Sustainable Agricultural Management Research Articles

Advancing Farm Entrepreneurship and Agribusiness Management for Sustainable Agriculture

The agricultural sector in both developed and developing countries stands at a pivotal juncture [...]

Predicting the impact of climate change on crop water footprint using CMIP6 in the Shule River Basin, China

Quantitatively predicting the impacts of climate change on water demands of various crops is essential for developing measures to ensure food security, sustainable agriculture, and water resources management, especially in arid regions. This study explored the water footprints (WFs) of nine major crops in the middle and downstream areas of Shule River Basin, Northwest China, from 1989 to 2020 using the WF theory and CROPWAT model and predicted the future WFs of these crops under four emission and socio-economic pathway (SSPs-RCPs) scenarios, which provides scientific support for actively responding to the negative impacts of climate change in arid regions. Results indicated: (1) an increasing trend of the overall crop WF, with blue WF accounting for 80.31–99.33% of the total WF in the last 30 years. Owing to differences of planting structure, water-conservation technologies, and other factors, the multi-year average WF per unit area of crops was 0.75 × 104 m3 hm−2 in downstream area, which was higher than that in midstream area (0.57 × 104 m3 hm−2) in the last 30 years; therefore agricultural water use efficiency in the downstream area was lower than that in the midstream area, implying that the midstream area has more efficient agricultural water utilization. (2) an initial increase and then decrease of crop WFs in the study area under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios by the end of the century, reaching their peak in 2030s which was higher than that from 1989 to 2020; with the maximum growth rates in the midstream area ranging from −0.85% in SSP5-8.5 to 5.33% in SSP2-4.5 and 29.74% in SSP5-8.5 to 34.71% in SSP2-4.5 in the downstream area. The local agricultural water demand would continue to increase and water scarcity issues would be more severe in the next 10–20 years, affecting downstream areas more. Under the SSP3-7.0 scenario, crop WF values of the midstream and downstream regions will be 2.63 × 108 m3 and 4.22 × 108 m3 in 2030, respectively, which is significantly higher than those of other scenarios and show a long-term growth trend. The growth rate of the midstream and downstream regions will reach 44.71% and 81.12%, respectively, by the end of this century, so the local agricultural water use would be facing more strain if this scenario materializes in the future. Therefore, the Shule River Basin should encourage development of water-saving irrigation technologies, adjust the planting ratio of high water consuming crops, and identify other measures to improve water resource utilization efficiency to cope with future water resource pressures.

The LTAR Grazing Land Common Experiment at Northern Plains.

The USDA Long-Term Agroecosystem Research (LTAR) network aims to enhance sustainable agricultural management practices through a coordinated, cross-site common experiment involving 18 locations across the United States. The objective of this paper is to provide an overview of the LTAR grazing lands common experiment at the Northern Plains (NP) site, where an experiment was initiated in 2019 to answer producers' and researchers' questions about whether the tactical application of fire or grazing can reduce the dominance of invasive Kentucky bluegrass in northern Great Plains ecosystems. As part of the LTAR common experiment, we contrast a prevailing practice (season-long grazing at moderate stocking rate) with four alternative practices at a half-hectare plot scale: (1) mob grazing by cattle, (2) multi-species grazing (mob grazing by cattle, with goats foraging at key times of the year), (3) prescribed fire, and (4) prescribed fire followed by cattle grazing. A stakeholder group is engaged in the co-production process to determine alternative practices and how to apply them. Every 5 years, the treatment with the best overall outcomes is applied at a field scale (15ha), resulting in a core treatment contrast of prevailing versus alternative grazing management systems. This experiment aims to develop alternative agroecological practices that optimize current and future economic and ecosystem benefits.

Decision Support System for the Assessment and Enhancement of Agrobiodiversity Performance

The industrialisation of agriculture and changes in production systems have contributed to a biodiversity decline worldwide. Developing accurate and adequate assessment methods can encourage farmers to support more sustainable agricultural management. This study presents a decision support system to promote agrobiodiversity that incorporates not only a quantitative assessment of relevant indicators of agrobiodiversity performance but also provides enhancement practice recommendations and associated benefits, presenting an action plan in order of priority. Additionally, the decision support system allows a visual comparison between biodiversity composite indicators and indicators representing pest control and crop yield. Since grape cultivation is considered one of the most intensive agricultural systems, thus significantly impacting biodiversity, the elaborated decision support system was tested on a viticultural agroecosystem in the demarcated Douro region in Portugal. The results demonstrated the decision support system functioning according to the selected methodology and allowed the identification of future lines for investigation. During the analysed period, the following were verified: an increase of 2% in the biodiversity indicator, 130% in harvest yield, and 2077% in the enemy-to-pest ratio. It is expected that the elaborated DSS will offer a significant contribution by bridging communication gaps on alternative management options to improve biodiversity performance in agricultural systems.

