Agricultural Management Practices Research Articles

Tillage and mulching effects on carbon stabilization in physical and chemical pools of soil organic matter in a coarse textured soil

Characterization of soil organic carbon (SOC) in terms of size, storage inside aggregates and chemical recalcitrance is vital to understand mechanisms of its stabilization and to devise climate smart agricultural management practices. Tillage and residue retention are known to influence carbon (C) storage within soil aggregates and its accumulation as particulate organic matter as well as organomineral complexes. However, the effect of tillage and crop residue retention on C stabilization in coarse textured soils through various mechanisms is not well understood. We studied the effect of conservation agriculture involving no tillage with surface residue mulch (NTM) in maize-wheat sequence on particulate (POC) and mineral associated organic C (MinOC), C storage within aggregates and acid non-hydrolysable C (NHC) in a sandy loam soil. Compared to conventional tillage without residue retention (CTM0), the NTM improved SOC stocks by 23% in top 15-cm soil and significantly increased coarse POC (∼92 to 284%) and fine POC (67 to 123%) with relatively little effect on MinOC. This indicated that MinOC had relatively small contribution towards SOC stabilization in coarse textured sandy loam soils with limited potential to form organomineral complexes. The results further showed that the effects of NTM were brought about by improved aggregate stability and C preservation inside macroaggregates of size >1 mm. Furthermore, greater amount of SOC (2.64 g C kg−1) and macroaggregate associated C (0.35–0.59 g C kg−1) occurred in recalcitrant forms (NHC) under NTM compared to conventional (CT) and deep tillage (DT). The NTM also impacted the belowground C input through improved root length density (RLD) and development of fibrous roots expressed as specific root length (SRL), which influenced SOC build-up and stabilization. It is concluded that compared to the existing practice of CTM0, the conservation agriculture involving NT with residue retention leads to SOC sequestration in coarse textured soils. Its large scale adoption, besides helping in climate change mitigation will lead to soil health improvement.

Water-Saving Irrigation and N Reduction Increased the Rice Harvest Index, Enhanced Yield and Resource Use Efficiency in Northeast China

For agricultural production, improving the rice harvest index (HI) through agricultural management practices is a major means to enhance water and N utilization efficiency and yield. Both irrigation regimes and nitrogen (N) rates are important aspects of agricultural management practices. However, it is unclear how the rice HI is affected by water and N. This study aimed to clarify the mechanism underlying the response of the rice HI to water and N, and to explore the most suitable water-saving and N reduction management practices to ensure yield. A two-year (2021~2022) field experiment was conducted on Mollisols in Northeast China. In this experiment, nine treatments were performed, involving three irrigation regimes (flooded irrigation, controlled irrigation, and “thin-shallow-wet-dry” irrigation) and three N rates (110, 99, and 88 kg/ha). The rice agronomic traits and transfer of photoassimilates under different water and N management practices were observed and studied; rice HI, WUE, and the NUE of rice was calculated and analyzed. The highest HI was achieved under controlled irrigation with a 99 kg/ha N rate, at values of 0.622 and 0.621 in 2021 and 2022, respectively. Controlled irrigation (CI) with an appropriate reduction in the N rate increased the proportion of productive tillers, the transfer rate of dry matter and non-structural carbohydrates (NSCs), the sugar–spikelet ratio, the grain–leaf ratio, and the leaf area index (LAI) during the heading–flowering stage. A subsequent analysis indicated that the main reason for the increase in the HI was the increase in the sugar–spikelet ratio during the heading–flowering stage. A high HI increased the rice yield by increasing the thousand-grain weight. The present study suggested that water-saving irrigation regimes and appropriate N reduction not only led to water and fertilizer resource savings but also improved agronomic characteristics during rice growth and enhanced transport capacity. Thus, these practices improved the rice HI and have enormous potential for increasing yield. Therefore, regulating the rice HI through water and N management methods should be considered an important strategy for improving rice yield.

