Sustainable Agricultural Development Research Articles

Nanocząstki krzemu w zrównoważonym rolnictwie

The increasing population, shrinking arable land, and climate change prompt the search for new solutions in agriculture. In a sustainable approach, agriculture should be based on improving the quality and quantity of yields while maintaining biodiversity and protecting the natural environment. Nanotechnology, present in many areas of our lives, offers opportunities to support the development of sustainable agriculture on many levels. Among the numerous solutions and nanomaterials, silicon, which is a natural component of the ecosystem, deserves special attention. In its nanoparticle form, it acquires new, unique properties. This article focuses on the significant role of silicon nanoparticles in organic farming, with particular emphasis on their function as nanofertilizers. The authors analyze the impact of silicon nanoparticles on plant growth and development and their potential in mitigating the negative effects of abiotic stress factors caused by drought, salinity, and exposure to metals. Additionally, the beneficial effects of silicon nanoparticles on plants growing under biotic stress conditions induced by microorganisms such as bacteria and fungi are presented. The paper includes a review of original research results conducted in recent years in this area, as well as possible mechanisms and strategies of silicon nanoparticle action at the physiological, cellular, and molecular levels. The issue of the safety of using nanoparticles in agriculture and the prospects for their further use as a factor enhancing the resistance and productivity of crops are also discussed.

Fusion of UAV-Acquired Visible Images and Multispectral Data by Applying Machine-Learning Methods in Crop Classification

The sustainable development of agriculture is closely related to the adoption of precision agriculture techniques, and accurate crop classification is a fundamental aspect of this approach. This study explores the application of machine learning techniques to crop classification by integrating RGB images and multispectral data acquired by UAVs. The study focused on five crops: rice, soybean, red bean, wheat, and corn. To improve classification accuracy, the researchers extracted three key feature sets: band values and vegetation indices, texture features extracted from a grey-scale co-occurrence matrix, and shape features. These features were combined with five machine learning models: random forest (RF), support vector machine (SVM), k-nearest neighbour (KNN) based, classification and regression tree (CART) and artificial neural network (ANN). The results show that the Random Forest model consistently outperforms the other models, with an overall accuracy (OA) of over 97% and a significantly higher Kappa coefficient. Fusion of RGB images and multispectral data improved the accuracy by 1–4% compared to using a single data source. Our feature importance analysis showed that band values and vegetation indices had the greatest impact on classification results. This study provides a comprehensive analysis from feature extraction to model evaluation, identifying the optimal combination of features to improve crop classification and providing valuable insights for advancing precision agriculture through data fusion and machine learning techniques.

Impact of Polyploidy on Crop Improvement and Plant Breeding Strategies: A Review

Polyploidy, the condition of possessing multiple sets of chromosomes, is a widespread phenomenon in plants that has had a profound impact on crop evolution and improvement. This review explores the role of polyploidy in plant breeding strategies, emphasizing its contributions to enhancing agronomic traits, overcoming genetic barriers, and expanding genetic diversity. Polyploid crops, such as wheat, cotton, and Brassica species, exhibit superior yield, increased biomass, and enhanced stress tolerance compared to their diploid counterparts. Polyploidy can arise naturally or be induced artificially, providing breeders with opportunities to create novel crop varieties through synthetic polyploidization. The complexity of polyploid genomes poses significant challenges, including fertility issues, meiotic instability, and difficulties in genome management. Advances in genomic and biotechnological tools, such as genomic selection and CRISPR-based genome editing, are beginning to address these obstacles, allowing for precise manipulation of polyploid genomes and improved breeding outcomes. Additionally, polyploidy plays a crucial role in overcoming reproductive barriers in interspecific hybrids, facilitating the transfer of desirable traits between species that would otherwise be genetically incompatible. The potential of polyploidy for developing climate-resilient crops is particularly noteworthy, as polyploid plants often exhibit greater tolerance to drought, salinity, and extreme temperatures. This makes polyploid breeding a promising approach for addressing the challenges of climate change and ensuring food security. Future research should focus on understanding the genetic and epigenetic mechanisms underlying polyploid genome stability and trait expression, as well as integrating advanced computational tools to predict and manipulate polyploid gene function. Emerging areas such as synthetic biology and multi-omics integration will further enhance our ability to engineer polyploid crops with complex trait architectures. Polyploidy remains a powerful and versatile tool for plant breeders, offering immense potential for crop improvement, genetic innovation, and the development of resilient agricultural systems. As the understanding and technological capabilities in polyploid breeding continue to advance, polyploid crops will play a central role in sustainable agricultural development and global food production.

