Agricultural Production Management Research Articles

Evaluation of Data Sufficiency for Interannual Knowledge Transfer of Crop Type Classification Models

We present a study on the effectiveness of using varying data sizes to transfer crop type classification models from one year to the next, emphasizing the balance between data sufficiency and model accuracy. The significance of crop detection through satellite imaging lies in its potential to enhance agricultural productivity and resource management. Machine learning, particularly techniques like long short-term memory (LSTM) models, has become instrumental in interpreting these satellite data due to its predictive accuracy and adaptability. However, the direct application of models trained in one year to subsequent years poses challenges due to variations in environmental conditions and agricultural practices. Fine-tuning pre-existing models is a prevalent strategy to overcome these temporal discrepancies, though it necessitates a careful evaluation of the quantity and relevance of new data. This study explores the cost–benefit of fine-tuning existing models versus developing new ones based on the quantity of new data, utilizing LSTM models for their transferability and practicality in agricultural applications. Experiments conducted using satellite data from farms in southern Alberta reveal that smaller datasets, with fewer than 25 fields per class, can effectively fine-tune models for accurate interannual classification, while larger datasets are more conducive to training new models. This poses a key challenge in optimizing data usage for crop classification, straddling the line between data sufficiency and computational efficiency. The findings offer valuable insights for optimizing data use in crop classification, benefiting both academic research and practical agricultural applications.

A systematic map of evidence on the relationship between agricultural production and biodiversity in tropical rainforest areas

BackgroundThe tropical rainforest biome plays a significant role in providing habitats for terrestrial biodiversity and delivering ecosystem service values, contributing to agricultural production. However, the increasing demand for tropical commodities with high economic value threatens this humid ecosystem and its biodiversity. To our knowledge, no studies have systematically mapped the relationship between the impacts of agricultural production on biodiversity and the effects of biodiversity on agricultural production in tropical rainforest areas.MethodsSince we were interested in systematically mapping the evidence measuring the impact of tropical agriculture on biodiversity (Map 1), and the vice versa relations, the influence of biodiversity on tropical agriculture production (Map 2), we developed a respective set of search strings, eligibility criteria, and subsequently performed independent searching, screening, and data coding processes. We searched articles from six peer-reviewed databases and 22 gray literature sources. Articles were screened based on the inclusion criteria at the title, abstract, and full-text levels. Individual articles that passed full-text screening were coded and synthesized to create heatmaps. Selected information of interest was also extracted and visualized in the graphics which were clustered based on the year of publication, geographical distribution, type of rainforest, exposure, outcome, farm commodity, and study comparators.Review findingsTwo heatmaps were generated from a contrasting number of references, with heatmap 1 extracted from 222 studies and heatmap 2 derived from 10 times fewer references (n = 20). In heatmap 1, impacts of land conversion to aboveground biodiversity and wild species and ecosystem functions in natural ecosystems were the most common relationships examined, with 115 articles and 62 articles, respectively. Conversely, heatmap 2 showed evidence that focused predominantly on the examination of the links between the impacts of genetic resource diversity on environmental factors and soil management in tropical agricultural production, with four articles each exploring these relations.ConclusionsThese systematic maps reveal that while studies investigating the impacts of tropical agricultural production on biodiversity were abundant, studies examining the impacts of biodiversity on tropical agricultural production were lacking despite both systematic maps experiencing an increasing trend of publication during 2000–2020. Map 1 emphasized the examination of the effects of land conversion on aboveground biodiversity, and on wild species and ecosystem functions. Map 2 highlighted the influence of crop genetic resources on environmental factors, and on soil management as the most frequently studied. The evidence cluster identified here can be the starting point for further systematic review study (to assess, for example, their cause–effect significance).

Flexible wearable sensors for crop monitoring: a review.