Climate-smart water management practices for sustainable agriculture in Uganda

ABSTRACT The aim of the study is to assess climate-smart water management practices for sustainable agriculture in the Lake Mutanda catchment area, Kisoro District, Uganda. The study was led by specific objectives, specifically: assessing the effects of flooding on agricultural productivity, and smallholder farmer's responses to rainfall variability for the past 20–30 years, investigating climate-smart water management practices for sustainable agriculture, and analyzing the seasonal variations of the physicochemical water quality parameters. The study used a mixed research design, which used mixed methodologies to collect and analyze data using both quantitative and qualitative approaches. A straightforward random sampling approach was used to determine the sample size. Three hundred and ninety-seven respondents participated in the study. Quantitative data was analyzed using the R programming language, and qualitative data was analyzed using ATLAS.ti. The study identified climate-smart water management practices like mulching, terracing, contour farming, conservation tillage, agroforestry, and fertilizer management. Most of the respondents (29.7%) cited a reduction in soil fertility, 57.9% of the total sampled respondents had noticed changes in rainfall variability, and 67.3% reported a decrease in rainfall. The majority (38% of the participants) use mulching to reduce running water into the catchment areas. Turbidity, temperature, and DO were beyond the WHO-permitted levels.

Automated Early Detection of Plant Diseases a Machine Learning Approach

This study introduces an innovative strategy for the early detection of plant diseases leveraging machine learning algorithms, a crucial step towards bolstering global food security. Despite the importance of early disease detection for mitigating crop losses, inadequate infrastructure often hinders swift and accurate identification. This paper addresses these challenges by utilizing the Random Forest algorithm to distinguish between healthy and diseased plant leaves using a specially curated dataset. Our proposed method involves comprehensive phases of dataset creation, feature extraction, classifier training, and subsequent classification. For the extraction of image features, the Histogram of Oriented Gradient (HOG) technique is utilized, offering a robust method for image analysis. Embracing the capabilities of publicly accessible, large-scale datasets, our method provides a scalable solution to plant disease detection. The empirical results demonstrate promising levels of accuracy in disease monitoring, presenting transformative potential for disease management practices in agriculture. Particularly in resourceconstrained settings, our machine learning approach paves the way for efficient and effective plant disease detection, carrying significant implications for future crop protection strategies.

Functionality of arbuscular mycorrhizal fungi varies across different growth stages of maize under drought conditions

Physio-biochemical regulations governing crop growth period are pivotal for drought adaptation. Yet, the extent to which functionality of arbuscular mycorrhizal fungi (AM fungi) varies across different stages of maize growth under drought conditions remains uncertain. Therefore, periodic functionality of two different AM fungi i.e., Rhizophagus irregularis SUN16 and Glomus monosporum WUM11 were assessed at jointing, silking, and pre-harvest stages of maize subjected to different soil moisture gradients i.e., well-watered (80% SMC (soil moisture contents)), moderate drought (60% SMC), and severe drought (40% SMC). The study found that AM fungi significantly (p < 0.05) affected various morpho-physiological and biochemical parameters at different growth stages of maize under drought. As the plants matured, AM fungi enhanced root colonization, glomalin contents, and microbial biomass, leading to increased nutrient uptake and antioxidant activity. This boosted AM fungal activity ultimately improved photosynthetic efficiency, evident in increased photosynthetic pigments and photosynthesis. Notably, R. irregularis and G. monosporum improved water use efficiency and mycorrhizal dependency at critical growth stages like silking and pre-harvest, indicating their potential for drought resilience to stabilize yield. The principal component analysis highlighted distinct plant responses to drought across growth stages and AM fungi, emphasizing the importance of early-stage sensitivity. These findings underscore the potential of incorporating AM fungi into agricultural management practices to enhance physiological and biochemical responses, ultimately improving drought tolerance and yield in dryland maize cultivation.

