Agricultural Production Research Articles

Agricultural economic complexity and regional inequalities: a new approach using census data from Brazil

ABSTRACT The paper presents an original application of economic complexity to the agricultural sector aiming to determine its suitability to measure and depict regional structural inequalities across Brazilian regions. Using census data, we calculate agricultural complexity indexes for products (PCI-Agro) and regions (ECI-Agro) and the agricultural product space. We show that higher PCI-Agro is associated with higher levels of productivity, education, income and human development. Also, we found that ECI-Agro is associated with higher productivity in agriculture. The econometric evidence points to an inverted ‘U’-shape curve relationship between ECI-Agro and agricultural productivity, which has significant policy implications.

Soil Suitability Evaluation for Agriculture: New Model Development and Validation

ABSTRACT The evaluation of land suitability for agricultural production is a complex process that requires specialized information and expert analysis. This paper introduces the Soil Suitability Evaluation (SSE) model, a computer program designed to assess land suitability for agriculture. The model considers soil surface conditions (erosion, micro-relief, and slope), physical characteristics (texture and depth), and chemical properties (salinity, sodicity, fertility, and calcium carbonate). It involves calculating these soil characteristics along with their respective weights. Compared to other land suitability evaluation methods, the SSE model provides realistic and practical results including assessments of soil and crop suitability. It serves as a simple and useful tool for decision-makers to determine land quality for agricultural use and proposed some recommendations for land reclamation.

Positive impacts of compost and biochar from orange peel waste on Phaseolus vulgaris physio-biochemistry and productivity and soil properties under salinity stress

ABSTRACT Bioconversion of organic orange peel wastes into compost (OPC) or biochar (OPB) can help overcome the effects of soil salinity and maintain agricultural productivity. Effect of 1.0% OPC or 1.0% OPB as soil amendments on soil properties and performance of Phaseolus vulgaris plants under two salinity levels [5.60 dS m−1 (S-1) and 9.60 dS m−1 (S-2)] versus control [1.60 dS m−1 (S-0)] was investigated. Compared to the control, S-1 and S-2 increased soil sodium ion (Na+) content, while reducing soil organic matter (OM), macro- and micro-nutrients, cation exchange capacity (CEC), and enzyme (urease, acid phosphatase, and alkaline phosphatase) activities, reflecting decreased plant growth and yield. Under S-1 or S-2, OPC application exceeded OPB application, but both considerably enhanced soil OM content, nutrients, CEC, and enzyme activities. These positive results reflected decreased leaf electrolyte leakage and increased leaf-relative water content, pigments, nutrients, K+/Na+ ratio, antioxidant activity, plant growth, and yield. The beneficial impacts of 1.0% OPC were more pronounced under S-2 than S-1 or S-0. In conclusion, adding 1.0% OPC to saline soil (ECe of about 5–10 dS m−1) could mitigate the influences of soil salinity to improve Phaseolus vulgaris growth, physio-biochemistry, and yield.

LOSSES OF THE WEST RUSSIAN VILLAGE DUE TO THE AXIS AGGRESSION: SCALE AND CONSEQUENCES

The article analyses the impact of human and material losses of the village of the West of the Russian Soviet Federative Socialist Republic (the RSFSR) on the peculiarities of reconstruction processes in the first post-war years in terms of the diverse sources, many of which have recently been introduced into the scientific turnover. The deficit of manpower, equipment, and capital investments in the region’s agriculture, including one due to wartime losses, significantly slowed down the agricultural production industrialisation completion, predetermined inability to solve key problems of life and labour of the population, became one of the important causes of low incomes of many collective and state farms, the reduction of arable land since the early 1950s, and the long dominance of extra-economic coercion and its supporting structures in the countryside. The consequences of the war devastation combined with the unpopular agrarian policy of the first post-war years brought to life the improvement of adaptive behaviour by the village, which the authorities spent a lot of energy and resources to combat. The losses of the war years and harsh agrarian policy were also the key reasons for the undermining of the preserved elements of traditional culture of the village of the West of the RSFSR.