Precision Agriculture and Water Conservation Strategies for Sustainable Crop Production in Arid Regions

The intensifying challenges posed by global climate change and water scarcity necessitate enhancements in agricultural productivity and sustainability within arid regions. This review synthesizes recent advancements in genetic engineering, molecular breeding, precision agriculture, and innovative water management techniques aimed at improving crop drought resistance, soil health, and overall agricultural efficiency. By examining cutting-edge methodologies, such as CRISPR/Cas9 gene editing, marker-assisted selection (MAS), and omics technologies, we highlight efforts to manipulate drought-responsive genes and consolidate favorable agronomic traits through interdisciplinary innovations. Furthermore, we explore the potential of precision farming technologies, including the Internet of Things (IoT), remote sensing, and smart irrigation systems, to optimize water utilization and facilitate real-time environmental monitoring. The integration of genetic, biotechnological, and agronomic approaches demonstrates a significant potential to enhance crop resilience against abiotic and biotic stressors while improving resource efficiency. Additionally, advanced irrigation systems, along with soil conservation techniques, show promise for maximizing water efficiency and sustaining soil fertility under saline–alkali conditions. This review concludes with recommendations for a further multidisciplinary exploration of genomics, sustainable water management practices, and precision agriculture to ensure long-term food security and sustainable agricultural development in water-limited environments. By providing a comprehensive framework for addressing agricultural challenges in arid regions, we emphasize the urgent need for continued innovation in response to escalating global environmental pressures.

Sustainable Development Strategies and Good Agricultural Practices for Enhancing Agricultural Productivity: Insights and Applicability in Developing Contexts—The Case of Angola

In general, agricultural productivity in Angola is low due to the limited awareness among stakeholders regarding sustainable development strategies (DSs) and good agricultural practices (GAPs) that could be adjusted to local crops, soil types, and climatic conditions. A structured approach was followed to develop a systematic literature review (SLR) that can address this gap by examining how DSs and GAPs may be adapted for Angola’s context to encourage sustainable agricultural development. Key steps included the selection and exclusion of literature from primary scientific databases based on specific screening indicators such as the publication date, language, relevance to DSs and GAPs, and geographic focus on developing or developed nations with comparable agricultural challenges. The initial search resulted in 11,392 articles, of which 4257 met the primary selection criteria. After further screening for relevance and availability, 98 articles were shortlisted, and 15 studies were ultimately included for in-depth analysis. This strict screening process ensured the inclusion of studies most applicable to Angola’s agricultural context. The key research findings indicate that certain DSs and GAPs have high adaptability potential for Angola. The findings emphasise practices such as drip irrigation and inorganic fertilisation, which are widely implemented in both developed and developing countries due to their efficiency in resource-limited environments. Additional strategies, such as water management systems, organic composting, and agroforestry practices, demonstrate significant potential to enhance soil fertility, water efficiency, and crop resilience against climate variability. By identifying these practices and strategies, this study provides a basic framework for policymakers in Angola to develop targeted implementation guidelines, fostering sustainable agricultural growth and resilience in the face of climatic challenges. Thus, this review contributes to the scientific and practical understanding of sustainable agriculture in developing countries, offering critical insights that support Angola’s efforts to achieve greater self-sufficiency and economic stability through sustainable agricultural practices.

