Efficient Farm Management Research Articles

The Early Prediction of Kimchi Cabbage Heights Using Drone Imagery and the Long Short-Term Memory (LSTM) Model

Accurate and timely crop growth prediction is crucial for efficient farm management and food security, particularly given challenges like labor shortages and climate change. This study presents a novel method for the early prediction of Kimchi cabbage heights using drone imagery and a long short-term memory (LSTM) model. High-resolution drone images were used to generate a canopy height model (CHM) for estimating plant heights at various growth stages. Missing height data were interpolated using a logistic growth curve, and an LSTM model was trained on this time series data to predict the final height at harvest well before the actual harvest date. The model trained on data from 44 days after planting (DAPs) demonstrated the highest accuracy (R2 = 0.83, MAE = 2.48 cm, and RMSE = 3.26 cm). Color-coded maps visualizing the predicted Kimchi cabbage heights revealed distinct growth patterns between different soil types, highlighting the model’s potential for site-specific management. Considering the trade-off between accuracy and prediction timing, the model trained on DAP 36 data (MAE = 2.77 cm) was deemed most suitable for practical applications, enabling timely interventions in cultivation management. This research demonstrates the feasibility and effectiveness of integrating drone imagery, logistic growth curves, and LSTM models for the early and accurate prediction of Kimchi cabbage heights, facilitating data-driven decision-making in precision agriculture for improved crop management and yield optimization.

Role of Artificial Intelligence in Vegetable Production: A Review

Role of Artificial Intelligence (AI) in vegetable production, emphasizing its potential to address critical challenges such as climate change, population growth, and resource scarcity. AI technologies, including machine learning, computer vision, and robotics, are revolutionizing agricultural practices. AI-driven innovations in crop management, pest control, and soil analysis enhance productivity, reduce labour costs, and ensure sustainable farming practices. Notable advancements include precision spraying by Blue River Technology, significantly reducing herbicide use, and deploying autonomous tractors and drones for efficient farm management. AI applications, such as PEAT's Plantix and Trace Genomics, provide accurate diagnostics for soil health and pest management. Satellite-based solutions like Farm Shots and aWhere offer real-time crop monitoring and weather prediction, optimizing resource use and mitigating risks. The review highlights the importance of making AI technologies more affordable and accessible to farmers, particularly in developing regions. Collaboration between researchers, industry stakeholders, and policymakers is crucial to harness AI's full potential in agriculture. As AI continues to evolve, its integration into vegetable production promises a more efficient, resilient, and sustainable agricultural sector, contributing to global food security and environmental preservation. The aim of the study is to evaluate the impact and effectiveness of Artificial Intelligence (AI) in vegetable production, focusing on how AI technologies enhance productivity, efficiency, and sustainability. The objectives are to assess current AI applications, analyze their benefits and challenges, and provide recommendations for future improvements and wider adoption in the agricultural sector. The research methodology for the study on the role of Artificial Intelligence in vegetable production involves a comprehensive literature review of existing AI technologies and their applications in agriculture, coupled with the analysis of case studies to evaluate real-world implementations. Additionally, expert interviews and surveys with farmers and industry professionals will be conducted to gather insights on the benefits, challenges, and future potential of AI in this sector. The theoretical implications of the study on the role of Artificial Intelligence in vegetable production include advancing the understanding of AI's capabilities in agricultural optimization and contributing to the academic discourse on sustainable farming practices. Practically, the study provides actionable insights for farmers and agribusinesses on implementing AI technologies to enhance crop yields, reduce resource wastage, and improve overall farm management efficiency.

IoT-based Tracking Cattle Health Monitoring System Using Wireless Sensors

Maintaining the health of dairy cows is essential to raising the production of dairy products worldwide. Due to the numerous health problems that cows face, the unpredictability of disease outbreaks, and the high cost of producing new animals, dairy farmers are beginning to lose faith in the industry. To increase milk production, producers must thus employ effective technological methods for monitoring the health of their cows. This research examined many automated wireless sensor-based dairy cow health monitoring systems. The primary goal of smart surveillance systems based on wireless sensor networks (WSNs) in agricultural optimization is to continuously monitor the health of dairy cows. For interested farmers to keep an eye on their cattle's whereabouts throughout the day from many locations, this monitoring device needs to be deployed in both local and remote agricultural areas. A database would hold the information gathered by the automated system. Farmers can then use farm automation to acquire data so they can implement efficient farm management practices. Moreover, WSN is a low-cost tool designed specifically to detect diseases in dairy cows. This technological advancement in agricultural automation would help increase output by lowering the need for human intervention. This page provides an overview of all livestock tracking methods along with a summary of the problems and difficulties they face