Use of HANTS algorithm in Sentinel-2 images for interpolation of missing data: a case-study for soy agricultural areas in periods of growth and harvesting

Abstract. Information about the current state of an agricultural culture can be fundamental in decision making, varying from quantity of fertilizers to be used in an area to estimating the productivity of a harvest in a period to establish credits payment. Satellite images with reasonable spatial resolution like Sentinel-2 are a powerful tool to visualize agricultural areas production, especially with the application of multispectral indexes like the Normalized Difference Vegetation Index (NDVI). Yet, due to the limitation of temporal resolution of satellites and periods with large cloudy weather can make the analysis difficult. This study proposed the use of the harmonic analysis time series algorithm (HANTS) to aid the incompleteness of time series obtained by the available satellite images in a case study of soy culture in Brazil. By applying the algorithm, a harmonic interpolation is obtained to produce a full daily NDVI time series from the beginning to end of the study period, facilitating further analysis to obtain metrics of interest. In this study, time series with gaps on specified periods are created based on full time series, then interpolated and compared with the real ones based on root mean squared error (RMSE) to access its accuracy.

Assessing the Spatial Distribution, Condition, and Intensity of Soil Acidity for Informed Management Decisions in the Semen Aari District of Ari Zone, Southwestern Ethiopia

Soil acidity poses a significant challenge to agricultural productivity in Ethiopia's highlands, particularly affecting the Semen Ari district in the Ari zone. The common practice of applying agricultural lime to mitigate soil acidity is hampered by a lack of detailed information on the extent, severity, and spatial distribution of acidic soils. This study aims to determine how soil acidity varies spatially by identifying and mapping the specific geographic patterns of soil acidity levels in the Semen Ari district. Seventy-one composite soil samples from the 0–20 cm layer were geo-referenced and analyzed. Using statistical analysis and ArcGIS software for spatial interpolation through ordinary kriging, soil pH ranged from 3.29 to 5.68, classifying 99% of the soils as strongly acidic. The root mean squared error (RMSE) of the interpolation was 0.30. Soil pH showed a significant negative correlation with exchangeable acidity but a non-significant negative correlation with organic carbon and total nitrogen. The results highlight the need for targeted soil management strategies, such as appropriate lime application rates and the cultivation of acid-tolerant crops, to enhance crop yields. Further research is recommended to include comprehensive soil property datasets to better understand the factors influencing soil pH variability, thus supporting more precise management of acidic soils in the region. The generated high-resolution soil acidity map serves as a valuable tool for agricultural planning and decision-making.

Effects of a Novel Tripyrasulfone Herbicide on Key Soil Enzyme Activities in Paddy Rice Soil

Weeds significantly impact paddy yields, and herbicides offer a cost-effective, rapid, and efficient solution compared to manual weeding, ensuring agricultural productivity. Tripyrasulfone, a novel 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitor developed by Qingdao Kingagroot Chemicals Co., Ltd., has demonstrated high efficacy when applied post-emergence, causing characteristic foliar bleaching in susceptible weed species, distinct from conventional acetolactate synthase, acetyl-CoA carboxylase, and synthetic auxin herbicides. This study investigates the impact of tripyrasulfone on the activity of key soil enzymes (urease (UE), acid phosphatase (ACP), sucrase (SC), catalase (CAT), and dehydrogenase (DHA)) in paddy soils from Jilin Province and Shandong Province. Different doses of tripyrasulfone (0.1, 1.0, and 2.5 mg kg−1) were applied, and the enzymatic activities were measured. Results indicated that tripyrasulfone initially inhibited UE and ACP activities before activating them. On the 20th day after treatment, UE activity had returned to control levels, whereas ACP activity remained significantly higher, showing long-lasting activation. SC and CAT activities were inhibited but gradually recovered to control levels. Furthermore, DHA activity was activated with a sustained effect, remaining significantly higher than the control group even 20 days after treatment. Overall, the impact of tripyrasulfone on soil enzyme activities diminished over time, suggesting that tripyrasulfone posed minimal long-term ecological risk to soil health.