Strategic, tactical, complex and simple changes to farm systems

AbstractPressure has grown to reconfigure commercial farm systems to ameliorate resource degradation as governments have increasingly sought to halt or reverse the damaging effects of agriculture on the environment. Now, climate change is creating additional pressure to redesign farm systems to withstand more variable, and historically more extreme, seasonal conditions. Meeting these pressures is thought to require a combination of responses ranging from incremental adjustments to farm systems through to their complete transformation into entirely new enterprises. Implementing each of these entails different resources, time horizons, skills, knowledge and planning, and this has corresponding consequences for any government or industry policies intended to promote adoption or compliance. While there is an extensive literature on the adoption of technology and management practices in agriculture, the literature on different types of change to farm systems, and the criteria for distinguishing between types, is limited. We describe a novel approach to classifying changes to farm systems by integrating a method for describing the strategic and tactical flexibility of farm systems with a method for describing the complexity of changes to farm systems. The resulting classification provides a framework for inferring the nature of the resources, skills, knowledge, planning and time needed to implement the different types of change. We provide illustrative examples drawn from a series of interviews with farmers of each type of change and discuss the implications for extension, agricultural and environmental policy and policy regarding adaptation to climate change. We found that heterogeneity in farm systems translates into heterogeneity in adoption processes. We believe that our approach provides a practical way of translating commonly used broad descriptors of adaptation, such as ‘incremental’, ‘systems’ and ‘transformational’, into the specific type of the change that farmers must make to their farm systems to enact adaptations, with consequent implications for the effective policy support of adaptation.

Identifying Credit Accessibility Mechanisms for Conservation Agriculture Farmers in Cambodia

As the expected cost of conservation agriculture (CA) inputs becomes an issue for farmers, financial institutions (FIs) play an essential role in promoting CA, a set of agricultural management practices with multiple positive effects. This research aimed to determine influencing factors, to rank challenges, and identify mechanisms for farmers to access agricultural credit for adoption of CA management practices in Cambodia. It was administered by conducting a survey of 242 randomly selected households for face-to-face interviews and conducting key informant interviews from purposive samples of 28 participants in Battambang and Preah Vihear provinces. The results indicated that influencing factors, including the family, adult labor and total farm size, had a positive relationship with farmers’ accessibility to agricultural credit, whereas age was negative. However, education year, farm size for main crops, on-farm income and farm experience were not significantly associated. High interest rates were the significant first-order challenge ranked, followed by document process complication, limited agricultural credit information, limited collateral security and a few other challenges. Support and improved process mechanisms to enhance credit accessibility are required to engage with multiple stakeholders, including farmers, FIs, non-government organizations (NGOs) and government officers. There has been a reduction in agricultural credit interest rates and incentives for importing CA inputs by the government, while provision of information support for agribusiness plans by NGOs which have implemented development project activities were considered as the main support mechanism. An improved process mechanism at the farmers’ level needs to include access to credit with low interest rates and a straightforward documentation process, whereas the FI level requires a business plan for lending decisions. It is evident that high lending rates hinder access to agricultural credit and the improvement of support and improved process mechanisms are necessary to better promote CA practices among farmers in Cambodia.

A Review of Greenhouse Gas Emissions from Agricultural Soil

Greenhouse gases (GHGs) like nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4) are both emitted and removed by soils. Accurate worldwide allocations of carbon budget are essential for land use planning, global climate change, and climate-related research. Precise measurements, drivers, and mitigation strategies are necessary, given agricultural soil’s significant potential storage and emission capacities. Different agricultural management practices cause greenhouse gas (GHG) emissions into the atmosphere and contribute to anthropogenic emissions. Agricultural soils can generate 70% of the world’s manmade N2O emissions and also behave as a CO2 sink and a source of organic carbon and as producers and consumers of CH4. When it comes to agronomic management, the source and sink of all these GHGs are distinct. Therefore, several approaches to measuring GHG emissions from agricultural soils are available and can be categorized into chamber systems and remote sensing approaches. Sustainable agriculture stands out as a viable and transformative approach to increase agricultural efficiency while addressing the challenge of GHG emissions. Incorporating advanced technologies, precise data analytics, and site-specific management practices can offer a pathway to mitigate GHG emissions, thereby reducing the global warming potential (GWP). Therefore, this review paper focuses solely on the drivers influencing and involving soil emissions and on quantification approaches for GHG emissions. In addition, mitigation practices aimed at optimizing GHG emissions from agricultural soils are highlighted.

