreover, comparative analysis with the Virulence Factor Database showed that adhesion virulence factors were most abundant.

Artificial intelligence applied in water optimization in agricultural crops (OTIMAGRI)

This study introduces an innovative optimization system for agriculture, utilizing the Particle Swarm Optimization (PSO) algorithm. This system focuses on maximizing net revenue in agricultural contexts while simultaneously minimizing the consumption of natural resources and inputs, with particular attention to the efficient use of water. A distinctive feature of the system is its versatility and accessibility, being designed for easy implementation on smartphones, which broadens its reach to a variety of users, from small to large-scale farmers. The efficiency and speed in obtaining results are key aspects of this system, facilitating a more agile and informed decision-making process in the agricultural sector. The PSO algorithm, which forms the basis of the system, effectively identifies the optimal balance between input consumption and crop productivity. This study validated the system through comparisons with available literature data, focusing on specific crops such as iceberg lettuce and melon. The results not only showed consistency with existing data but in some cases, exceeded expectations. In conclusion, this system represents a significant contribution to modern agriculture, offering a reliable and easily accessible tool for economic decision-making. The ability to quickly adapt to different types of inputs, compatibility with mobile devices, and the ease of incorporating new production functions, regardless of complexity or the number of inputs, highlight the flexibility and practical relevance of this technological innovation.

PREVALENCE OF BOVINE MASTITIS AND THE FIRST DETECTION OF MYCOPLASMA BOVIS IN VIETNAM’S DAIRY FARMS

Mastitis still remains an economic disease in dairy farms all over the world. This study aimed to report the prevalence of this disease in large-scale farms in Vietnam, the pathogens profile, and first report the detection of Mycoplasma bovis (M. bovis) by applying conventional culture and real-time PCR (polymerase chain reaction) methods. We examined 64,802 enrolled dairy cows from six farms in Vietnam, yielding 1,874 (2.9%) total cases of clinical mastitis. The majority of the 14 pathogens were identified. Klebsiella spp. accounted for 21.0% of the total isolates. Streptococcus uberis (S. uberis), Escherichia coli (E. coli), other Streptococcus spp, and Coagulase-Negative Staphylococci (CNS), followed at 12.6, 7.6, 6.0, and 5.5 percent, respectively. The rest pathogens include Pseudomonas spp., Enterococcus faecalis, Enterobacter spp., Bacillus spp., Citrobacter spp., Pasteurella spp., Proteus spp., and Staphylococcus aureus were isolated with less than 1% each. To the best of our knowledge, this is the first report for detecting M. bovis in mastitis milk samples in Vietnam, where 35 out of 1,422 samples submitted in 2022 were M. bovis positive. Moreover, of the 35 positive cases, 11 cases had only M. bovis, while 24 cases had M. bovis and other pathogens, including 13 Enterobacteriaceae, 7 Streptococcus spp., and 4 CNS.

TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows

Abstract. The growing number and growing size of wind energy projects coupled with the rapid growth in high-performance computing technology are driving researchers toward conducting large-scale simulations of the flow field surrounding entire wind farms. This requires highly parallel-efficient tools, given the large number of degrees of freedom involved in such simulations, and yields valuable insights into farm-scale physical phenomena, such as gravity wave interaction with the wind farm and farm–farm wake interactions. In the current study, we introduce the open-source, finite-volume, large-eddy simulation (LES) code TOSCA (Toolbox fOr Stratified Convective Atmospheres) and demonstrate its capabilities by simulating the flow around a finite-size wind farm immersed in a shallow, conventionally neutral boundary layer (CNBL), ultimately assessing gravity-wave-induced blockage effects. Turbulent inflow conditions are generated using a new hybrid off-line–concurrent-precursor method. Velocity is forced with a novel pressure controller that allows us to prescribe a desired average hub-height wind speed while avoiding inertial oscillations above the atmospheric boundary layer (ABL) caused by the Coriolis force, a known problem in wind farm LES studies. Moreover, to eliminate the dependency of the potential-temperature profile evolution on the code architecture observed in previous studies, we introduce a method that allows us to maintain the mean potential-temperature profile constant throughout the precursor simulation. Furthermore, we highlight that different codes do not predict the same velocity inside the boundary layer under geostrophic forcing owing to their intrinsically different numerical dissipation. The proposed methodology allows us to reduce such spread by ensuring that inflow conditions produced from different codes feature the same hub wind and thermal stratification, regardless of the adopted precursor run time. Finally, validation of actuator line and disk models, CNBL evolution, and velocity profiles inside a periodic wind farm is also presented to assess TOSCA’s ability to model large-scale wind farm flows accurately and with high parallel efficiency.