Crops were the main source of human food, which have met the increasingly diversified demand of consumers. Sensors were used to monitor crop phenotypes and environmental information in real time, which will provide a theoretical reference for optimizing crop growth environment, resisting biotic and abiotic stresses, and improve crop yield. Compared with non-contact monitoring methods such as optical imaging and remote sensing, wearable sensing technology had higher time and spatial resolution. However, the existing crop sensors were mainly rigid mechanical structures, which were easy to cause damage to crop organs, and there were still challenges in terms of accuracy and biosafety. Emerging flexible sensors had attracted wide attention in the field of crop phenotype monitoring due to their excellent mechanical properties and biocompatibility. The article introduced the key technologies involved in the preparation of flexible wearable sensors from the aspects of flexible preparation materials and advanced preparation processes. The monitoring function of flexible sensors in crop growth was highlighted, including the monitoring of crop nutrient, physiological, ecological and growth environment information. The monitoring principle, performance together with pros and cons of each sensor were analyzed. Furthermore, the future opportunities and challenges of flexible wearable devices in crop monitoring were discussed in detail from the aspects of new sensing theory, sensing materials, sensing structures, wireless power supply technology and agricultural sensor network, which will provide reference for smart agricultural management system based on crop flexible sensors, and realize efficient management of agricultural production and resources.

Recognition of Plastic Film in Terrain-Fragmented Areas Based on Drone Visible Light Images

In order to meet the growing demand for food and achieve food security development goals, contemporary agriculture increasingly depends on plastic coverings such as agricultural plastic films. The remote sensing-based identification of these plastic films has gradually become a necessary tool for agricultural production management and soil pollution prevention. Addressing the challenges posed by the complex terrain and fragmented land parcels in karst mountainous regions, as well as the frequent presence of cloudy and foggy weather conditions, the extraction efficacy of mulching films is compromised. This study utilized a DJI Mavic 2 Pro UAV to capture visible light images in an area with complex terrain features such as peaks and valleys. A plastic film sample dataset was constructed, and the U-Net deep learning model parameters integrated into ArcGIS Pro were continuously modified and optimized to achieve precise plastic film identification. The results are as follows: (1) Sample quantity significantly affects recognition performance. When the sample size is 800, the accuracy of plastic film extraction notably improves, with area accuracy reaching 91%, a patch quantity accuracy of 96.38%, and an IOU and F1-score of 85.89% and 94.20%, respectively, compared to the precision achieved with a sample size of 300; (2) Different learning rates, batch sizes, and iteration numbers have a certain impact on the training effectiveness of the U-Net model. The most suitable model parameters improved the training effectiveness, with the highest training accuracy achieved at a learning rate of 0.001, a batch size of 10, and 25 iterations; (3) Comparative experiments with the Support Vector Machine (SVM) model validate the suitability of U-Net model parameters and sample datasets for precise identification in rugged terrains with fragmented spatial distribution, particularly in karst mountainous regions. This underscores the applicability of the U-Net model in recognizing plastic film coverings in karst mountainous regions, offering valuable insights for agricultural environmental health assessment and green planting management in farmlands.

Silencing Ditylenchus destructor cathepsin L-like cysteine protease has negative pleiotropic effect on nematode ontogenesis

Ditylenchus destructor is a migratory plant-parasitic nematode that severely harms many agriculturally important crops. The control of this pest is difficult, thus efficient strategies for its management in agricultural production are urgently required. Cathepsin L-like cysteine protease (CPL) is one important protease that has been shown to participate in various physiological and pathological processes. Here we decided to characterize the CPL gene (Dd-cpl-1) from D. destructor. Analysis of Dd-cpl-1 gene showed that Dd-cpl-1 gene contains a signal peptide, an I29 inhibitor domain with ERFNIN and GNFD motifs, and a peptidase C1 domain with four conserved active residues, showing evolutionary conservation with other nematode CPLs. RT-qPCR revealed that Dd-cpl-1 gene displayed high expression in third-stage juveniles (J3s) and female adults. In situ hybridization analysis demonstrated that Dd-cpl-1 was expressed in the digestive system and reproductive organs. Silencing Dd-cpl-1 in 1-cell stage eggs of D. destructor by RNAi resulted in a severely delay in development or even in abortive morphogenesis during embryogenesis. The RNAi-mediated silencing of Dd-cpl-1 in J2s and J3s resulted in a developmental arrest phenotype in J3 stage. In addition, silencing Dd-cpl-1 gene expression in female adults led to a 57.43% decrease in egg production. Finally, Dd-cpl-1 RNAi-treated nematodes showed a significant reduction in host colonization and infection. Overall, our results indicate that Dd-CPL-1 plays multiple roles in D. destructor ontogenesis and could serve as a new potential target for controlling D. destructor.