Recent advances of application of optical imaging techniques for disease detection in fruits and vegetables: A review

Fruits and vegetables are among the agricultural products that enjoy a high demand in the market. However, the most critical challenge in the production of fruits and vegetables is disease infections which can lead to economic loss. The current assessment method of disease infection still relies on conventional methods and laboratory analysis. The conventional methods although simple and easy are laborious and have less accuracy while laboratory analysis is costly and time-consuming. Over the last two decades, optical imaging techniques have emerged and are gaining interest in the agricultural and food industries. These techniques offer easy, rapid, accurate, reliable as well as user-friendly sensing tools for the identification of quality and disease infection in agricultural products. The fundamental aspect in the techniques is the interaction of light with the tissue and the manipulation of light when interacting with a surface. The integration of edge computing along with artificial intelligence has also reshaped the technology to another level. The use of other agricultural technologies such as drones or robots enables real-time monitoring and efficient farm management. Thus, this paper reviews recent advances in the application of optical imaging techniques for disease detection in fruits and vegetables. This includes computer vision, multispectral imaging, hyperspectral imaging, biospeckle and thermal imaging. The feasibility of the optical imaging techniques is discussed. Challenges and considerations for future research are also highlighted. This review provides a new insight into the recent application of optical imaging which is not only beneficial for the agricultural and food industries but also to other relevant industries.

Fuzzy K-Means and Principal Component Analysis for Classifying Soil Properties for Efficient Farm Management and Maintaining Soil Health

Soil health indicators can guide soil management-related decisions for sustainable agriculture. Principle component (PC) analysis and the fuzzy k-means technique, also known as continuous classification, are useful for designing site-specific management strategies for varying soil properties within a contiguous area. The objective of this study was to identify appropriate soil health indicators as well as to create contiguous areas for precision management of a large diverse farm from measured soil properties. From the farm, which is sited on Armijo–Harkey soil, 286 loose and intact samples were obtained, representing a depth of 15 cm from the soil surface. Statistical analysis showed that several data were log-normally distributed. PCA analysis showed that the first three PCs explained 73% of the variation with PC1, consisting of factors related to the soil’s physical condition; PC2, containing factors related to chemical properties; and PC3, including factors related to macro- and micro-porosities. Minimizing the fuzziness performance index (FPI) and modified partition entropy (MPE) delineated four management classes. The membership class maps showed that the contrasting management strategies could be developed for the four management zones to achieve yield goals while conserving scarce surface water for irrigation, increasing water use efficiency, and decreasing nitrate leaching in arid and semi-arid irrigated farmlands.

Farmers’ usage of information and communication technologies for integrated farm management: An analytical study focusing on rural households of Bangladesh

Agricultural farming consists of multiple components and information related to farm management is one of the crucial factors. Information and communication technologies (ICTs) provide reliable and need based information in an effective way. This study focuses on investigating the extent of ICTs usage for integrated farm management and to clarify the underlying factors in Gauripur upazila (sub-district) under Mymensingh district of Bangladesh. To achieve the purpose of the study, data were collected from 75 randomly sampled farmers using structured questionnaire. Both descriptive and inferential statistical techniques were applied to obtain the findings of the study. The findings of the study clearly revealed that the highest proportion of the farmers (46%) had the tendency to use ICTs to a moderate extent. The analytical findings from the study showed that among the socio-demographic attributes annual income, training received, access to information sources and knowledge level were significantly associated with the usage of ICTs by the farmers. The step-wise multiple regression analysis indicated that knowledge on ICT usage, annual income and access to information sources were the most influential factors affecting the usage of ICTs by the farmers. The policy implications of the study suggests paying more attention to increase the operational knowledge and access to ICTs with provision to hands-on training and economic incentives for rural poor farmers. The integrated approach combining both public and private sectors can be effective to improve the extent of ICT usage for efficient farm management.