Meta-Analysis on Farmers’ Adoption of Agricultural Technologies in East Africa: Evidence from Chinese Agricultural Technology Demonstration Centers

Farmers’ low agricultural productivity in East African countries is attributed to among other factors, the low application of modern agricultural technologies. Using meta-analysis this study intended to examine the determinants of adoption of agricultural technologies in East African countries recipients of Chinese Agricultural Technology Demonstration Centers (ATDCs). A comprehensive review employed to gather 22 studies addressing the determinants of adoption of agricultural technologies such as improved varieties, agricultural mechanization, soil conservation, and animal breeding from Ethiopia, Tanzania, Uganda, and Rwanda. The collected data were analyzed using STATA 17 software. The study used a random-effect model to estimate the determinants of agricultural technologies adoption. The findings demonstrated that access to credit, age, education, farming experience, farmer group membership, household size, and off-farm income have a significant influence on farmers ‘adoption of improved varieties. Age, farm size, and education have a significant influence on the adoption of agricultural mechanization. Soil conservation practices are influenced by age, household size, and sex. Moreover, age, education, extension services, and farming experience have a significant impact on the adoption of animal breeding. It is recommended that East African governments, policymakers, and agricultural practitioners to focus on these common variables when planning for the intervention of new agricultural technologies.

Revolutionizing plant health with machine learning for disease detection and diagnosis

The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant interest in modern agriculture. The appeal of AI arises from its ability to rapidly and precisely analyze extensive and complex information, allowing farmers and agricultural experts to quickly identify plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant attention in the world of agriculture and agronomy. By harnessing the power of AI to identify and diagnose plant diseases, it is expected that farmers and agricultural experts will have improved capabilities to tackle the challenges posed by these diseases. This will lead to increased effectiveness and efficiency, ultimately resulting in higher agricultural productivity and reduced losses caused by plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has resulted in significant benefits in the field of agriculture. By using AI technology, farmers and agricultural professionals can quickly and accurately identify illnesses affecting their crops. This allows for the prompt adoption of appropriate preventative and corrective actions, therefore reducing losses caused by plant diseases.

Urban food availability and consumption in Grand Lomé, Togo: A household perception analysis

Introduction: Food well-being of the population is one of the priorities of the Togolese government, which relies on the agricultural investment and food security Programme to increase national food production. In addition, the country relies on food imports to make up the shortfall. At the same time undernourishment and malnutrition remain high among the country’s population. This research analyzes food supply and its implications for household consumption in Grand Lomé, Togo. [Methods] The methodology used documents, a survey of 963 heads of household randomly sampled households and semi-structured interviews with 10 households and with Togolese food safety agency (ANSAT). Quantitative data were processed and analyzed using Excel spreadsheets R and R-Studio, while content analysis was applied to the verbal applied to the verbal statements collected. Results: Firstly, the results show that domestic agricultural production contributed an average of 91% of food supply between 2014–2017. The deficit is made up by food imports, which rose from 13.5% in 2014 to 15.4% in 2017. This translated into an acceptable food energy consumption of 2337 Kcal/head/day in 2017. Secondly, 81% of respondents recognize a strong food presence at consumer markets, except that the chi-square test applied to the data at the 5% threshold shows (p-value < 2.2 × 10−16), indicates that this satisfaction is a function of place of residence. Despite this, persistent shortages affect more staple crops, livestock and dairy products, leading households to deprive themselves and buy food at affordable prices. Finally, we observe non-diversified diets marked by regular consumption of “cereals/legumes”, vegetables and beverages to the detriment of “tubers/roots”, “meat/fish”, “fruit” and “dairy products”. Conclusion: This research shows that food supply, although adequate, is not sufficient to ensure balanced, nutritious and culturally appropriate food consumption by urban households. Recommendations: To meet these challenges, the central government, in collaboration with urban communes and consumer advocates, must mobilize resources to create urban agricultural farms, strengthen food protection systems, distribute staple products directly to households and limit the importation of food that is hazardous to health.

Fusarium oxysporum f. sp. apii Race 4 Threatening Celery Production in South Florida.