SWFormer: A scale-wise hybrid CNN-Transformer network for multi-classes weed segmentation

Weeds in rapeseed field are an important factor in crop yield reduction and economic loss. Thus, Precision Agriculture is an important task for sustainable agriculture and weed management. At present, deep learning techniques have shown great potential for image-based detection and classification in various crops and weeds. However, the inherent limitations of traditional convolutional neural networks pose significant challenges due to the locally similarity of weeds and crops in color, shape and texture. To address this issue, we introduce SWFormer, a scale-wise hybrid CNN-Transformer network. SWFormer leverages the distinct strengths of both convolutional and transformer architectures. Convolutional structures excel at extracting short-range dependency information among pixels, whereas transformer structures are adept at capturing global dependency relationships. Additionally, we propose two innovative modules. Firstly, the Scale-wise Cascade Convolution (SWCC) module is designed to capture multiscale features and expand the receptive field. Secondly, the Adaptive Semantic Aggregation (ASA) module facilitates adaptive and effective information fusion across two distinct feature maps. Our experiments were conducted on the publicly available cropandweed dataset and SB20 dataset. it yields improved performance over other mainstream segmentation models. Specifically, SWFormer with 52.33M/527.51GFLOPs achieves an mAP of 76.54% and an accuracy of 83.95% on the cropandweed dataset. For the SB20 dataset, it attains an mAP of 61.24% and an accuracy of 79.47%. Overall, the evaluation clearly demonstrates our proposed SWFormer is conducive to promoting further research in the area of Precision Agriculture.

Green urban transition: interdisciplinary insights on green façades design in hot climates as one of crucial strategies for low-carbon development

AbstractThe purpose of this study is to present interdisciplinary insights on vertical green systems (VGS) design in hot climate zones. The approach to the VGS design proposed in this article combines architectural design, structural systems, green building and township development with sustainable urban farming, circularity, waste and water management. On the basis of in-depth study on green façade performance in hot climates, the authors identified methods derived from various fields of science which, when correctly combined, enable effective protection of plants against excessive solar radiation. The main finding of the research is the identification of the most effective combination of methods supporting healthy growth of the VGS. This set of solutions includes orientation of the façade to cardinal directions correlated with appropriate plants selection, shading strategies, such as passive and kinetic shields and organic fertilizers supporting plants’ resilience. The authors presented key challenges and opportunities of VGS application and discussed their role in the decarbonization process of the building sector. The multidisciplinary analysis of the state of knowledge highlighted areas that require further investigation, such as the practical implementation of proposed strategies and their effectiveness in real-world scenarios. Graphical abstract

Hybridization of deep learning, nonlinear system identification and ensemble tree intelligence algorithms for pan evaporation estimation

A reliable pan evaporation (Epan) estimation over a daily scale is vital for sustainable water and agriculture management, especially for designing water use allocations, irrigation system management, and demand and utilization assessments. However, designing a reliable computational methodology is a challenging task for Epan estimation due to its stochastic and random characteristics. In this study, the potential of five machine learning (ML) models namely Hammerstein-Weiner (HW), Random Forest (RF), Boosted Regression Tree (BRT), Long Short-Term Memory (LSTM), and Stepwise Linear Regression (SWLR) models were evaluated for Epan modeling in Lake Hawassa catchment, Ethiopia. Afterward, hybrid SWLR-LSTM, SWLR-RF, SWLR-BRT, and SWLR-HW models were developed to benefit from the strength of the non-linear and linear characteristics of the models. The estimation results were evaluated using determination coefficient (R2), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE) and Root Mean Square Error (RMSE), Index of Agreement (IA) and Kling-Gupta efficiency (KGE). The performance analysis result of individual models showed that the RF model led to more accurate estimation with NSE = 0.938, IA = 0.956, R2 = 0.94, KGE = 0.852, RMSE = 0.483 mm/day and Pbias = -1.193 mm/day during the validation phase. Among the hybrid models, SWLR-RF provides the best predictive performance improving SWLR, HW, LSTM, BRT and RF by 26.563 %, 21.348 %, 15.577 %, 9.091 % and 3.514 %, respectively based on the validation phase NSE value. Generally, the results of the current study demonstrated the capability of deep learning, ensemble tree and hybrid linear-nonlinear models for Epan estimation in the data-scarce catchment.