Vadose Zone Journal Special Section: Soil physics in agricultural production, water resources, and waste management

<i>Vadose Zone Journal</i> Special Section: Soil physics in agricultural production, water resources, and waste management

WSN-Driven Advances in Soil Moisture Estimation: A Machine Learning Approach

Soil moisture estimation is crucial for agricultural productivity and environmental management. This study explores the integration of Wireless Sensor Networks (WSNs) with machine learning (ML) and deep learning (DL) techniques to optimize soil moisture estimation. By combining data from WSN nodes with satellite and climate data, this research aims to enhance the accuracy and resolution of soil moisture estimation, enabling more effective agricultural planning, irrigation management, and environmental monitoring. Five ML models, including linear regression, support vector machines, decision trees, random forests, and long short-term memory networks (LSTM), are evaluated and compared using real-world data from multiple geographical regions, which includes a dataset from NASA’s SMAP project, supplemented by climate data, which employs both active and passive sensors for data collection. The outcomes demonstrate that the LSTM model consistently outperforms other ML algorithms across various evaluation metrics, highlighting the effectiveness of WSN-driven approaches to soil moisture estimation. The study contributes to the advancement of soil moisture monitoring technologies, offering insights into the potential of WSNs combined with ML and DL for sustainable agriculture and environmental management practices.

Higher crop rotational diversity in more simplified agricultural landscapes in Northeastern Germany

ContextBoth crop rotational diversity and landscape diversity are important for ensuring resilient agricultural production and supporting biodiversity and ecosystem services in agricultural landscapes. However, the relationship between crop rotational diversity and landscape diversity is largely understudied.ObjectivesWe aim to assess how crop rotational diversity is spatially organised in relation to soil, climate, and landscape diversity at a regional scale in Brandenburg, Germany.MethodsWe used crop rotational richness, Shannon’s diversity and evenness indices per field per decade (i.e., crop rotational diversity) as a proxy for agricultural diversity and land use and land cover types and habitat types as proxies for landscape diversity. Soil and climate characteristics and geographical positions were used to identify potential drivers of the diversity facets. All spatial information was aggregated at 10 × 10 km resolution, and statistical associations were explored with interpretable machine learning methods.ResultsCrop rotational diversity was associated negatively with landscape diversity metrics and positively with soil quality and the proportion of agricultural land use area, even after accounting for the other variables.ConclusionOur study indicates a spatial trade-off between crop and landscape diversity (competition for space), and crop rotations are more diverse in more simplified landscapes that are used for agriculture with good quality of soil conditions. The respective strategies and targets should be tailored to the corresponding local and regional conditions for maintaining or enhancing both crop and landscape diversity jointly to gain their synergistic positive impacts on agricultural production and ecosystem management.