Growth Simulation of Agriculturally Dominant Cities by Incorporating Multiple Drivers into a CA-based patch-generating Land Use Simulation Model: A Case Study in Siracusa, Italy

Abstract. Understanding the historical trajectory of land use and land cover (LULC) and predicting future alterations is essential to advancing the SDGs. Current researches on LULC in agriculture-dominated cities primarily focuses on the spatiotemporal pattern analysis of carbon storage changes, with relatively little attention given to LULC simulation studies. Moreover, most studies employing cellular automata (CA) models concentrate on urban centers, overlooking the specific sustainable development of agricultural cities. This paper selects Siracusa, Italy, as a case study, incorporating multiple drivers and constructing four development scenarios. The patch-generating land use simulation (PLUS) model is employed to predict the LULC for Siracusa by 2030 and to examine potential transformations under each scenario. Our findings show that 60% of the land in Siracusa is designated as cropland, predominantly situated in flat regions. Our simulation results reveal that urbanization and demographic growth are the main factors driving cropland conversion to urban areas. Different development scenarios exhibit significant variations in future land use structures; the cropland protection scenario emphasizes the stability and sustainability of agricultural development, whereas the economic development scenario highlights the substantial impact of urban and industrial expansion on agricultural land. These insights are instrumental for land use planning and policy-making in Siracusa and other agriculture-centric cities, providing a framework to guide urbanization while promoting agricultural sustainability.

The Possibility of Environmentally Sustainable Yield and Quality Management of Spring Wheat (Triticum aestivum L.) of the Cornetto Variety When Using Sapropel Extract

Sustainable development is one of the main directions of modern agriculture. First of all, sustainability in the agricultural sector can be achieved through the possible abandonment of traditional mineral fertilizers. Many decades of using these fertilizers have led to the degradation of arable soils and to soil and environmental pollution. As a result, this causes reductions in yields and the environmental quality of agricultural products and affects the health of the population. An alternative to traditional mineral fertilizers may be the use of innovative organomineral fertilizers obtained from local resources. These include manure, humus, compost, sediments, etc. In recent years, fertilizers obtained from the sapropels of the bottom sediments of lakes have become widespread. Their distinctive feature is the environmental friendliness and completeness of the content of chemical elements and substances necessary for the development and growth of plants. In addition, the methods of obtaining and applying these fertilizers allow us to talk about their effectiveness in use. The range of applications of these fertilizers is diverse, from use in the form of a dry extract applied directly to the soil to the use of liquid suspensions used at various stages of processing and from pre-sowing seed treatment to watering and spraying plants at different periods of vegetation. Moreover, an important aspect is the research work on the variational use of sapropel fertilizers on different crops, with different methods of production and concentrations and at different stages of processing. This publication contains the results of a study of the effect of the obtained innovative sapropel fertilizer on productivity, wheat grain quality, and economic efficiency (Triticum aestivum L.). To identify the optimal concentration of sapropel extract, laboratory studies were carried out to determine the germination energy and germination of wheat seeds of different varieties when they were soaked in various concentrations: 0.4, 0.8, 1.2, 1.6, and 2.0 g/L. The best indicators of germination energy and germination of wheat seeds during treatment with the extract were obtained at a concentration of 1.2 g/L. The research was conducted at an accredited variety testing laboratory. A field experiment was conducted in the fields of the agrobiological station of North Kazakhstan University named after Manash Kozybayev. The treatment of the seeds was carried out by soaking them in sapropel extract to evenly distribute the substance. The scheme of the field experiment included the option of using foliar treatment with a solution of sapropel extract at the tillering stage. As a result of the application of the obtained extract in the field, environmental and socio-economic efficiency was noted. The conducted field studies note its positive effect and effectiveness on the morphological, qualitative, and quantitative indicators of the wheat harvest. In the areas where wheat seeds were pretreated, as well as where foliar treatment with the resulting sapropel suspension was carried out, the best yield indicators were revealed. In these variants of the experiment with pre-sowing and pre-sowing and foliar treatment with the sapropel extract solution, the yield was 3.63 and 3.81 tons per hectare, respectively. The introduction of sapropel extract at the stage of seed treatment before sowing, as well as foliar processing of wheat at the tillering stage, will increase the efficiency and profitability of the agricultural industry and obtain a synergistic effect in the form of socio-economic efficiency and environmental safety of production. In our opinion, this will contribute to the development of sustainable agriculture and the production of environmental products.