Assessing the Evolution of Stability and Maturity in Co-Composting Sheep Manure with Green Waste Using Physico-Chemical and Biological Properties and Statistical Analyses: A Case Study of Botanique Garden in Rabat, Morocco

Organic waste utilization stands as a pivotal approach to ecological and economic sustainability. This study aimed to assess the stability, maturity, and evolution of co-composts comprising various blends of green waste (GW) and sheep manure (SM). Employing a diverse array of physico-chemical and biological parameters, we investigated the co-composting process over 120 days. Three types of garden waste (mixture of green waste (MGW), fallen leaves (FL), and grass cutting (GC)) were utilized. The results revealed significant compost transformation, evident by odor and insect absence and a shift to dark brown coloration, indicating maturation. The compost C2, derived from FL, exhibited superior soil amendment potential. Significantly, it exhibited a pH level of 6.80, an EC of 2.45 mS/cm, and an OM content of 55%, along with a C/N ratio of 16.15. Analysis of the macronutrients revealed values of 1.98% for TN, 3.22% for TP, and 0.61% for K. Crucially, the compost showed no phytotoxic effects and boasted a high GI of 94.20% and a low respiration rate of 4.02 mg/50 g, indicating its stability and appropriateness for agricultural application. Our findings underscore compost’s potential as an eco-friendly soil amendment, offering valuable insights for sustainable agricultural management and supporting the circular economy.

Adoption potential of black soldier fly (Hermetia illucens (L.), Diptera: Stratiomyidae) larvae composting technology among smallholder farmers in Greater Ahafo-Ano, Ashanti region of Ghana

As the world’s population increases, the growing demand for food intensifies the generation of agricultural waste, leading to several environmental issues. Intensive research indicates black soldier fly (BSF) larvae Hermetia illucens (Linnaeus, 1758) as efficient converters of organic waste into nutrient-rich biomass suitable for animal feed. Using a structured questionnaire and volunteer farmers (N = 595), we investigated the potential for adoption of BSF larvae composting technology for sustainable agricultural waste management in Greater Ahafo-Ano. Almost all surveyed farmers declared they generated a significant amount of biowaste on their farms and were willing to learn how to use the BSF-based technology to transform it into value-added products. The waste generated was mainly disposed of in pits at Ahafo-Ano South-East (56.2%), by composting at Ahafo-Ano South-West (34.9%) and by sale at Ahafo-Ano North (34.4%). Across the three districts, awareness of the BSF was very low – 14.5% in Ahafo-Ano South-East, 14.1% in Ahafo-Ano South-West and 0.5% in Ahafo-Ano North. However, high acceptance of the technology was recorded. It was found that about 8% of farmers surveyed in Ahafo-Ano South-East had already tried BSF farming on a small scale. Indicating this district as a good entry point to introduce the technology into Greater Ahafo-Ano.

Circular Agriculture Approach: Towards a Sustainable Agricultural Waste Management in the Kingdom of Bahrain

Circular Agriculture Approach: Towards a Sustainable Agricultural Waste Management in the Kingdom of Bahrain

Enhancing agricultural output: Investigating the impact of advanced organic formulations on crop productivity, nutrient use efficiency, and profitability in a multi-crop system

The significance of integrating agricultural by-products such as paddy husk ash (PHA) and potato peels with organic fertilizers lies in enhancing soil fertility, increasing crop yields, and reducing reliance on traditional organic fertilizers like farmyard manure (FYM) or compost alone. Grounded in sustainable agriculture and nutrient management frameworks, this study examines the impact of diverse formulations derived from agricultural waste on productivity, nutrient efficiency, and profitability in a pigeon pea–vegetable mustard–okra cropping system. A two-year field experiment (2020–2022) at ICAR-IARI, New Delhi tested seven nutrient sources viz., (T1) control, (T2) 100% RDN through FYM, (T3) 100% RDN through improved RRC, (T4) 100% RDN through PHA based formulation, (T5) 75% RDN through PHA based formulation, (T6) 100% RDN through PPC based formulation and (T7) 75% RDN through PPC based formulation that were tested in RBD and replicated thrice. Treatment T4 had significant effect on seed yield of pigeon pea (1.89 ± 0.09 and 1.97 ± 0.12 t ha−1), leaf yield of vegetable mustard (81.57 ± 4.59 and 82.97 ± 4.17 t ha−1), and fruit yield of okra (13.54 ± 0.82 and 13.78 ± 0.81 t ha−1) grown in rotation, followed by treatment T6 and T2 during both the years respectively over control. Enhanced system uptake of N, P and K along with system gross and net returns in T4, showed increases of 78.9%, 83.8%, 72.4%, 54.4% and 56.8% in the first year and 77.5%, 80.8%, 77.7%, 54.8% and 57.4% in the second year, respectively, over control. Treatment T4 significantly improved apparent recovery by 66.3% and 69.2% in pigeon pea, 64.7% and 47.9% in vegetable mustard, and 72.7% and 79.4% in okra over T3, averaged across two years. Based on the above findings, (T4) 100% RDN through PHA-based formulation, and (T6) 100% RDN through PPC-based formulation can be recommended for areas with a shortage of FYM but availability of rice husk ash/potato peels for sustainable agricultural wastes and improved sustainability.