Willingness to adopt precision agriculture: An analysis of Gombe and Bauchi States of Nigeria

Adoption of Precision Agriculture (PA) technologies has apparent benefits for agricultural production management, cost savings, and environmental sustainability. However, these technologies are also extremely complicated and influenced by various social and economic forces, which impact their adoption rate, particularly in developing nations. Demographic and agricultural characteristics also play an important role in determining willingness to adopt PA. This study is a survey aimed at establishing the willingness indices of farmers and other relevant personnel to embrace precision farming technologies in Gombe and Bauchi states of Nigeria. The survey included individuals of varied ages; but an average age of 28.5 years for Gombe and 36.5 years for Bauchi, educational levels, and employment statuses, as well as agricultural and operational features. The willingness to adopt PA is high in both states, with 92% in Gombe and 96% in Bauchi. The findings suggest that, while high costs and a lack of technical know-how had a negative impact on readiness to adopt PA in both states, the level of complexity was rated higher by the older generation in Bauchi state, which had a comparatively high mean farmer age. The size of the farm and the number of employees had no direct influence on the adoption of PA technology. Other criteria, such as age and number of years of operation, indicate overlap between the various stages of internet and digital technology adoption, mechanization, and PA. The high willingness rating indicates that farmers are willing to adopt PA technologies if made accessible. It is therefore advised that legislative tools, training programs, and necessary access to financing facilities be employed to make these technologies available and cheap for farmers.

Economic Analysis of Forest Management for Timber Production and Agricultural Production in Hillside Watershed Areas of Madagascar: A Case Study of the Itasy Region

In hillside watersheds, forests play a crucial role in protecting against soil erosion. Not only does forest vegetation cover serve as a biological measure to prevent soil erosion, it also generates income through sustainable management practices. However, there is a scarcity of research that evaluates and compares the profitability of perennial woody crops with that of other agricultural crops by integrating biological aspects within an economic analytical framework in Central Madagascar. In this study, we address this gap by combining a biological growth model that captures complex forest dynamics with economic management data to utilize the discounted cash flow method. We applied this approach to evaluate and compare the profitability of forest management and agricultural crop production in the hillside watershed areas of Central Madagascar, which have experienced severe human-induced soil erosion. Although our results showed that timber production is a profitable option, other agricultural crops generate much higher profits, indicating that financial subsidies alone may not be enough to shift land use toward perennial woody crops. In addition, growing fruit trees such as oranges and mangoes appears to be a much more financially attractive option for local farmers than annual agricultural crop production.

EFFECT OF OKORO [(Albizia zygia (J.F.) Macbr)] LEAFY BIOMASS AND NPK FERTILISER ON THE GROWTH AND YIELD OF SWEET PEPPER (Capsicum annum L.)

Agricultural productivity and sustainable crop management are pivotal aspects of global food security and economic stability. The quest for efficient and environmentally friendly practices in crop production has led to a surge in research exploring the utilisation of organic materials alongside conventional fertilisers to enhance crop growth and yield. An experiment was conducted over a growing period of three months, starting in August 2023 at the research field of Kwame Nkrumah University of Science and Technology, Kumasi-Ghana, to investigate the effects of Albizia zygia leafy biomass in combination with NPK fertiliser on the growth and yield of sweet pepper (Capsicum annum L.). A randomised complete block design was employed with four treatment groups replicated four times, including various combinations of A. zygia leafy biomass and NPK fertiliser, alongside control groups of sole fertiliser application and untreated plots (T1). The treatments were as follows: T1 = No biomass, no NPK (control); T2 = 0.5348 kg of A. zygia leafy biomass per 4.86 m2 (1100.5 kg/ha of A. zygia leafy biomass); T3 = 0.2463 kg of NPK per 4.86 m2 (506.7 kg/ha of NPK); and T4 = 0.2674 kg of A. zygia leafy biomass per 4.86 m2 + 0.1231 kg of NPK (550.3 kg/ha of A. zygia leafy biomass + 253.4 kg/ha of NPK). The assessed parameters were plant height, number of leaves, number of branches, fruit yield and fresh fruit weight. Significant variations (p ˂ 0.05) were observed in the growth parameters (plant height, number of leaves, number of branches) and yield attributes (fruit yield and fresh fruit weight) of sweet pepper among the treatments. The combined application of A. zygia leafy biomass and NPK fertiliser (T4) exhibited increased effects on plant height, number of leaves, number of branches, fruit yield and fresh fruit weight compared to individual treatments and control groups (p ˂ 0.05). Notably, the treatment combining A. zygia leafy biomass and NPK fertiliser (T4) demonstrated a substantial increase in plant height (44.58 cm), a significant improvement in the number of leaves (42.98) and a remarkable enhancement in the number of branches (11.33) compared to the sole applications and the control group. Furthermore, the combined treatment significantly increased (p ˂ 0.05) the yield parameters, including fruit yield and fresh fruit weight per hectare, showcasing an increase of 8796/ha and 23.91 t/ha, respectively, compared to sole fertiliser application and the control. The findings from this study demonstrated that the incorporation of A. zygia leafy biomass, either solely or in combination with NPK fertiliser, positively impacted the growth and yield of sweet pepper plants. These results highlight the potential of A. zygia leafy biomass as a valuable organic amendment for sustainable and enhanced crop production, offering promising implications for agricultural practices aimed at improving yield and plant growth.