A Comparative Analysis of the Sustainable Development of Urban Agriculture in Chengdu and Singapore

With the deepening global concern for sustainable development, urban agriculture has received more and more attention as an important combination of urban development and ecological environmental protection. This paper provides a comprehensive evaluation and comparison of two areas, Chengdu and Singapore, in terms of their capacity for sustainable development of urban agriculture through an empirical analysis method. To make a multidimensional evaluation index system, the study uses the hierarchical analysis method (AHP) and the fuzzy comprehensive evaluation method. The index covers important areas like the level of agricultural development, the intensity of applied inputs, the rate of energy use, the rate of resource utilization, and the level of agricultural factors. The results show that Chengdu performs significantly in terms of resource utilization rate and agricultural factor level, while Singapore performs better in terms of energy utilization rate. The study provides targeted recommendations, including strengthening scientific and technological innovation, policy support, and ecological and environmental protection, with the aim of providing references and lessons for the sustainable development of urban agriculture in the two places and other areas around the world.

Climate Smart Agriculture: Innovating Sustainable Practices for a Changing Climate

Climate Smart Agriculture (CSA) is a strategy aimed at lowering greenhouse gas emissions, strengthen climate change resilience, raising crop production and incomes significantly. In a variety of agro-ecological zones, the implementation of climate smart agriculture methods has significantly improved the availability of food, adaptability to fluctuations in the climate, and emissions reduction. One of the key benefits of CSA is its ability to improve agricultural productivity and stabilize food security, particularly for small-scale farmers in developing nations who are most vulnerable to climate change. By integrating sustainable techniques such as conservation agriculture, crop diversification, and agroforestry, CSA helps ensure stable yields, even amid erratic weather patterns. Enhanced soil fertility, enhanced irrigation practices and the use of varieties of crops that are climate resilient all contribute to sustained productivity, mitigating the risks of crop failure and food shortages. CSA also strengthens agricultural systems' resilience to climate impacts by promoting practices like zero tillage, residue management, and efficient irrigation, which improve soil health and water retention, making crops more resistant to droughts and floods. In addition to boosting resilience, CSA contributes to climate change mitigation by reducing agriculture's carbon footprint. Precision farming, organic agriculture, and other low-emission practices help curb greenhouse gas emissions from farming activities. Mobile technology is also emerging as a key driver of CSA by providing real-time information, decision-making support, and access to essential resources, further enhancing the adaptability and sustainability of farming systems. The significance of CSA as a strategic solution to the threats that climate change poses to food security and agricultural sustainability is being more widely acknowledged by governments. As a result, numerous initiatives have been launched at national, regional, and international levels to encourage the widespread adoption of CSA practices. In order to achieve sustainable agricultural development and ensure food security in the face of climate change, CSA must be scaled up around the world.

Proyecto para la creación de un centro de acopio para la asociación agrícola Machete y Garabato en el sector Matilde Esther, cantón Bucay de la provincia del Guayas, periodo 2024

The project is to create a collection center for the Machete y Garabato agricultural association in the Matilde Esther sector, Bucay canton in the province of Guayas, in 2024, to transform the future of cocoa in the region. The general objective is to transform cocoa production and add value to the product. This feasibility study analyzes the conditions to establish a space that allows local farmers to process and market their cocoa efficiently and sustainably. Political, economic, social, technological, and environmental aspects were examined to identify potential opportunities and threats to the project. Additionally, market research was conducted to understand the supply and demand for cocoa products at the local and national levels. The implementation of this collection center will not only benefit farmers but also promote sustainable agricultural practices and economic development in the community.

Can farmers’ adoption of conservation tillage achieve sustainable agricultural production targets? Evidence from the Jianghan Plain, Hubei, China

ABSTRACT As a major agricultural country, China is under threat from land degradation because of long-term high-carbon-emission land use patterns. Conservation tillage (CT) technologies are expected to restore soil fertility, transform agricultural production models, and improve sustainability. However, the impacts of farmers’ CT adoption on achieving sustainable agricultural development remain unclear. To investigate the heterogeneous ecological effects resulting from farmers’ adoption of CT technologies, this study estimates the ecological effects of CT and examines the differences in these effects across different technologies, utilizing survey data from 1,449 farmers in the rural Jianghan Plain of China. The results show that farmers’ technology perceptions, previous adoption behaviour, and collective economic conditions significantly affect their CT adoption behaviour. The agricultural eco-efficiency in the Jianghan Plain is only 0.65. After removing bias from self-selection and unobservable factors, the ecological effect of CT adoption was 8.43%. The four different CT technologies evaluated in this study were found to increase agricultural eco-effects and achieve sustainable agriculture. However, the agricultural eco-effects of different CT technologies vary, indicating heterogeneity. Finally, policies that include implementing different combinations of CT models according to local conditions, technical training, and establishing subsidies for CT adoption are highly recommended.