Estimating Agricultural Irrigation Water Usage in Delaware, USA

Irrigation is an important agricultural management practice and the second largest consumer of freshwater resources in Delaware. As more farmland is converted to irrigated agriculture, it is crucial that water resource managers be able to determine reasonable estimates of irrigation water usage in order to protect the resource. This study used a soil water balance approach to simulate irrigation for corn and soybeans in Delaware (USA). The simulations were divided into four scenarios to determine which irrigation management method best represents agricultural irrigation water usage in Delaware. Two scenarios utilized an evapotranspiration-based (ET-based) approach using meteorological data from the Delaware Environmental Observing System (DEOS) with a soil water availability threshold to determine when and how much to irrigate. A second set of scenarios used a calendar-based approach and a rain gauge trigger to simulate irrigation. Analyses were performed to examine the seasonal and spatial variability of irrigation in Delaware and to compare simulated irrigation data to reported water use data provided by the Delaware Department of Natural Resources and Environmental Control’s Water Allocation Permit program. Seasonal irrigation varied due to environmental as well as irrigation decision making factors. Spatially, irrigation varied primarily as a result of the soil water holding properties. The ET-based scenario with a fixed amount of irrigation had the best agreement with the reported irrigated water use data. This study demonstrated the utility of high-resolution, environmental data with a soil water balance approach to improve estimates of irrigated water usage at the state and regional scale.

Potentially Toxic Metals: Their Effects on the Soil-Human Health Continuum

As the foundation of nutritious foods lies in the soil, the consumption of crops grown in contaminated soils may pose an elevated risk of health issues through the soil-plant-human pathway. The impact of heavy metals (HMs) and metalloids on the physiological and biochemical responses of plants can have adverse effects on both growth and yield. The excessive accumulation of these substances in plant tissues poses a significant challenge to public health. HMs possess the capability not only to function as carcinogens but also to act as co-carcinogens, thereby activating specific chemical compounds. According to the World Health Organization (WHO) reports, the target values, representing the desirable maximum concentrations of HMs in the soil, follow the order: Cr&gt;Pb&gt;Zn&gt;Cu&gt;Ni&gt;Cd. This implies that Cd poses the highest potential risk, given its target value of 0.8 mg/kg, while Cr carries the lowest potential risk, with a target value of 100 mg/kg. Various agricultural management practices are recognized as significant pathways that induce the accumulation of metals in the soil and the surrounding environment. Hence, understanding the origin and status of HMs in the environment, along with assessing their potential risks and developing strategies to mitigate these risks, becomes crucial. This study aims to evaluate different facets of the soil-human health continuum concerning potentially toxic metals.

Farmers’ perception and strategies for mitigating and adapting to climate change impacts in the agricultural sector

Abstract The objectives of this study are 1) to determine farmers’ perceptions, adaptation and mitigation towards climate change; 2) to provide recommendations for appropriate adaptation strategies towards climate change. The research methodology employed was qualitative, involving a literature review and focus group discussions (FGD). The study’s findings reveal several key indicators within the farming community. Farmer perceptions encompass their responses to external factors, comprehension, and assessment of climate change. Adaptation indicators comprise perception, level of exposure, risk tolerance, sociodemographic characteristics, and adaptive capacity. Mitigation strategies involve enhancing productivity and efficiency, implementing specialized technology options, and adapting agricultural management practices. Farmer perceptions highlight the recognition of climate change as a relatively recent phenomenon, coupled with its palpable impact on agriculture. Adaptations made by farmers include pesticide use, adjusted planting seasons, exploring alternative livelihoods, and land cultivation. Mitigation efforts entail the management of water resources and the adoption of cutting-edge agricultural technologies. These indicators, combined with the insights from focus group discussions (FGD), can serve as valuable tools for developing instruments to assess climate change within the agricultural sector.