Evaluating the Effectiveness of Agricultural Extension Methods in Logar Province, Afghanistan

Agricultural extension can accelerate the transfer and exchange of information that becomes applied knowledge, improve the quantity and quality of agricultural products, enhance the competitiveness of farmers using new technology and information, improve the production and marketing management of agricultural products are the result of a better chosen method of extension. The present study aimed to determine the effectiveness of individual, group and mass communication methods of agricultural extension in Pol-e-Alam city. The research is applied in terms of purpose. Primary data was used for conducting research. The data was collected by survey using a questionnaire, in a simple random sampling in 2023. The sample size was 80 persons. SPSS 21 was used for data analyze. The findings showed that group methods (41.25%) had the highest, individual methods (35.0%) and mass communication methods (23.75%) had the least effectiveness. In the group methods, results exhibition (28.75%) with the highest value, the method's exhibition (23.75%), Group Discussion (21.25%), Small Group Training (13.75%) and Observational tours (12.5%) with the lowest amount by respondents were considered effective. In the individual methods, farm visits (77.50%) were in first and telephone calls (3.75%) were in fifth order. In the mass communication methods, the demonstrations and campaigns (45.0%) with the highest weight and the TV (5%) with the lowest weight were selected by the respondents.

Effect of Texture Feature Distribution on Agriculture Field Type Classification with Multitemporal UAV RGB Images

Identifying farmland use has long been an important topic in large-scale agricultural production management. This study used multi-temporal visible RGB images taken from agricultural areas in Taiwan by UAV to build a model for classifying field types. We combined color and texture features to extract more information from RGB images. The vectorized gray-level co-occurrence matrix (GLCMv), instead of the common Haralick feature, was used as texture to improve the classification accuracy. To understand whether changes in the appearance of crops at different times affect image features and classification, this study designed a labeling method that combines image acquisition times and land use type to observe it. The Extreme Gradient Boosting (XGBoost) algorithm was chosen to build the classifier, and two classical algorithms, the Support Vector Machine and Classification and Regression Tree algorithms, were used for comparison. In the testing results, the highest overall accuracy reached 82%, and the best balance accuracy across categories reached 97%. In our comparison, the color feature provides the most information about the classification model and builds the most accurate classifier. If the color feature were used with the GLCMv, the accuracy would improve by about 3%. In contrast, the Haralick feature does not improve the accuracy, indicating that the GLCM itself contains more information that can be used to improve the prediction. It also shows that with combined image acquisition times in the label, the within-group sum of squares can be reduced by 2–31%, and the accuracy can be increased by 1–2% for some categories, showing that the change of crops over time was also an important factor of image features.