Fusarium wilt, caused by Fusarium oxysporum f. sp. apii (Foa), is a vascular disease affecting celery. This soilborne pathogen is classified into four distinct pathogenic races: 1, 2, 3, and 4. Notably, race 4 emerges as the most virulent, representing the latest evolutionary development of this pathogen, which was first reported in 2013 in California. In 2022, celery plants in South Florida exhibited typical Fusarium wilt symptoms, with the disease reaching a 100% incidence and causing yield losses ranging from 20 to 100%. Given the significance of celery as a vegetable crop and the severity of this outbreak, the primary objective of this study was to identify and characterize the causal agent of Fusarium wilt in South Florida. The second goal aimed to test the pathogenicity and virulence of the Fusarium isolates from Florida on celery and parsley plants. Using race-specific primers and dual-loci phylogenetic analyses, the isolates surveyed in this study were identified as Foa race 4. Pathogenicity assays in the greenhouse showed that the Foa race 4 isolate from celery induced disease not only on the two celery cultivars (Duda 30 and Duda 71) but also on two commonly cultivated parsley varieties (curly and Italian). Our study also revealed that the Foa race 4 significantly (P < 0.05) affected plant health attributes in all cultivars, including plant height, total plant weight, and root weight. Interestingly, the pathogen exhibited higher (P < 0.0001) virulence on parsley than celery based on vascular discoloration. These findings strongly indicate the urgency of comprehending and managing Fusarium wilt on celery and related crops. Furthermore, the ability of Foa race 4 to affect different plant species highlights a potential threat to agricultural production, emphasizing the need for proactive measures to mitigate the impact of this virulent pathogen.

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.

Future climate impacts on crop production in Bhutan

Understanding the relationship between weather extremes and crop productivity is the backbone of risk assessments on food security. The former is paramount in countries (i.e., Bhutan) where there are a limited number of impact assessments in agriculture. The analysis performed in this work highlights the trends in agricultural production under a changing climate and the attribution of yield change to a specific climatic hazard. We improve the understanding between the two (climate and yields) by applying an FAO eco-physiological model (PyAEZ) and by relying on state-of-the-art downscaled climate projections (CORDEX-CORE). We analyze climate change impacts on ten crops (maize, foxtail millet, buckwheat, wheat, rice, common beans, cabbage, white potatoes, carrots, and citrus) at national level. The main simulation findings point to a higher yield gain and loss under rainfed conditions as opposed to irrigated conditions by the end-century (2070-2099) compared to the baseline period (2010-2039), particularly under RCP 8.5 for white potatoes (+17.7%), wheat (+15.5%), cabbage (+12.8%), buckwheat (-6.6%), and citrus (-5.8%). The irrigation potential to modulate climate change impacts is reflected in a yield gain of +43.4% on average for all crops and RCP’s when compared to rainfed conditions. On average, weather extremes explain 28% and 33% of the yield variability over time, respectively under RCP’s 2.6 and 8.5. Overall, this study supports smallholder farmers, decision-makers, and project developers to implement adaptation solutions that minimize growing weather extremes such as heat-stress and dry-spells.

Building Agricultural Resilience: Strategies for Climate Change Adaptation

Climate change has a significant impact on India's agriculture, influencing environmental conditions, agricultural techniques, and production. The term "climate-resilient agriculture" refers to technologies, methods, tools, and frameworks aimed at adapting to and mitigating the effects of climate change on agricultural production, food security, and livelihoods. Crop productivity and food security are greatly impacted by climate changes, which calls for creative agricultural methods to maintain food supply while reducing environmental damages. These difficulties are made worse by environmental changes and global population expansion, especially in developing countries. Technological advancements, such as geospatial analysis, and eco-friendly farming technologies, play a crucial role in optimizing land use and enhancing food security. Crop variety adaptation and management of seed, water, land, livestock, soil and nutrients, pest management, risk management, and post-harvest management are examples of effective agricultural adaptation strategies. Technological developments like geographic analysis.

An improved optical trapezoid model to identify wetlands and irrigation water ponds in the arid irrigation district using Google Earth Engine