Effect of Soil pH Variation on Peanut Plant Growth

This research investigates the qualitative effects of soil pH variation on peanut plant growth, aiming to provide insights into the complex interactions between soil properties and plant responses in agricultural ecosystems. Through semi-structured interviews with ten informants, supplemented by insights synthesized from prior research and theoretical frameworks, the study explores perceived relationships, implications for soil health and nutrient availability, challenges, opportunities, and practical implications for peanut production. Key findings highlight the critical influence of soil pH on peanut growth and productivity, with deviations from optimal pH levels affecting nutrient availability, root health, and overall plant vigor. Participants underscore the importance of targeted soil management practices, including lime application, organic matter incorporation, and crop rotation, in optimizing soil pH and supporting resilient peanut production systems. The study identifies challenges in soil pH management, such as limited accessibility of soil testing services and inadequate farmer knowledge, while also highlighting opportunities for innovation and improvement. The insights gleaned from the study contribute to advancing knowledge in agricultural sciences and inform evidence-based soil management practices for sustainable peanut production.

Interpretable machine learning decodes soil microbiome’s response to drought stress

BackgroundExtreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil bacterial microbiota and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable machine learning holds immense potential for drought stress classification of soil based on marker taxa.ResultsThis study demonstrates that the 16S rRNA-based metagenomic approach of Differential Abundance Analysis methods and machine learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil bacterial micobiome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested.ConclusionsIn the detection of drought stress in soil bacterial microbiota, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.

Causal inference approaches reveal both positive and negative unintended effects of agricultural and urban management practices on instream biological condition

Agricultural and urban management practices (MPs) are primarily designed and implemented to reduce nutrient and sediment concentrations in streams. However, there is growing interest in determining if MPs produce any unintended positive effects, or co-benefits, to instream biological and habitat conditions. Identifying co-benefits is challenging though because of confounding variables (i.e., those that affect both where MPs are applied and stream biota), which can be accounted for in novel causal inference approaches. Here, we used two causal inference approaches, propensity score matching (PSM) and Bayesian network learning (BNL), to identify potential MP co-benefits in the Chesapeake Bay watershed portion of Maryland, USA. Specifically, we examined how MPs may modify instream conditions that impact fish and macroinvertebrate indices of biotic integrity (IBI) and functional and taxonomic endpoints. We found evidence of positive unintended effects of MPs for both benthic macroinvertebrates and fish indicated by higher IBI scores and specific endpoints like the number of scraper macroinvertebrate taxa and lithophilic spawning fish taxa in a subset of regions. However, our results also suggest MPs have negative unintended effects, especially on sensitive benthic macroinvertebrate taxa and key instream habitat and water quality metrics like specific conductivity. Overall, our results suggest MPs offer co-benefits in some regions and catchments with largely degraded conditions but can have negative unintended effects in some regions, especially in catchments with good biological conditions. We suggest the number and types of MPs drove these mixed results and highlight carefully designed MP implementation that incorporates instream biological data at the catchment scale could facilitate co-benefits to instream biological conditions. Our study underscores the need for more research on identifying effects of individual MP types on instream biological and habitat conditions.