Zinc Oxide Nanoparticles: An Influential Element in Alleviating Salt Stress in Quinoa (Chenopodium quinoa L. Cv Atlas)

Climate change has intensified abiotic stresses, notably salinity, detrimentally affecting crop yield. To counter these effects, nanomaterials have emerged as a promising tool to mitigate the adverse impacts on plant growth and development. Specifically, zinc oxide nanoparticles (ZnO-NPs) have demonstrated efficacy in facilitating a gradual release of zinc, thus enhancing its bioavailability to plants. With the goal of ensuring sustainable plant production, our aim was to examine how green-synthesized ZnO-NPs influence the seedling growth of quinoa (Chenopodium quinoa L. Cv Atlas) under conditions of salinity stress. To induce salt stress, solutions with three different NaCl concentrations (0, 100, and 200 mM) were prepared. Additionally, Zn and ZnO-NPs were administered at four different concentrations (0, 50, 100, and 200 ppm). In this study, plant height (cm), plant weight (g), plant diameter (mm), chlorophyll content (SPAD), K/Na value, Ca/Na value, antioxidant enzyme activities (SOD: EU g−1 leaf; CAT: EU g−1 leaf; POD: EU g−1 leaf), H2O2 (mmol kg−1), MDA (nmol g−1 DW), proline (µg g−1 FW), and sucrose (g L−1), content parameters were measured. XRD analysis confirmed the crystalline structure of ZnO nanoparticles with identified planes. Salinity stress significantly reduced plant metrics and altered ion ratios, while increasing oxidative stress indicators and osmolytes. Conversely, Zn and ZnO-NPs mitigated these effects, reducing oxidative damage and enhancing enzyme activities. This supports Zn’s role in limiting salinity uptake and improving physiological responses in quinoa seedlings, suggesting a promising strategy for enhancing crop resilience. Overall, this study underscores nanomaterials’ potential in sustainable agriculture and stress management.

Empowering Communities with Integrated Pest Management for Sustainable Agriculture

Background: The aim of this community development initiative, centered around community engagement, is to address the pressing issue of declining agricultural productivity due to pest infestations in the Asahan Regency, a developing area. Through educational and training programs, the initiative seeks to advocate for Integrated Pest Management (IPM) practices and safe pesticide usage among farmers, while also addressing the environmental impacts of chemical pesticide use. Contribution: The primary focus is to gauge the impact of these initiatives in fostering sustainable and environmentally responsible agricultural practices in Asahan Regency, ultimately enhancing the overall well-being of the farming community. Method: A total of 50 farmers actively participated in these programs, all of whom derived substantial benefits that directly contributed to the improvement of their livelihoods. Results: The implementation of IPM with a community development approach has the potential to significantly enhance paddy rice productivity while promoting sustainable agriculture Conclusion: By involving the local community, improving pest control methods, reducing environmental impact, and fostering knowledge sharing, this approach can lead to positive outcomes for both farmers and the environment

Multidimensional analysis for assessing sustainability determinants of rice farming in Siak, Riau