The Effect of the Construction of a Tillage Layer on the Infiltration of Snowmelt Water into Freeze–Thaw Soil in Cold Regions

The snow melting and runoff process in the black soil area of Northeast China has led to soil quality degradation in farmland, posing a threat to sustainable agricultural development. To investigate the regulatory effect of tillage layer construction on the infiltration characteristics of snowmelt water, a typical black soil in Northeast China was selected as the research object. Based on field experiments, four protective tillage treatments (CK: control treatment; SB: sub-soiling treatment; BC: biochar regulation treatment; SB + BC: sub-soiling tillage and biochar composite treatment) were set up, and the evolution of soil physical structure, soil thawing rate, snow melting infiltration characteristics, and the feedback effect of frozen layer evolution on snowmelt infiltration were analyzed. The research results indicate that sub-soiling and the application of biochar effectively regulate soil aggregate particle size and increase soil total porosity. Among them, at the 0–10 cm soil layer, the soil mean weight diameter (MWD) values under SB, BC, and SB + BC treatment conditions increased by 6.25%, 16.67%, and 19.35%, respectively, compared to the CK treatment. Sub-soiling increases the frequency of energy exchange between the soil and the environment, while biochar enhances soil heat storage performance and accelerates the melting rate of frozen soil layers. Therefore, under the SB + BC treatment conditions, the maximum soil freezing rate increased by 21.92%, 5.67%, and 25.12% compared to the CK, SB, and BC treatments, respectively. In addition, sub-soiling and biochar treatment effectively improved the penetration performance of snowmelt water into frozen soil layers, significantly enhancing the soil’s ability to store snowmelt water. Overall, it can be concluded that biochar regulation has a good improvement effect on the infiltration capacity of surface soil snowmelt water. Sub-soiling can enhance the overall snowmelt water holding capacity, and the synergistic effect of biochar and deep tillage is the best. These research results have important guiding significance for the rational construction of a protective tillage system model and the improvement of the utilization efficiency of snowmelt water resources in black soil areas.

Characteristics and Risk Assessment of Heavy Metal Contamination in Arable Soils Developed from Different Parent Materials

This study analyzes the heavy metal pollution in cultivated soils developed from different parent materials in Yunnan Province and assesses their risk levels. The results show significant regional differences in soil heavy metal pollution, greatly influenced by the type of parent material. Cadmium (Cd) pollution is most severe in multiple parent material soil regions, particularly in areas with carbonate and purple rocks, exhibiting a high pollution risk. Other heavy metals such as zinc (Zn), copper (Cu), and lead (Pb) also show varying degrees of enrichment in different parent material zones, posing potential pollution risks. The soil pollution levels of heavy metals were classified using the geo-accumulation index method. It was found that soils developed from carbonate rocks and purple rocks have the most severe heavy metal pollution, while soils from quartzitic rocks, acidic crystalline rocks, and basalt exhibit relatively lower pollution levels. By analyzing the characteristics of heavy metal pollution in different parent materials, this study provides a scientific basis for regional soil pollution management and sustainable agricultural development.