Unraveling the spatial-temporal patterns of typhoon impacts on maize during the milk stage in Northeast China in 2020

Typhoons are increasing in inland areas and affecting agriculture under climate change. Assessing the large-scale impact of typhoons on crops is essential for improving agricultural resilience. However, typhoon impacts are typically intricate and uncertain, depending on the different stages of crop growth and the indicators used for assessment. Here, we leveraged satellite data to examine the spatial-temporal patterns of typhoon-induced impacts on maize during the milk stage in Northeast China in 2020. We found that land surface water index (LSWI) showed a significantly higher negative relative change of 161.5% in regions severely affected by typhoons, surpassing that of normalized difference vegetation index (NDVI, 19.7%), near-infrared reflectance of vegetation (NIRV, 31.7%), and enhanced vegetation index (EVI, 25.2%), suggesting the outperformance of LSWI in monitoring the impact of typhoons on maize. Based on LSWI, we revealed the spatial-temporal distribution of typhoon impacts. Specifically, significant impacts on maize growth were observed in central Northeast China, particularly in Heilongjiang (49% of maize fields) and Jilin provinces (53% of maize fields). Furthermore, week-long lagging typhoon impacts on maize growth were generally found but lasted until the end of the maize growing season. We also found the typhoon impacts were partially mitigated by spatial and temporal compensations. Regions with above-average maize growth offset the losses experienced in areas severely affected by typhoons. Additionally, better maize growth in the early stages enhanced its resilience against subsequent typhoon impacts. Despite the mitigating effects on maize production, typhoons may increase the risk of maize mold, which warrants further investigation. Given the projected increase in future climate extremes, this study provides valuable insights into the rapid assessment of typhoon impacts using satellite data and cloud computing and supports decision-making in crop management for sustainable agricultural production.

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management.

How Does Specialization in Agricultural Production Affect Soil Health?

The aim of the study was to assess the impact of the specialization of agricultural production on selected parameters of soil health, i.e., soil organic carbon content (SOC), soil acidification, soil nutrient status, i.e., total nitrogen content (NT), available forms of phosphorus, potassium, and magnesium, and microelements content, as well as the content of selected potentially toxic metals (PTMs). For the study, 18 farms located in the Masovian Voivodeship in Central Poland were selected. They were grouped into six types, and each type was represented by three farms. The study included organic farms; farms specializing in: crop, vegetable, poultry, dairy cattle, and pigs production. A total of 144 soil samples were analyzed. The results showed that the specialization of agricultural production and fertilizer management had a significant impact on most of the tested soil health parameters, except SOC and NT content. Despite the high organic fertilizer doses introduced into soils in poultry (170 kg N per hectare as poultry manure) and pig farms (150 kg N per hectare as pig manure), there was no significant influence of these amendments on SOC content. This may indicate low organic carbon sequestration potential in some Polish agricultural soils. Organic farms had the lowest levels of plant nutrients in the tested soil samples, which may limit soil productivity. All the tested soils were strongly acidified, which could restrict both production and regulatory soil functions. Based on the synthetic index of soil fertility (SSFI), vegetable and poultry farms were characterized by very high fertility, while crop, dairy cattle, and pig farms fell into the medium fertility class. Organic farms were in the lowest fertility class. However, the study suggests that the SSFI may not be the best indicator for assessing soil fertility and health; therefore, further research is needed.

Behavioural drivers of long-term land leasing adoption: application of an extended theory of planned behaviour

AbstractIncreasing agricultural productivity while also protecting the environment, requires careful stewardship of the most important resource in production – land. Most land in Northern Ireland is controlled via owner occupation and granting access to others is a matter for careful consideration. Proprietorial caution, combined with custom and values attributed to land in the region has resulted in a pattern of tenure dominated by short-term rental agreements called conacre. An insecure, short-term land rental system, such as the conacre may be a disincentive to farmers in their decision-making around investing in land management and development, consequently resulting in reduced farm-level sustainability. In this study, we analysed the psychological and behavioural factors influencing farmers’ intention to take up long-term land leasing in Northern Ireland. To achieve our objective, we employed a mixed-methods approach and statistically analysed our quantitative data using an extended theory of planned behaviour (TPB) approach in a structural equation modelling framework. The results of our analyses showed that attitude, perceived behavioural control, subjective norm and perceived risk were statistically significant determinants of farmers’ intention to adopt long-term land leasing. The study results have important implications for the design of effective policies aimed at encouraging long-term land leasing through behavioural change to ensure sustainable agricultural production and effective land management.