Effect of Biochar Application on the Improvement of Soil Properties and Buckwheat (Fagopyrum esculentum Moench) Yield on Two Contrasting Soil Types in a Semi-Arid Region of Inner Mongolia

Biochar application to soil is widely recognized as a promising agricultural management practice to increase crop production by enhancing the physical, chemical, and microbiological properties of the soil. Despite the fact that numerous studies have investigated biochar production and alterations in soil properties, the effects of biochar on contrasting soils within the same region remain poorly understood, especially for semi-arid regions. Therefore, a three-year field experiment was initiated in 2020 wherein biochar was applied once to a buckwheat field at rates of 0, 20, 40, and 60 Mg ha−1 (BC0: no biochar; BC1: 20 Mg ha−1; BC2: 40 Mg ha−1; BC3: 60 Mg ha−1) for two soil types (aeolian sandy and grey meadow soil) in the northeast of Inner Mongolia, China. The soil water storage (SWS), nutrient contents (organic matter, available nitrogen, phosphorus, and potassium), microbial biomass (carbon, nitrogen, and phosphorus), and enzyme activities (urease, invertase, and alkaline phosphatase) were assessed at a soil depth of 0–15 cm as part of the soil quality assessment, and the buckwheat grain yield was estimated for crop productivity evaluation. The results showed that biochar amendment improved selected soil physicochemical and microbiological properties and buckwheat yields for both soil types. Compared to BC0, the biochar addition increased buckwheat yields, on average, by 11.23% to 22.82% in aeolian sandy soil and by 7.36% to 14.87% in grey meadow soil across three years. The results of principal component analysis (PCA) and random forest analysis (RFA) indicate that soil available nutrients and microbiological properties were the most important factors influencing buckwheat yields in aeolian sandy soil and grey meadow soil, respectively. Based on RFA, the available potassium, phosphorus, and nitrogen were found to contribute at rates of 13.10%, 10.06%, and 8.12%, respectively, to buckwheat yields in aeolian sandy soil. In contrast, alkaline phosphatase, urease, and microbial biomass carbon contribute 20.26%, 8.48%, and 7.82%, respectively, to the buckwheat yields in grey meadow soil. Following biochar addition, there was greater improvement in soil health and buckwheat production for aeolian sandy soil than grey meadow soil. In conclusion, biochar addition is an effective practice for improving soil health and crop productivity in both aeolian sandy soil and grey meadow soil in semi-arid regions.

Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables

This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m2/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.

Response of soil quality degradation to cultivation and soil erosion: A case study in a Mollisol region of Northeast China

Soil degradation is endangering agricultural and ecological sustainability in the Mollisol region of Northeast China. Cultivation and soil erosion are the primary drivers degrading soil quality. However, few studies have comprehensively quantified the effects of cultivation and soil erosion on soil quality degradation in Mollisols. Therefore, three sampling sites with similar landscapes, erosion environments and agricultural management practices were selected. The cultivation chronosequence was determined using a space-for-time substitution approach, including 1) forestland without cultivation (0 yr, stage I), 2) farmland with few cultivation years (< 20 yr, stage II), and 3) farmland with many cultivation years (> 80 yr, stage III). Soil erosion was quantified using the caesium-137 (137Cs) and black soil thickness. Sixteen soil physicochemical indicators were analyzed, and soil quality and its degradation were evaluated using an integrated soil quality index (SQI) and a soil degradation index (SDI), respectively. The results showed that soil erosion was profoundly accelerated by cultivation. Soil organic carbon (SOC), bulk density (BD), mean weight diameter (MWD) and soil erodibility (K) factor were selected to develop the SQI. The SQI was higher in stage I (0.61±0.05) than in stages II (0.49±0.06) and III (0.52±0.03) and decreased with increasing soil erosion rate, implying that cultivation and soil erosion exacerbated the degradation of soil quality. Furthermore, cultivation and soil erosion contributed 16.0–51.1% and 2.0–18.4% to the changes in soil quality, respectively, and their combination further amplified the impacts (8.4–61.5%). Cultivation and accelerated soil erosion driven by cultivation led to the degradation of soil quality; the former controlled the degradation processes mainly by decreasing the MWD, and the latter induced soil quality degradation by reducing the SOC and increasing the K factor. These findings are important for advancing our understanding of the driving forces and mechanisms of soil degradation in the Mollisol region.