Climate-Smart Drip Irrigation with Fertilizer Coupling Strategies to Improve Tomato Yield, Quality, Resources Use Efficiency and Mitigate Greenhouse Gases Emissions

Background: Integrated water and fertilizer management is important for promoting the sustainable development of agriculture. Climate-smart drip irrigation with fertilizer coupling strategies plays an important role to mitigate greenhouse gas emissions, ensuring food production, and alleviating water scarcity and excessive use of fertilizers. Methods: The greenhouse experiment consists of three drip irrigation treatments which include D1: drip irrigation (100 mm); D2: drip irrigation (200 mm); D3: drip irrigation (300 mm) under three different fertilizer management practices N1: nitrogen level (150 kg N ha−1); N2: nitrogen level (300 kg N ha−1); N3: nitrogen level (450 kg N ha−1). Results: The results showed that significantly improved soil moisture contents, quality and tomato yield, while reduced (38.6%) greenhouse gas intensity (GHGI) under the D3N3 treatment. The D2 and D3 drip irrigation treatments with 450 kg nitrogen ha−1 considerably improved NH4+-N contents, and NO3−-N contents at the fruit formation stage. The improve in net primary productivity (NPP), net ecosystem productivity (NEP), evapotranspiration (ET), and ecosystem crop water productivity (CWPeco) through D3N3 treatment is higher. The D3N3 treatment improved (28.2%) the net global warming potential (GWP), but reduced GHGI, due to improved (18.4%) tomato yield. The D3N3 treatment had significantly greater irrigation water productivity (IWP) (42.8%), total soluble sugar (TSS) (32.9%), vitamin C content (VC) (39.2%), soluble sugar content (SSC) (44.2%), lycopene content (41.3%) and nitrogen use efficiency (NUE) (52.4%), as compared to D1N1 treatment. Conclusions: Therefore, in greenhouse experiments, the D3N3 may be an effective water-saving and fertilizer management approach, which can improve WUE, tomato yield, and quality while reducing the effect of global warming.

Resource allocation design method based on WSN multi-dimension soil inspection

Abstract. With the industrialization of agriculture and the sharp rise of food prices, in order to promote the intensive development of natural resources and achieve economies of scale agriculture in production, people put forward the innovative strategy of "precision agriculture" to maintain the sustainable development of agriculture. To protect the environment and farmer communities and increase productivity. However, in the special context of precision agriculture, the problems related to energy consumption and electromagnetic wave propagation in complex environments have a significant impact on precision agriculture. In the case of no monitoring and harsh environment, wireless sensor networks (WSN) can complete the tasks of real-time monitoring and information collection. In the past, the understanding of WSN in agriculture was limited to unilateral detection, which led to the underutilization of the soil. This paper introduces an innovative solution for precision agriculture based on wireless sensor networks, which mainly focuses on the observation and impact assessment of soil water volume,PH value, temperature and pressure in agricultural production process. It aims to reduce the waste of natural resources and the use of fertilizer substances, while maintaining or improving the yield of farming, in order to better promote the development of WSN in the agricultural field.

How do cooperative rebates and asset specificity impact operating performance of farmer cooperatives? evidence from China.

Improving farmer cooperatives' operating performance is essential for their economic sustainability and for promoting sustainable agricultural development. Despite the prominent role of benefit distribution and asset specificity in cooperation organizations, their impact on performance remains controversial, and the relationship between the three is unclear. This study focuses on cooperative benefit distribution in terms of their rebates (first and second rebates) and investigates the relationship between asset specificity, cooperative rebate and operating performance. The results show that both the first and second rebates have a positive impact on operating performance and play substitutive roles in increasing operating performance. Physical asset specificity (PAS) impacts first rebates positively, whereas human asset specificity (HAS) impacts first rebates negatively. PAS has a positive effect on operating performance whereas HAS has no significant influence on operating performance. Furthermore, the first rebate mediates the relationship between PAS and operating performance and suppresses the relationship between HAS and operating performance. These findings provide support for developing relevant policies to improve cooperative performance and sustainability.