Abstract This study was conducted in five villages in Bunga Raya District (Kemuning Muda, Bunga Raya, Tuah Indrapura, Jayapura, and Buantan Lestari), the rice production centers of Siak Regency, Riau. The research location was selected based on the largest percentage of rice field area. The research was conducted from January to July 2019. This research aims to determine sustainable lowland rice farming management variables for future lowland rice farming development in Bunga Raya District, Siak Regency. Purposive sampling was used to choose the 82 sample farmers, who were selected using the Slovin equation. The respondents are selected with a margin of error of 7% of the total population of farmer families. Data was gathered through the use of interview techniques, questionnaires, and field observations. Multi-dimensional scaling (MDS) was used to analyze sustainable rice farming management factors after updating the Rapfish program. According to the findings of the sustainability study of 42 studied variables, 12 of them show sensitivity. Five of the ten stakeholder needs attributes were examined to provide sensitivity. The combined analysis of current prospective sustainability with stakeholders yielded five attributes determining factors for the sustainability of lowland rice farming in Bunga Raya: land conversion, participation in training/extension services, pesticide use, agro-tourism potential, and farmer capital availability. Another recommendation that policy makers need to consider, which is no less important, is to always preserve the rice field ecosystem by developing natural and human resources, so that increasing the welfare of farmers and increasing regional income can be achieved.

Differential responses of the phyllosphere abundant and rare microbes of Eucommia ulmoides to phytohormones

Phyllosphere microbiota play a crucial role in plant productivity and adaptation, and the abundant and rare microbial taxa often possess distinct characteristics and ecological functions. However, it is unclear whether the different subcommunities of phyllosphere microbiota respond variably to the factors that influence their formation, which limits the understanding of community assembly. The effects of two phytohormones, namely, indole-3-acetic acid (IAA) and N6-(delta 2-isopentenyl)-adenine (IP), on the phyllosphere microbial subcommunities of Eucommia ulmoides were investigated using potted experiments. The results demonstrated that the phytohormones induced significant variations in the composition, diversity, and function of the abundant microbial subcommunity in the phyllosphere of E. ulmoides, however, their effects on the rare subcommunity were negligible, and their effects on the moderate subcommunity were between those of the abundant and rare taxa. The phytohormones also induced significant alterations in the phenotypic and physiological properties of E. ulmoides, which indirectly affected the phyllosphere microbial community. Leaf thickness and average leaf area were the main phenotypic variables that affected the composition of the phyllosphere microbial community. The total alkaloid content and activity of superoxide dismutase (SOD) were the main physiological variables that affected the composition of the phyllosphere microbial community. The phenotypic and physiological indices of E. ulmoides explained the variations in the phyllosphere microbial subcommunities in descending order: abundant > moderate > rare taxa. These variables explained a significant proportion of the variations in the abundant taxa, and an insignificant proportion of the variations in the rare taxa. This study improves our understanding of the assembly of the phyllosphere microbiota, which provides important theoretical knowledge for future sustainable agriculture and forestry management based on the precise regulation of phyllosphere microbiota.

Soil fertility based on mineralogical properties to support sustainable agriculture management

<p>Detailed information on soil mineral composition has been crucial in providing the basis for designing sustainable agricultural practices, as this information offers long-term insights into natural soil fertility. This research aimed to further investigate the characteristics of soil mineral composition as a basis for managing soil fertility. Three soil profiles representing three different parent materials have been examined in the field, and soil samples have been collected for laboratory analysis. Profile 1 originates from the Raung Volcano Rock Formation (Qhvr). Profile 2 originates from the Sukamade Formation (Toms). Profile 3 originates from Puger Formation (Tmp). The presence of Mount Raung volcanic influence results in distinct soil characteristics in Profiles 2 and 3 compared to typical sedimentary and karst rock soils. The sand fraction minerals in all three profiles are predominantly composed of opaque minerals, followed by rock fragments, ferromagnesian mineral series (olivine, augite, hypersthene, and hornblende), plagioclase minerals (anorthite, biotite, and labradorite), iron concretions, epidote, and tourmaline. The clay fraction minerals in all three profiles consist of illite, kaolinite, and quartz. The minerals illite and ferromagnesian groups in all three profiles play a significant role in increasing the availability of potassium, calcium, and magnesium nutrients, thereby reducing the need for fertilizers for these elements. The soils in all three profiles naturally possess good fertility; however, designing a sustainable agricultural system requires consideration of the morphology, landform, and climate of all three profiles.</p>

Comparison of direct and indirect soil organic carbon prediction at farm field scale