A Comprehensive Perception of Biological Control Potential of Endophytes and Quality Refinement of Lagenaria siceraria.

Agriculture and livestock management practices known as organic farming rely more on internal processes than external inputs. Natural environments depend heavily on diversity, and organic farming incorporates both the stated purpose of fostering diversity as well as the use of diversity as a management tool. A more complete understanding of agriculture in terms of agro-ecology has begun to be questioned by the traditional reductionist approach to the study of agriculture. Therefore it is necessary to be aware more about the significance of microbes in processes including soil growth, plant nourishment, and the eradication of plant disease, pest, and weeds. In this study, fluorescent Pseudomonas strain (EFP56) and Trichoderma harzianum were studied for antifungal and antibacterial activity against four common root rot fungi and four common laboratory bacteria in vitro experiments. Furthermore, soil-borne disease surveillance and nutritional quality of Lagenaria siceraria, fluorescent Pseudomonas strain (EFP56) and Trichoderma harzianum were combined with neem cake and cotton cake to check their efficacy. Through the application of organic soil amendments in combination with biocontrol agents improved the quality of vegetables and their nutritional value by raising their polyphenol, carbohydrate, and protein content as well as enhancing antioxidant scavenging status. The experiments were conducted in pots and in fields to confirm their efficacy rate. The final outcomes also revealed greater induction of defense system, disease lessening and enriched fruit quality. Consortium of neem cake and cotton cake with bio-stimulants can regulate biotic as well as abiotic stress.

Continuous Plant-Based and Remote Sensing for Determination of Fruit Tree Water Status

Climate change poses significant challenges to agricultural productivity, making the efficient management of water resources essential for sustainable crop production. The assessment of plant water status is crucial for understanding plant physiological responses to water stress and optimizing water management practices in agriculture. Proximal and remote sensing techniques have emerged as powerful tools for the non-destructive, efficient, and spatially extensive monitoring of plant water status. This review aims to examine the recent advancements in proximal and remote sensing methodologies utilized for assessing the water status, consumption, and irrigation needs of fruit tree crops. Several proximal sensing tools have proved useful in the continuous estimation of tree water status but have strong limitations in terms of spatial variability. On the contrary, remote sensing technologies, although less precise in terms of water status estimates, can easily cover from medium to large areas with drone or satellite images. The integration of proximal and remote sensing would definitely improve plant water status assessment, resulting in higher accuracy by integrating temporal and spatial scales. This paper consists of three parts: the first part covers current plant-based proximal sensing tools, the second part covers remote sensing techniques, and the third part includes an update on the on the combined use of the two methodologies.

Unveiling Methods to Stimulate Plant Resistance against Pathogens.

Plant diseases caused by pathogens pose significant threats to agricultural productivity and food security worldwide. The traditional approach of relying on chemical pesticides for disease management has proven to be unsustainable, emphasizing the urgent need for sustainable and environmentally friendly alternatives. One promising strategy is to enhance plant resistance against pathogens through various methods. This review aims to unveil and explore effective methods for stimulating plant resistance, transforming vulnerable plants into vigilant defenders against pathogens. We discuss both conventional and innovative approaches, including genetic engineering, induced systemic resistance (ISR), priming, and the use of natural compounds. Furthermore, we analyze the underlying mechanisms involved in these methods, highlighting their potential advantages and limitations. Through an understanding of these methods, scientists and agronomists can develop novel strategies to combat plant diseases effectively while minimizing the environmental impact. Ultimately, this research offers valuable insights into harnessing the plant's innate defense mechanisms and paves the way for sustainable disease management practices in agriculture.