Impact of Agriculture Corporate Farm Increase Productivity Widespread Destructive Activity on Earth Democracy in India

Agricultural productivity in India can be improved by investing in agricultural research and development, improving irrigation infrastructure, promoting farm mechanization, and strengthening agricultural extension services. Agriculture is the foundation of the civilization, culture and heritage of India. It most commonly refers to corporations that are large-scale farms, market agricultural technologies in particular pesticides, fertilizers, and GMO's, have significant economic and political influence, or some combination of the three. The large-scale corporate agriculture is more efficient than peasant farming in the country. It leads to better allocative efficiency, induces higher private investment in agriculture, and results in higher output, income and exports. During 2021-22, in the countries total exports agricultural exports contributed of to the tune of US $ 50.2 billion with a 20% increase from US $ 41.3 billion in 2020-21. In FY 2023, it is projected that the Indian agriculture sector will grow at the rate 3.5%. Agriculture in India is a complex mosaic of distinct agro-ecosystems, differentiated by climatic, soil, vegetation and other natural features. About half of the Indians derive their livelihood from agriculture and allied activities. Technology and Research Corporate farming enterprises usually invest in research and development of new agricultural technologies, seeds, and farming practices. Farmers who are part of these systems can benefit from access to cutting-edge technology and knowledge that can improve productivity and efficiency. It is one of the oldest systems of the world characterized by its diversity and heterogeneity, unorganized and stressed on account of natural and anthropogenic vagaries from ‘seed to market’. Agriculture is the backbone of the Indian economy, contributing to around 16.5% of India's GDP and employing over 50% of the workforce. The Indian agriculture market is valued at USD 435.9 billion as of 2022 and is expected to reach USD 580.82 billion by 2028, growing at a CAGR of around 4.9% between 2023 and 2028. Historically, stressed natural resources due to unfavourable weather, monsoon and natural calamities resulted in crop failures leading to food shortage that made serious impacts on the civilization. Post-Independence, the Indian agriculture transformed from a food-scarce to food-exporting country primarily due to science led innovations that caused multi-fold increase in the agricultural production from 135 million tons in 1950/51 to over 1300 million tons in 2021/22 in spite of increasing abiotic and biotic stresses and depleting along with deteriorating natural resources. Agriculture is one amongst the few sectors that made progressive growth after independence and marched well with the need of the country. Success in agriculture since Independence has been the backbone of India’s success.

Cumulative effects of offshore wind farms on common guillemots (Uria aalge) in the southern North Sea - climate versus biodiversity?

Governments are under increasing pressure to reduce greenhouse gas emissions, and large-scale wind farms are being developed in marine environments worldwide. However, top predators are strongly affected by environmental change and anthropogenic activities. Common guillemots (Uria aalge, hereafter guillemots), as one of the world’s most numerous seabird species, are prone to interference with offshore wind farms (OWFs). This study assessed the cumulative impacts of all operating OWFs on guillemots in the German North Sea. These estimates were applied to quantify the possible conflicts between guillemot occurrence and current German government plans to implement large-scale OWFs. If OWFs were implemented according to the current maritime spatial plan for the German Exclusive Economic zone, they would cover 13% of the German North Sea. Guillemot numbers peak during autumn, with German North Sea offshore waters hosting approximately 90,000 individuals. Guillemot density in autumn was significantly reduced within a radius of 19.5 km around operating OWFs. Applying this disturbance distance to current installation plans, about 70% of the German North Sea would be affected, and an estimated 68% of guillemots in the German North Sea would experience habitat loss. This highlights the possible threat to guillemots in the southern North Sea if the current German government plans are implemented. The current estimates are highly relevant to decisions regarding marine spatial planning and management recommendations. Such evaluations are essential for developing sustainable scenarios including reducing the human CO2 footprint, whilst also conserving biodiversity.

