Indian Journal of Plant and Soil

Introduction: Niger (Guizotia abyssinica L.) is a traditional minor oilseed crop predominantly cultivated in tribal regions of India. Under conducive environmental conditions, niger is susceptible to various diseases, among which leaf spot caused by Alternaria spp. is one of the most predominant, leading to significant yield losses. In recent years, the area under niger cultivation has increased in the Konkan region of Maharashtra. Therefore, the present study was conducted to assess the influence of weather parameters on the development of Alternaria leaf spot of niger (Guizotia abyssinica L.) under Konkan agro-climatic conditions during the Kharif season of 2024. Methodology: Field experiments were carried out at the Department of Plant Pathology, College of Agriculture, Dapoli, and disease severity was recorded using a 0–9 disease rating scale. The calculated disease severity was correlated with prevailing weather factors. Results: During the experiment, disease initiation was observed from the 39th standard meteorological week, with PDI ranging from 2.19% to 44.44%. Correlation analysis revealed that maximum temperature (r = 0.923) was significantly and positively associated with disease intensity at the 5% level. In contrast, minimum temperature, morning and evening relative humidity, rainfall, and wind speed showed negative but non-significant correlations with PDI, while bright sunshine hours showed a non-significant positive association. Conclusions: The results highlight that maximum temperature one week prior to disease observation played a critical role in influencing disease severity. The study underscores the importance of weather-based disease monitoring, which can facilitate the development of forecasting models and timely management strategies to reduce yield losses in niger cultivation.

Introduction: Soil acidity is one of the environment driven factors impacting growth and productivity of rice crop. Because, leaching of base cations that causes soil acidic (lower pH), and enrichments of mineralized ions like iron and manganese in calcifuges (acid loving), mesophytic plants. Higher rainfall in summer especially in tropic and subtropics is influenced by elevated temperature due to anthropogenic activities causing climate change. Mathodology: Two laboratory experiments were carried out to investigate the effects of locally available bio-inputs viz., bay leaf, potato peel, and banana peel extracts on the physiological performance of some upland (Ahu) rice crop (varieties: Inglongkiri, Dehangi (tolerant), Lachit (susceptible), and Luit) under higher iron and manganese in acid soil conditions during the Autumn season (MarchSeptember, 2024). In the experiments, the treatments were viz.,100 ppm FeSO4 or 200 ppm Mn as MnSO4.3H2O as basal at vegetative stage (control), was kept constant. Along with this, treatments comprised of root dip before transplanting and foliar spray with bay leaf, banana peel and potato peel extracts (10% (w/v) separately at 20 days after transplanting. Natural soil without any treatment served as an absolute control. The Pearson correlation coefficient (r) was used to measure the strengh and direction of the linear relationship between two parametrs ranging from -1 to 1. Results: The studies revealed that seed yield was positively correlated with, panicle weight, panicle number per plant, seeds per panicle, test weight, HD grains, Economic yield, Biological yield, Grain Harvest Index under higher iron condition. In the other study, the application of 200 ppm of MnSO4 supplemented with aqueous bay leaf extract (10 g/100 mL) was found to be the most effective under higher manganese condition. Correlation analysis further revealed that economic yield had positive associations with panicle weight, number of panicles per plant, seeds per panicle, test weight, percentage of high-density grains, economic yield, biological yield, and grain harvest index but negatively correlated with sterility percentage. Conclusion: Among the treatments, bay leaf extract was the best and among the varieties, Dehangi consistently outperformed the other rice varieties under study ahhering the better physiological, biochemical, and yield traits.