Mapping areas suitable for agriculture and their accessibility: The case of the centre region of Cameroon

Given the pressure on land in the Central Region due to economic development and the search for opportunities, the best land for agriculture needs to be identified, so as to preserve it from urbanization, the expansion of mining areas, infrastructure and other occupations. It is in this context that the issue of mapping to identify agricultural zones and assess their accessibility was raised. This is an opportune moment to feed reflections on planning, agricultural production and natural resource management in the Center Region of Cameroon. GIS-based multi-criteria spatial analysis of data on land use, hydrographic network, slopes, soils and their suitability for cultivation provided precise, geolocalized information on land potentially suitable for agriculture in general. Following the analyses, we were able to establish and highlight the areas suitable for agriculture and accessible, which amounted to 8%, or 5662.42 km2 for high-potential and accessible areas. This was followed by 56%, or 37144.95 km2 for medium-potential and accessible zones, and finally 6%, or 4238.93 km2 for low-potential and accessible zones. Once the accessible areas had been removed, 5% or 3028.27 km2 of high-potential inaccessible areas remained, 19%, that is 14639.46 km2 of medium-potential inaccessible areas and 2% that is 967.12km2 of low-potential inaccessible areas.

Safe Farming: Ultrafine Bubble Water Reduces Insect Infestation and Improves Melon Yield and Quality.

Melon pest management relies on the excessive application of pesticides. Reducing pesticide spraying has become a global issue for environmental sustainability and human health. Therefore, developing a new cropping system that is sustainable and eco-friendly is important. This study found that melon seedlings irrigated with ultrafine water containing H2 and O2 (UFW) produced more root hairs, increased shoot height, and produced more flowers than the control irrigated with reverse osmosis (RO) water. Surprisingly, we also discovered that UFW irrigation significantly reduced aphid infestation in melons. Based on cryo-scanning electron microscope (cryo-SEM) observations, UFW treatment enhanced trichome development and prevented aphid infestation. To investigate whether it was H2 or O2 that helped to deter insect infestation, we prepared UF water enrichment of H2 (UF+H2) and O2 (UF+O2) separately and irrigated melons. Cryo-SEM results indicated that both UF+H2 and UF+O2 can increase the density of trichomes in melon leaves and petioles. RT-qPCR showed that UF+H2 significantly increased the gene expression level of the trichome-related gene GLABRA2 (GL2). We planted melons in a plastic greenhouse and irrigated them with ultrafine water enrichment of hydrogen (UF+H2) and oxygen (UF+O2). The SPAD value, photosynthetic parameters, root weight, fruit weight, and fruit sweetness were all better than the control without ultrafine water irrigation. UFW significantly increased trichome development, enhanced insect resistance, and improved fruit traits. This system thus provides useful water management for pest control and sustainable agricultural production.

Revolutionizing Agriculture: The Impact of Automation on Productivity and Efficiency

The complex agricultural environment, together with the need for high levels of productivity, necessitates establishing robust systems that can be efficiently and economically developed. The absence of order and structure in the external environment heightens the probability of experiencing failures. Furthermore, it is often seen that equipment management is entrusted to those with little proficiency in technology. Therefore, the significance of intrinsic safety and reliability becomes a pivotal attribute. The issue of ensuring food safety requires using automated technologies that are both sterilized and reliable to minimize the risk of contamination leakage. This article examines the progress and prospects of automation in the agricultural sector, with a specific emphasis on the use of autonomous equipment, robotics, and artificial intelligence. The article examines the advantages of automation in enhancing the management of agricultural production, minimizing expenses, and achieving objectives related to environmental sustainability. Nevertheless, it is important to acknowledge the complexities associated with automation, as it brings to light several obstacles such as the repercussions on agricultural workers, possible disparities in social and environmental aspects, and the need for more investigation and advancement.