The Multifaceted Soils of Himachal Pradesh, India: Types and Characteristics

The soils of Himachal Pradesh face a multitude of pressing issues that threaten their health and agricultural productivity. The diverse geographical features, temperature regimes and biological zones of Himachal Pradesh have resulted into a vast range of soil types in the state. Alpine, forest, alluvial and sub-mountain soils are the four major soil categories into which the state's soils can be generally divided. Only a particular species and adapted plants can thrive in the extreme weather circumstances of alpine soils, which are located at higher elevations and possess soils having thin, stony and acidic in reaction. The mid-hill areas are dominated by forest soils which are distinguished by their high levels of organic matter and nutrients. These soils support luxuriant vegetation and make effective agricultural methods possible. Alluvial soils, which are mostly found in river valleys and are deposited by ravine systems are extremely rich and perfect in fertility and suitable for growing a range of crops including vegetables, rice and maize. Located in the foothills, sub-mountain soil exhibits a blend of traits from alluvial and forest soils, thus providing a range of uses for horticulture and agriculture. In addition to supporting a diverse array of plant and animal life, Himachal Pradesh's varied soil types are vital to the region's agricultural economy. One of the biggest problems is soil erosion, which is made worse by the steep terrain and abundant rainfall in the area. This results in the loss of topsoil and decreased fertility. Deforestation, overgrazing, and unsustainable farming methods all contribute to land degradation, which further reduces biodiversity and soil quality. Furthermore, crop development is impacted by soil acidity in high-altitude regions, which restricts nutrient availability. However, these issues need efficient conservation measures and soil management techniques. In order to maintain Himachal Pradesh's rich biodiversity and agricultural potential for future generations, sustainable agricultural development and environmental protection in this ecologically sensitive area depend on an understanding of the complex soil dynamics. Addressing these interconnected issues requires a comprehensive approach, emphasizing sustainable land management practices, community engagement, and restoration efforts to preserve the vital soil resources of this ecologically sensitive region.

Study on optimization of maize irrigation scheduling in Shaanxi Province

With the impact of climate change in recent years, the instability of precipitation and the increase of evaporation have made water resources in Shaanxi Province more and more stressed. How to maintain stable economic development while ensuring the sustainable development of agriculture within the limited water resources has become an important issue facing Shaanxi Province. This paper analyzes the temporal and spatial distribution characteristics of maize irrigation water requirement in Shaanxi Province based on meteorological data and obtains the ideal irrigation water requirement. However, considering the agricultural water scarcity issue in this region, this paper establishes different water scarcity scenarios, adopts the Jensen model to construct an objective function aimed at minimizing crop yield reduction, and solves it using a genetic algorithm to obtain the optimized maize irrigation scheduling under different water scarcity scenarios. The results analysis indicates the following: (1) The effective rainfall in Shaanxi Province from 1960 to 2019 shows a slight upward trend, while the maize water requirement and maize irrigation water requirement shows a downward trend. (2) The spatial distribution of maize irrigation water requirement in Shaanxi Province decreases gradually from north to south. High-value areas are mainly distributed in the northern regions of Yulin City and Yan’an City in Shanbei. Low-value areas are distributed in Ankang City in Shannan. (3) In the face of water scarcity, spring maize should ensure water use during the middle growth period, fast development period, and initial growth periods. Under the same water scarcity conditions, the yield reduction rate is the highest in Guanzhong, followed by the Shannan, and the lowest in Shanbei. This paper aims to provide a scientific and reasonable optimization plan for maize irrigation in Shaanxi Province by calculating and analyzing the maize irrigation water requirement in Shaanxi Province, combined with optimization tools such as genetic algorithms, to address the challenges brought by water resource constraints.