Big hands holding small hands: The role of new agricultural operating entities in farmland abandonment

It is crucial to address farmland abandonment to achieve Zero Hunger according to the United Nations’ sustainable development goals. However, quantitative research on this topic is relatively lacking. Based on a large sample of panel data from the Chinese Family Database, this study applies a panel Tobit model to examine the relationship between new agricultural operating entities (NAOEs) and farmland abandonment. The results show that NAOEs—professional operators, family farms, and agricultural cooperatives—are negatively and significantly associated with the ratio of abandoned farmland areas. This effect is achieved through channels such as promoting land leasing, providing technical guidance, and facilitating the sale of agricultural products. Additionally, NAOEs benefit more from China’s central and western regions, mountainous areas, villages with better land tenure security, and households with poorer human resource endowments. These findings provide valuable insights for policymakers to ensure food security and promote sustainable development by cultivating various large-scale farming entities.

Food estate management strategy in Humbang Hasundutan Regency, North Sumatra Province

Food Estate is the concept of developing agricultural production on a wide scale, as an effort to create national food security by extensifying land as a form of large-scale agriculture that relies on the agricultural industry. Data were obtained from questionnaires through the Purposive sampling method, identification of internal and external factors can be carried out so that a food estate management strategy can be formulated using a SWOT analyst. Based on internal factors, the most important strength is the commitment of off-takers (cooperation, use of certified seeds, production targets, demplot, farm business analysis, and drip irrigation systems) with a score of 0.43, and the use of technology and increasing public knowledge about the horticultural system with a score of 0.43. While the variable that has the highest weakness is that land cannot be traded with a score of 0.42. The results of an external analysis obtained the opportunity variable with the highest value, namely the production cost borne by the off taker, and the agreed crop price between the off taker and farmers with a score of 0.44, and then external variables in the form of threats, namely pests and high rainfall with a score value of 0.42.

Envisioning prairie agroecology: Farmer visual constructions of place-based ecological agriculture in Canada

Climate change is driving new challenges for farmers around the world, provoking a sense of urgency in the search for on-farm solutions and pathways to sustainable agricultural systems. While agroecology has gained recognition as a promising option for climate resilience, it remains nascent in the Canadian Prairies—a significant agricultural region dominated by large-scale conventional agriculture. Amid the sprawling fields of grains and oilseeds primarily destined for export markets, growing farm consolidation, and increased land concentration, farmers committed to ecological principles are building a prefigurative foundation for prairie agroecology. Far from cohering into a unified definition, this article captures what agroecology on the Prairies looks like. Through a community-engaged research design and in consultation with the National Farmers Union of Canada, we explore farmers’ reflections on agroecology and provide a participatory platform for farmer-led constructions of agroecology. Through dialogue with 19 farmers of different farm sizes and production types that began in the 2021 farming season—one marked by drought and extreme heat—we use photovoice, visual elicitation interviews, and participatory video to document and mobilize visual constructions of an emerging prairie agroecology. Despite a shared vision among the farmer participants about the need for a sustainability transition through diversified farming practices, three key questions emerged about the future of prairie agroecology: (1) Can prairie agroecology scale up and out, and if so, under what conditions? (2) To what extent is prairie agroecology possible without dense, place-based social networks? and (3) What are the possibilities for prairie agroecology when the future of farming in the region is uncertain, especially given the changing farmer demographics and Canada’s settler colonial context? We explore these questions and provide reflections on key issues confronting the future of prairie agroecology.