EFFECT OF CONTRACT FARMING ON SMALLHOLDER FARMERS' GREEN LEAF TEA PRODUCTION IN TANZANIA

This paper investigates the impact of contract farming on green leaf tea output among Tanzanian smallholder farmers. Data from 393 growers from Mbeya and Njombe regions were collected in a cross-sectional survey and analysed descriptively using IBM Statistics Version 26. A multiple linear regression model was used to test the null hypothesis. The findings show that contract farming engagement (β=140.102; P=0.058) positively impact production. Moreover, household size (β=2.268; P=0.903) and gender (β=294.978; P=0.000) positively impact green leaf tea production. Besides, age (β=-2.719), education (β=-3171.868), and farm size (β=-20.866) all negatively impact production, but education only was statistically significant at P=0.002. We conclude that, contract farming has a favourable impact on green leaf tea production and suggests recognising its potential for farmers’ growth. Besides, its nuanced importance and borderline P value (0.058) prompts further research on contract design, capacity-building, and market dynamics. Additionally, this paper highlights nuanced effects of farmer attributes. While household size and gender positively influences production, age, education, and larger farms negatively impact it. A comprehensive approach to tea production optimization, considering age-appropriate practices, education-specific interventions, and efficient farm management, is crucial. Further investigation into the combined effects of age, education, and gender is suggested for holistic insights.

Factors Affecting the Acceptance of Dairy Farming Standards by Modernized Farmers for Agricultural Extension in Thailand

Milk production in compliance with dairy farm standards (DFS) assists farmers in improving the quality of local raw milk, facilitating more efficient farm management, and potentially increasing raw milk prices. In addition, it helps consumers to consume milk that is safe for their health. This study aimed to investigate the factors affecting the acceptance of DFS among modernized farmers in Sakon Nakhon and Udon Thani, Thailand. A total of 201 respondents were selected using the cluster sampling method. A structured questionnaire was utilized to collect data. The data were analyzed using binary logistic regression with 12 variables: age, education, experience in dairy farming, types of labor, size of dairy farming land, farm size, amount of raw milk yields, processing cost of raw milk, the profitability of raw milk production, access to and use of information on dairy farming, number of attended dairy farming workshops, and farmer satisfaction towards their dairy cooperative administration. The results indicate that modernized dairy farmers with more experience, medium-sized farms (21-100 dairy cows/farm), combined family and hired labor, and higher raw milk yield were more willing to accept DFS. This result is relevant in the context of agricultural extension according to government policies to raise the quality of raw milk in Thailand.

Sustainability of agroecosystems by indices: A comparative study between indicators of ecological footprint sustainability and emergy analysis; a case study in Dez catchment, Iran

Efficient farm management is one of the most important issues in achieving Sustainable performance. Ecological Footprint (EF) measurement and Emergy Analysis (EA) are appropriate tools to achieve the principles of sustainability in different ecosystems. This study aimed to compare the indicators and determine the ecological sustainability of two cropping systems of irrigated wheat and grain maize using ecological footprint evaluation and emergy analysis in the catchment area of Dez in Iran in the crop year 2019–2020. For this purpose, a total of 400 questionnaires from wheat and maize farmers were collected based on a simple random sampling method. The results of evaluating the ecological footprint method of wheat and maize cultivation in the study area at the level of one hectare were 3.50 and 4.66 global hectares, respectively. Also, the results of total emergy inputs for the ecological system of wheat and maize production were 1.60E+16 and 2.55E+16 sej ha−1 per year, respectively. This study showed that the two methods EF and EA are complementary. The evaluation results of both sustainability indices were very close to each other so both cultivation systems were environmentally unsustainable. We also found that the ecosystem of the irrigated wheat production system is more desirable than the ecosystem of the maize production system in terms of yield, renewability, and environmental sustainability. Since both cropping systems are strongly affected by the purchased inputs (more than 95%), as a result, they create a high environmental pressure, which shows the apprehensive condition of the current agricultural production systems in Iran. This study is the first study from Iran that uses these two tools simultaneously to determine the sustainability assessment of two strategic products of the region.