ABSTRACT In arid regions, wetlands support ecosystem with high biodiversity and productivity. Meanwhile, irrigation during the fallow period often results in the temporary formation of water ponds in the cropland, which plays a critical role in agricultural productivity. The prompt identification of wetlands and irrigation water ponds in irrigation districts is important for the protection of local wetland ecosystem and the effective management of irrigation water. Utilizing the OPtical TRApezoid Model (OPTRAM) for soil moisture estimation, we have proposed an improved version of OPTRAM aimed to identify wetlands and irrigation water ponds in irrigation district using Sentinel-2 data through Google Earth Engine platform. Different criteria were used for the identification of wetland and irrigation water pond, given the greater diversity associated with wetlands. Parameters for the wet edge determined from OPTRAM were used to identify wetlands, while an additional threshold was added to the model and calibrated in accordance with the irrigated area to identify irrigation water ponds. The improved OPTRAM was applied in Hetao Irrigation District (HID) of Northwest China from 2016 to 2023, where autumn irrigation applied in late autumn after crop harvesting was considered. The identified distributions of wetland and autumn irrigation were validated with observations from field survey and statistical data, and were also compared with other remote sensing products. Results show that the proposed model is effective in identifying both wetlands and irrigation ponds. Considering the wetlands, the distances from identified boundaries to the actual boundaries obtained from field surveys were calculated, revealing the mean absolute error of 6.99 m, alongside a validation accuracy ratio of 0.94 for points located within wetlands. For autumn irrigation extent, the overall accuracy is 0.90 based on the observations from field survey, with mean absolute relative errors for irrigated areas across four sub-irrigation districts recorded as 20.55%, 8.10%, 12.83%, and 11.38%, respectively, when compared with statistical data. The majority of wetlands within HID are small in size and scattered, with different trends observed in the wetland areas of sub-irrigation districts and Wuliangsuhai Lake. Autumn irrigated croplands are mainly concentrated in Jiefangzha sub-irrigation district while being scattered across other sub-irrigation districts, depicting an overall decreasing trend in the autumn irrigated area. In summary, the proposed model performed well in identifying both wetlands and irrigation ponds, and can offer valuable support for the conservation of wetland ecosystem and irrigation management.

Revitalizing Agricultural Economy Through Rural E-Commerce? Experience from China’s Revolutionary Old Areas

Many of the world’s less developed regions may not be able to improve the well-being of rural residents through agricultural revitalization because of their remoteness from agricultural markets. Using the county-level data set of China’s underdeveloped old revolutionary base areas from 2010 to 2021, this paper takes the policy planning of rural e-commerce as event intervention to investigate the driving role of the digital product market on agricultural economic development. Empirical results show that rural e-commerce planning policy has significantly promoted the agricultural added value of the pilot counties, and the digital market is the key driving factor of the agricultural economic growth in these underdeveloped areas. Both food production and livestock output have increased significantly as a result of e-commerce policies. Considering the potential bias of the bidirectional fixed effect estimators of staggered differences-in-differences (DID), this study uses heterogeneous robust estimators to verify the growth effect of the agricultural economy. Specifically, digital agricultural markets have significantly promoted agricultural mechanization and significantly improved agricultural total factor productivity. Moreover, empirical evidence does not support transmission mechanisms for off-farm employment and agricultural entrepreneurship. The findings can help less developed countries and regions develop policies to expand the agricultural markets with digital dividends, thereby promoting the development of the agricultural economy.

Simulating Agricultural Water Recycling Using the APEX Model

Irrigation plays a vital role in many agricultural crop production regions. Drainage water recycling (DWR) is a popular irrigation water management system that collects excess water drained from cropland fields and stores it in on-site reservoirs for reuse. The efficacy of these systems varies by location, climate, irrigation frequency, and crop demands. Simulating this system would be beneficial for assessing the impact of water and land management practices on agriculture and natural resources. This study presents the development of computational algorithms for DWR simulation with the Agricultural Policy Environmental eXtender (APEX) model, along with the results for 39 testing sites where both reservoir and drainage systems are adopted. Simulating a DWR system with the revised reservoir module, the APEX model simulates irrigation water reuse ranging between 29% and 93%; sediment reduction of around 66%; nitrogen loss reduction of 23% and 73% for the mineral and organic forms, respectively; and phosphorus loss reduction of 22% and 79% for the soluble and sediment-transported forms, respectively. In conclusion, the results provided by the APEX model for sediment loss reduction align with field data, but discrepancies for nitrogen and phosphorus losses emerged from this test.