Enhancement of Tomato Fruit Quality Through Moderate Water Deficit

In arid areas, water shortage has become a major bottleneck limiting the sustainable development of agriculture, necessitating improved water use efficiency and the full development of innovative water-saving irrigation management technologies to improve quality. In the present study, tomato (Solanum lycopersicum cv. Micro Tom) fruits were used as materials, and different irrigation frequencies were set during the fruit expansion stage. The normal treatment (CK) was irrigated every three days, while the water deficit treatments were irrigated at varying frequencies: once every 4 days (T1), 5 days (T2), 6 days (T3), 7 days (T4), and 8 days (T5). These corresponded to 80%, 70%, 60%, 50%, and 40% of the maximum field moisture capacity (FMC), respectively, with CK maintaining full irrigation at 90% of the maximum FMC. The water deficit treatment T3, with less stress damage to plants and the most significant effect on fruit quality improvement, was selected based on plant growth indices, photosynthetic characteristics, chlorophyll fluorescence parameters, and fruit quality indices, and its effects on carotenoids, glycolic acid fractions, and volatile compounds during tomato fruit ripening were further investigated. The outcome indicated that moderate water deficit significantly increased the carotenoid components of the tomato fruits, and their lycopene, lutein, α-carotene, and β-carotene contents increased by 11.85%, 12.28%, 20.87%, and 63.89%, respectively, compared with the control fruits at the ripening stage. The contents of glucose and fructose increased with the development and ripening of the tomato fruits, and reached their maximum at the ripening stage. Compared to the control treatment, the moderate water deficit treatment significantly increased the glucose and fructose levels during ripening by 86.70% and 19.83%, respectively. Compared to the control conditions, water deficit conditions reduced the sucrose content in the tomato fruits by 27.14%, 18.03%, and 18.42% at the mature green, turning, and ripening stages, respectively. The moderate water deficit treatment significantly increased the contents of tartaric acid, malic acid, shikimic acid, alpha ketoglutaric acid, succinic acid, and ascorbic acid, and decreased the contents of oxalic acid and citric acid compared to the control. The contents of total soluble sugar and total organic acid and the sugar–acid ratio were significantly increased by 48.69%, 3.71%, and 43.09%, respectively, compared with the control at the ripening stage. The moderate water deficit treatment increased the fruit response values to each sensor of the electronic nose, especially W5S, which was increased by 28.40% compared to the control at the ripening stage. In conclusion, during the ripening process of tomato fruit, its nutritional quality and flavor quality contents can be significantly improved under moderate (MD) deficit irrigation treatment. The results of this experiment can lay the foundation for the research on the mechanism of water deficit aiming to promote the quality of tomato fruit, and, at the same time, provide a theoretical basis and reference for tomato water conservation and high-quality cultivation.

Enhancing Soil Salinity Evaluation Accuracy in Arid Regions: An Integrated Spatiotemporal Data Fusion and AI Model Approach for Arable Lands

Soil salinization is one of the primary factors contributing to land degradation in arid areas, severely restricting the sustainable development of agriculture and the economy. Satellite remote sensing is essential for real-time, large-scale soil salinity content (SSC) evaluation. However, some satellite images have low temporal resolution and are affected by weather conditions, leading to the absence of satellite images synchronized with ground observations. Additionally, some high-temporal-resolution satellite images have overly coarse spatial resolution compared to ground features. Therefore, the limitations of these spatiotemporal features may affect the accuracy of SSC evaluation. This study focuses on the arable land in the Manas River Basin, located in the arid areas of northwest China, to explore the potential of integrated spatiotemporal data fusion and deep learning algorithms for evaluating SSC. We used the flexible spatiotemporal data fusion (FSDAF) model to merge Landsat and MODIS images, obtaining satellite fused images synchronized with ground sampling times. Using support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models, we evaluated the differences in SSC evaluation results between synchronized and unsynchronized satellite images with ground sampling times. The results showed that the FSDAF model’s fused image was highly similar to the original image in spectral reflectance, with a coefficient of determination (R2) exceeding 0.8 and a root mean square error (RMSE) below 0.029. This model effectively compensates for the missing fine-resolution satellite images synchronized with ground sampling times. The optimal salinity indices for evaluating the SSC of arable land in arid areas are S3, S5, SI, SI1, SI3, SI4, and Int1. These indices show a high correlation with SSC based on both synchronized and unsynchronized satellite images with ground sampling times. SSC evaluation models based on synchronized satellite images with ground sampling times were more accurate than those based on unsynchronized images. This indicates that synchronizing satellite images with ground sampling times significantly impacts SSC evaluation accuracy. Among the three models, the CNN model demonstrates the highest predictive accuracy in SSC evaluation based on synchronized and unsynchronized satellite images with ground sampling times, indicating its significant potential in image prediction. The optimal evaluation scheme is the CNN model based on satellite image synchronized with ground sampling times, with an R2 of 0.767 and an RMSE of 1.677 g·kg−1. Therefore, we proposed a framework for integrated spatiotemporal data fusion and CNN algorithms for evaluating soil salinity, which improves the accuracy of soil salinity evaluation. The results provide a valuable reference for the real-time, rapid, and accurate evaluation of soil salinity of arable land in arid areas.