Simulating Spring Barley Yield under Moderate Input Management System in Poland

In recent years, forecasting has become particularly important as all areas of economic life are subject to very dynamic changes. In the case of agriculture, forecasting is an essential element of effective and efficient farm management. Factors affecting crop yields, such as soil, weather, and farm management, are complex and investigations into the relation between these variables are crucial for agricultural studies and decision-making related to crop monitoring, with special emphasis for climate change. Because of this, the aim of this study was to create a spring barley yield prediction model, as a part of the Advisory Support platform in the form of application for Polish agriculture under a moderate input management system. As a representative sample, 20 barley varieties, evaluated under 13 environments representative for Polish conditions, were used. To create yield potential model data for the genotype (G), environment (E), and management (M) were collected over 3 years. The model developed using Multiple Linear Regression (MLR) simulated barley yields with high goodness of fit to the measured data across three years of evaluation. On average, the precision of the cultivar yielding forecast (expressed as a percentage), based on the independent traits, was 78.60% (Model F-statistic: 102.55***) and the range, depending of the variety, was 89.10% (Model F-statistic: 19.26***)–74.60% (Model F-statistic: 6.88***). The model developed using Multiple Linear Regression (MLR) simulated barley yields with high goodness of fit to the measured data across three years of evaluation. It was possible to observe a large differentiation for the response to agroclimatic or soil factors. Under Polish conditions, ten traits have a similar effect (in the prediction model, they have the same sign: + or -) on the yield of almost all varieties (from 17 to 20). Traits that negatively affected final yield were: lodging tendency for 18 varieties (18-), sum of rainfall in January for 19 varieties (19-), and April for 17 varieties (17-). However, the sum of rainfall in February positively affected the final yield for 20 varieties (20+). Average monthly ground temperature in March positively affected final yield for 17 varieties (17+). The average air temperature in March negatively affected final yield for 18 varieties (18-) and for 17 varieties in June (17-). In total, the level of N + P + K fertilization negatively affected the final yield for 15 varieties (15-), but N sum fertilization significantly positively affected final yield for 15 varieties (15+). Soil complex positively influenced the final yield of this crop. In the group of diseases, resistance to powdery mildew and rhynchosporium significantly decreased the final yield. For Polish conditions, it is a complex model for prediction of variety in the yield, including its genetic potential.

Application of Information Technology in the Agriculture Sector of Indian Economy

Agriculture is one of the most important sectors in India and the pillar of the Indian economy. Now it demands the transfer of technologies from lab to land for the second green revolution. Particularly for small and marginal farmers the generation, invention, and application of agricultural knowledge are progressively important, who need relevant information to improve, sustain, and diversify their farm enterprise. Information technology supports new methods and ideas for healthy and precision agriculture. Information technology has the potential tool for improving decision-making in agriculture. IT can improve farm management and farming technologies by efficient farm management. The extensive use of IT to make a direct contribution to agricultural productivity. Satellite technology, geographic information systems using the techniques of agronomy and soil science is to increase agricultural production. In this paper, we study how information technology is helpful for management, improving planning and superior produce of agro products, and the basic issues of information technology in farm practice.

EmbeddedPigCount: Pig Counting with Video Object Detection and Tracking on an Embedded Board.

Knowing the number of pigs on a large-scale pig farm is an important issue for efficient farm management. However, counting the number of pigs accurately is difficult for humans because pigs do not obediently stop or slow down for counting. In this study, we propose a camera-based automatic method to count the number of pigs passing through a counting zone. That is, using a camera in a hallway, our deep-learning-based video object detection and tracking method analyzes video streams and counts the number of pigs passing through the counting zone. Furthermore, to execute the counting method in real time on a low-cost embedded board, we consider the tradeoff between accuracy and execution time, which has not yet been reported for pig counting. Our experimental results on an NVIDIA Jetson Nano embedded board show that this “light-weight” method is effective for counting the passing-through pigs, in terms of both accuracy (i.e., 99.44%) and execution time (i.e., real-time execution), even when some pigs pass through the counting zone back and forth.

A missing piece of the puzzle of on-farm freshwater restoration: What motivates land managers to record and report land management actions?

Worldwide, progress has been made toward managing productive lands more sustainably to improve freshwater health. However, a lack of national guidance for environmental reporting and recording means that it is not possible to quantify consistently which land management actions that help improve water quality have been implemented, where, when, and to what extent. This situation suggests that information on the effectiveness of these actions is missing or fragmented. Systematic recording and reporting of land management actions is an important piece of a large freshwater restoration puzzle. We investigated what motivates New Zealand land managers to record their actions and report them to their networks by conducting 23 semi-structured interviews. Between February and November 2020, we spoke with food producers, New Zealand Indigenous people of the land <em>tāngata whenua</em> community members, and government and industry representatives. The key themes that described motivators for these land managers to record and report land management actions were collective engagement (e.g., working with catchment care groups), identity and social norms (e.g., being a “socially approved” farmer), and efficient farm management (e.g., using one simple recording tool for multiple purposes to save time). While these findings will be broadly germane to international contexts, they are being used specifically to inform the development of a proposed National Register of Land Management Actions in Aotearoa New Zealand.