Optimizing Water Productivity through the Conjunctive Use of Borewell and Canal Water for Enhancing Sheep Productivity in Arid Climate of Western Rajasthan, India

In the arid regions of Rajasthan, water scarcity poses a significant challenge to both agriculture and livestock productivity. Sustainable practices in such climates require effective management of limited water resources. This study focuses on optimizing water productivity through the conjunctive use of canal water and borewell water to address the problem of water scarcity while maximizing output per unit of water. The experiment involved eighteen healthy lambs of aged 2-3 months which were managed intensively and divided into three groups of six lambs each based on a randomized block design. All groups were fed Pennisetum glaucum (pearl millet) and Medicago sativa (lucerne) ad-libitum along with concentrates adjusted according to their body weights. The groups were offered three types of drinking water: canal water (G1), a combination of canal and borewell water (G2) and borewell water (G3) in a clean bucket twice a day. No significant effect on total feed intake and body weight of the lambs was found based on the drinking water source. However, G1 and G2 lambs, which were offered canal water, consumed significantly (p&lt;0.05) less water (74.4 ± 1.84 liters) compared to the lambs in G3 (81.6 ± 1.91 liters). The virtual water requirement per kilogram of weight gain, calculated from both direct water intake and indirect intake through feed was lowest in G1 lambs (5771.34 liters) followed by G2 lambs (6129.00 liters) and G3 lambs (6312.00 liters) per kilogram of weight gain, respectively. These results indicate that water productivity was highest with canal water (G1) and lowest with borewell water (G3) per kilogram of weight gain. This study underscores the importance of efficient water management in maximizing productivity and resource use efficiency in water-limited arid regions.

Aged Refuse Recycling to Treat Wastewater from Coffee Processing

Over the last two decades, the use of bioreactors filled with aged refuse extracted from closed areas of landfills has proven to be a viable alternative for the treatment of different types of wastewater. This study presents the results obtained during the evaluation of aged refuse used as filling material for a downflow bioreactor during the removal of the organic load present in wastewater generated in the wet processing of coffee. The tests were carried out over a period of 120 days, with 15 days to start up and stabilize the bioreactor and 105 days to perform treatability tests. The aged refuse, once extracted, was dried and sifted to a particle size of less than 50 mm. The bioreactor used had a cylindrical geometry (Ø = 0.20 m, and h = 3.40 m), and it was fed with hydraulic loads of 50, 100, and 150 L m−3 d−1. The analysis of the data obtained shows that the system studied achieves the removal of 98.3% of the initial organic load when fed with 150 L m−3 d−1. This showcases recycling aged refuse as a technically viable alternative to treat the wastewater generated during coffee processing. Also, the evaluated system has the advantage of needing a short period of time to achieve its stabilization, which turns out to be of great value, especially in its possible use in the treatment of residual water generated in the harvest of agricultural products where the period of harvest is very short.

Addressing Climate Change: The Role of Agriculture in Greenhouse Gas Mitigation

Agriculture is a cornerstone of global food security, which is significantly affected by climate change, primarily driven by human activities that increase greenhouse gas (GHG) emissions. Accounting for approximately 13% of global anthropogenic GHG emissions, agricultural practices-such as livestock rearing, rice cultivation and synthetic fertilizer use which contribute substantially to global warming. Emissions from nitrous oxide (N₂O), methane (CH₄) and carbon dioxide (CO₂) exacerbate this issue, with N₂O being particularly concerning due to its high global warming potential. Mitigation strategies within the agricultural sector are essential, including improved nutrient management, the use of nitrification inhibitors and innovative fertilizer application technologies. Organic farming demonstrates lower N₂O emissions compared to conventional practices, emphasizing the importance of sustainable agricultural methods. Additionally, conservation tillage and practices like zero-tillage help reduce CO₂ emissions by minimizing soil disturbance and enhancing carbon sequestration. Biochar application further supports soil health and GHG reduction by enhancing carbon storage. To mitigate methane emissions from rice fields, reduced tillage and the introduction of electron acceptors can effectively inhibit methanogenesis. As CO₂ constitutes about 72% of total GHG emissions, agricultural practices that increase soil organic carbon (SOC) are critical for effective carbon sequestration. Overall, the agricultural sector holds significant potential for GHG mitigation, with studies suggesting that 89% of the mitigation potential can be achieved through carbon sequestration. Therefore, adopting these strategies is crucial not only for reducing emissions but also for ensuring sustainable agricultural productivity in the face of climate change challenges.