To advance sustainable and resilient agricultural management policies, especially during land use changes, it is imperative to monitor, report, and verify soil organic carbon (SOC) content rigorously to inform its stock. However, conventional methods often entail challenging, time-consuming, and costly direct soil measurements. Integrating data from long-term experiments (LTEs) with freely available remote sensing (RS) techniques presents exciting prospects for assessing SOC temporal and spatial change. The objective of this study was to develop a low-cost, field-based statistical model that could be used as a decision-making aid to understand the temporal and spatial variation of SOC content in temperate farmland under different land use and management. A ten-year dataset from the North Wyke Farm Platform, a 20-field, LTE system established in southwestern England in 2010, was used as a case study in conjunction with an RS dataset. Linear, additive and mixed regression models were compared for predicting SOC content based upon combinations of environmental variables that are freely accessible (termed open) and those that require direct measurement or farmer questionnaires (termed closed). These included an RS-derived Ecosystem Services Provision Index (ESPI), topography (slope, aspect), weather (temperature, precipitation), soil (soil units, total nitrogen [TN], pH), and field management practices. Additive models (specifically Generalised Additive Models (GAMs)) were found to be the most effective at predicting space-time SOC variability. When the combined open and closed factors (excluding TN) were considered, significant predictors of SOC were: management related to ploughing being the most important predictor, soil unit (class), aspect, and temperature (GAM fit with a normalised RMSE = 9.1%, equivalent to 0.4% of SOC content). The relative strength of the best-fitting GAM with open data only, which included ESPI, aspect, and slope (normalised RMSE = 13.0%, equivalent to 0.6% of SOC content), suggested that this more practical and cost-effective model enables sufficiently accurate prediction of SOC.

Enhancing Soil Health and Fertility Management for Sustainable Agriculture: A Review

In modern agriculture, ensuring soil health and effective fertility management are paramount for sustainable crop production and environmental stewardship. This review article comprehensively explores a spectrum of strategies aimed at enhancing soil health and fertility management within the context of sustainable agriculture. Beginning with an overview of the pivotal role soil health plays in agricultural systems, the review meticulously examines the significance of adopting sound soil fertility management practices to sustain soil productivity while mitigating adverse environmental impacts. Traditional and innovative approaches to soil management are thoroughly discussed, encompassing a range of practices such as organic amendments, cover cropping, crop rotation, reduced tillage, and integrated nutrient management. These practices, deeply rooted in agricultural traditions, are shown to enhance soil structure, foster nutrient cycling, and promote beneficial soil microbial communities, thereby contributing to long-term soil health and productivity. Furthermore, the review elucidates emerging technologies and methodologies that hold promise for revolutionizing soil health and fertility management in sustainable farming systems. Precision agriculture techniques leveraging GPS, remote sensing, and data analytics are highlighted for their potential to optimize resource use and improve crop management practices. Biochar application, a burgeoning area of research, is explored for its ability to enhance soil fertility, sequester carbon, and improve soil water retention. Additionally, manipulation of the soil microbiome through innovative techniques presents exciting opportunities for enhancing soil health and resilience, the review underscores the importance of continued research, education, and policy support in promoting sustainable soil management practices. Recommendations for future research directions are provided, emphasizing the need for interdisciplinary collaborations, long-term field studies, and farmer participatory research. Practical implications for farmers and policymakers are also delineated, emphasizing the imperative of adopting holistic approaches to soil management that integrate traditional wisdom with cutting-edge technologies. By embracing sustainable soil management practices, farmers and policymakers can safeguard soil health, enhance agricultural productivity, and ensure the long-term sustainability of our food systems and environment.

Evaluation of local compost methods for soil management in northwestern Nigeria: Advanced scientific theories and economic values

Compost technology is an advanced technology that involves the transformation of organic materials and organic waste/sludge into organic manures/fertilizers. This method was locally applied by many rural farmers in northwestern Nigeria long ago. It has been used by farmers as the best method for soil quality and soil fertility management in dryland and fadama areas of the region. This research paper was built to provide scientific advances in common compost methods in northwestern Nigeria. Approximately twelve (12) different methods were surveyed and explained based on cultural practices and scientific theories. These composting methods include dumping, hole-gathering, burning, container-packed, open-build, farm-tunnel, pond-manure, lake-fill-in, irrigated-bed-in, home-bin, farm-cattle and sheet-composting methods. These sets of methods were provided with specific instructions, fundamentals and theoretical justifications. There have been broad advances in how these methods can be used to generate revenues, create jobs, ensure safe and well-frained environments, improve food security and decrease or to some extent control the spread of malaria and other related human diseases in northwestern Nigeria. This study provides suggestions for better use of organic materials/sludge, development of bioorganic fertilizer industries, and continued revision of sustainable agricultural management with negative chemicals.

The Role of Artificial Intelligence in Sustainable Agriculture and Waste Management: Towards a Green Future

The Role of Artificial Intelligence in Sustainable Agriculture and Waste Management: Towards a Green Future