Artificial Neural Networks in Agriculture

Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple processing operations take place. The spectrum of neural networks application is very wide, and it also includes agriculture. Artificial neural networks are increasingly used by food producers at every stage of agricultural production and in efficient farm management. Examples of their applications include: forecasting of production effects in agriculture on the basis of a wide range of independent variables, verification of diseases and pests, intelligent weed control, and classification of the quality of harvested crops. Artificial intelligence methods support decision-making systems in agriculture, help optimize storage and transport processes, and make it possible to predict the costs incurred depending on the chosen direction of management. The inclusion of machine learning methods in the “life cycle of a farm” requires handling large amounts of data collected during the entire growing season and having the appropriate software. Currently, the visible development of precision farming and digital agriculture is causing more and more farms to turn to tools based on artificial intelligence. The purpose of this Special Issue was to publish high-quality research and review papers that cover the application of various types of artificial neural networks in solving relevant tasks and problems of widely defined agriculture.

Donkey Milk Production Technology and Efficient Farm Management in Georgia

Millions of people in today's world consume milk and dairy products, which are not only an important source of food for these people, but also a source of livelihood for farmers and processors. But to achieve this, consumers, dairy farms and processors need up-to-date the information on how the milk and dairy products can contribute to healthy human nutrition and how the dairy industry is evolving. Keywords: milk, food, farm management, economy.

The use of Landsat-8 and Sentinel-2 imageries in detecting and mapping rubber trees

Information on rubber tree (Hevea brasiliensis) areas and stages of rubber tree growth is needed in making decisions to maximise land use and for efficient farm management. The use of conventional methods in collecting this information requires a long time, high costs, and constraints to access certain areas. Therefore, this study was conducted to evaluate Landsat-8 OLI and Sentinel-2 images in detecting and mapping the rubber tree area. This study presents a pixel-based supervised classification approach to obtain an accurate map of land cover and rubber tree growth stage distribution using resampled 10 m spatial resolution of Sentinel-2 and pansharpened 15 m Landsat-8 OLI. Seven land cover classes (bare soil, water, mature rubber, immature rubber, oil palm, forest, and built-up area) were classified using support vector machine (SVM), artificial neural network (ANN) and spectral angle mapper (SAM). The results showed that the highest classification accuracy was obtained using SVM, 87.22% for Sentinel-2 and 85.74% for Landsat-8. Next, the classification accuracies of ANN were almost similar with 86.17% and 82.39% for Sentinel-2 and Landsat-8, respectively. SAM has produced less than 60% of acceptable accuracy for both datasets. The performance of the aforementioned classifiers was statistically tested using a McNemar test. The test showed that the p-value between SVM and ANN was not significant and thus, ANN and SVM produced similar accuracies and outperformed SAM for both cases. In this study, the best output produced via SVM from Sentinel-2 was selected to produce the thematic map due to the spatial accuracy advantage of Sentinel-2 compared to Landsat-8. The calculated areas of immature and mature rubber from the thematic map were 7.79 km2 and 10.93 km2, respectively, which then used to estimate the number of tappers needed for the management of rubber. It is concluded that the Sentinel-2 Multispectral Instrument (MSI) data can be recommended to be used in rubber cultivation area assessment.

Prediction of Biomass and N Fixation of Legume-Grass Mixtures Using Sensor Fusion.

European farmers and especially organic farmers rely on legume–grass mixtures in their crop rotation as an organic nitrogen (N) source, as legumes can fix atmospheric N, which is the most important element for plant growth. Furthermore, legume–grass serves as valuable fodder for livestock and biogas plants. Therefore, information about aboveground biomass and N fixation (NFix) is crucial for efficient farm management decisions on the field level. Remote sensing, as a non-destructive and fast technique, provides different methods to quantify plant trait parameters. In our study, high-density point clouds, derived from terrestrial laser scanning (TLS), in combination with unmanned aerial vehicle-based multispectral (MS) data, were collected to receive information about three plant trait parameters (fresh and dry matter, nitrogen fixation) in two legume–grass mixtures. Several crop surface height metrics based on TLS and vegetation indices based on the four MS bands (green, red, red edge, and near-infrared) were calculated. Furthermore, eight texture features based on mean crop surface height and the four MS bands were generated to measure horizontal spatial heterogeneity. The aim of this multi-temporal study over two vegetation periods was to create estimation models based on biomass and N fixation for two legume–grass mixtures by sensor fusion, a combination of both sensors. To represent conditions in practical farming, e.g., the varying proportion of legumes, the experiment included pure stands of legume and grass of the mixtures. Sensor fusion of TLS and MS data was found to provide better estimates of biomass and NFix than separate data analysis. The study shows the important role of texture based on MS and point cloud data, which contributed greatly to the estimation model generation. The applied approach offers an interesting method for improvements in precision agriculture.

Technical and Scale Efficiency Analysis of Cash Crops Production in Pakistan: An Application of Data Envelopment Analysis (DEA)

The study has analyzed the technical efficiency of major cash crops' yield in Pakistanfrom 1948 till 2018. Cotton and sugarcane are the crops selected for analysis with an area ofthousand hectares. For carrying out the analysis, data is taken from the Ministry of Food andAgriculture (MINFA), Pakistan Bureau of Statistics, and economic surveys of Pakistan of differentyears. The technique employed in the study is the non-parametric Data Envelopment Analysis(DEA) technique. The result obtained after technical efficiency analysis reveal the suboptimal cashcrop yield in Pakistan throughout the analysis. The average technical efficiency of cotton andsugarcane crops from 1948 to 2018 are approximately 0.80 and 0.84 respectively. Furthermore,the result of scale efficiency analysis showed the monotonous performance of cash crops'production. The consistent variation in cotton and sugar cane crops' yield over the years reportedby the efficiency analysis, accounts for Constant Return to Scale (CRS) and Variable Return toScale (VRS) models, resulted in varying Return to Scale (RTS). The plausible reasons can beattributed to the Increasing Returns to Scale (IRS) or Decreasing Returns to Scale (DRS) in thecase of cotton and sugarcane production. An interesting finding is unveiled that out of 70 years,cotton and sugarcane crops achieved the optimal scale efficiencies for only one year. The bestpossible level of output of cotton and sugarcane crops in the future can be achieved by allocatinga lesser area under the cultivation of these crops in Pakistan. It is also imperative that farmers adoptand implement modern farm technologies. Increasing the area under crop production is not asolution, especially in the wake of alarming population growth and urbanization in Pakistan. Theformulation of policies encouraging farmers requires modern farm inputs to realize the optimalyield of cash crops in Pakistan. The government should educate the farmers regarding advancedfarming and efficient farm management techniques to help to augment the farm output withoutexpansion of the area for these crops

INFORMATION ACQUISITION METHODS AND THEIR RELATION TO THE LEVEL OF EQUIPMENT OF A FARM IN INFORMATIVE INFRASTRUCTURE ELEMENTS

All item flows are always accompanied by information flows. The condition of the goods flow between a sending point and a receipt point is exchange of information between a sender and a consignee. The role of scientific-technical and economic information in modern agriculture grows successively. It is hard to imagine efficient management that would guarantee rational use of the factors of production, including respect for the environment, ensuring high quality of products and satisfaction of market requirements without it. Information is indispensable for both agricultural producers as well as producers and providers of production means and agricultural products consignees. Farms which strive to be competitive and which want to develop must use facilities of modern information technologies. The aim of the paper was to determine information acquisition methods and the relation to the level of equipment and the use of information infrastructure elements on commodity farms. The paper covered 50 agricultural farms located in the south Poland region. Information acquisition methods and the level of equipment and use of information infrastructure elements were determined. Respondents also evaluated which of the information acquisition methods was the best and which was the worst. It was concluded that the level of farm equipment in elements of information infrastructure was satisfactory and the information infrastructure methods guaranteed efficient farm management.

Accounting for data sparsity when forming spatially coherent zones.

Efficient farm management can be aided by the identification of zones in the landscape. These zones can be informed from different measured variables by ensuring a sense of spatial coherence. Forming spatially coherent zones is an established method in the literature, but has been found to perform poorly when data are sparse. In this paper, we describe the different types of data sparsity and investigate how this impacts the performance of established methods. We introduce a set of methodological advances that address these shortcomings to provide a method for forming spatially coherent zones under data sparsity.