Weathering is extensively utilized for pedospheric data that helps in understanding genetic relationships and soil characteristics to address environmental challenges. In this study, six representative soil profiles from the Nellore schist belt were analyzed for their morphology, physical properties, chemical makeup, and elemental composition. These soils, located on the rolling peneplains of the Somasila project, exhibit a red to dark reddish-brown matrix with clay-rich argillic/cambic horizons, characterized by an Ap-Bt/Bw sequence along with compact ferruginous gravels and laterite. The depth trends of redness ratings and CIELab values in moist conditions are significantly higher than in dry conditions, but they show a decreasing trend with profile depth. Additionally, am demonstrates a strong positive correlation with redness rating (Torrent) at 0.73** in moist conditions and 0.83** in dry conditions, while showing a moderate positive correlation with clay at 0.57*. In Venkateswapalem (P2) and Venkatachalam (P3), there is an increase in clay by more than 1.2 times in the B horizons, indicating argillic characteristics, yet the silt to clay ratio exceeds 0.15, suggesting a young soil with a high degree of weathering potential. These soils are slightly acidic to neutral, exhibiting low organic carbon, total nitrogen, cation exchange capacity, and exchangeable bases, resulting in a base saturation of over 60%, categorizing them under the subgroups of Inceptisols and Alfisols. The high molar ratios of Si-Al-Fe indicate a non-lateritic nature; however, the ternary diagram along with IOL/CIA suggests that these soils are kaolinitized with an advanced stage of laterization, reflecting significant base loss and accumulation of Fe-Al oxides. Correlation results revealed a strong significant relationship between Kornberg-A and PI (r=0.92**), as well as a high positive correlation between CIA and WI (r=0.86**). The findings indicate that two clusters are classified within the subgroups of Entisols, Inceptisols, and Alfisols, exhibiting a high degree of weathering influenced by topographic position, which directly affects weathering intensities.

Pulses are vital sources of protein, soil fertility enhancement, and sustainable cropping systems but their productivity is severely constrained by diseases such as Fusarium wilt, dry root rot, foliar diseases like cercospora leaf spot, anthracnose, ascochyta blight, powdery mildew, bacterial blight and some viral diseases. In recent years, organic and natural farming approaches have become popular as environmentally friendly alternatives to chemical-based disease control. The integration of eco-friendly practices such as biological control agents, botanical extracts and ecological cultural practices is emphasized in recent developments in plant disease management in pulses. Beneficial microorganisms such as Trichoderma, Pseudomonas, and Bacillus spp. have demonstrated significant potential in suppressing soil-borne pathogens through antagonism, competition, and induction of systemic resistance and botanical extracts such as neem and garlic, enhances plant immunity and suppresses pathogens naturally. Proper nutrition, water management, and the cultivation of disease-resistant varieties further strengthen crop resilience. Overall, this holistic approach promotes sustainable disease management, environmental protection, and long-term productivity in pulses.

The continuously rising population of the world and decreasing land for agriculture are highlighting the issue of sustainable agriculture and food security. The ancient methods of crop improvement require manual labour and time. The plant phenotyping techniques are providing a helping hand to plant breeders as they study plant phenotypes using imaging, aerial vehicles, sensors, robots, crop models etc. these tools accurately measure the visible traits of the plant such as plant height, physiology and response to stress in different conditions and thus speed up the crop breeding researches. The research is entering a new phase of development in which phenotyping is combined with machine learning, artificial intelligence, predictive crop models to support decision making in sustainable agriculture. The current review discusses the use of phenotyping tools in agriculture and the challenges that are still limiting the use of these novel techniques in agriculture and the possible solutions.

Precision agriculture, which relies on real-time data, is crucial for making farming more efficient and sustainable. This paper delves into an exciting area: how we can use real-time soil sensor data to make farming practices even better. We take a close look at the latest soil sensor technologies and how they can be integrated into precision agriculture systems.Soil sensors have come a long way, offering farmers detailed, up-to-the-minute information about their soil. In this manuscript we explore these technologies like electromagnetic induction, capacitance, and optical sensors, and discuss how they can be applied in precision agriculture.What makes this paper stand out is that we dive deep into how using soil sensors alongside precision agriculture can benefit farming sustainability. We discuss how these sensors help in maintaining soil health, managing water efficiently, optimizing nutrient use, and overall, using resources wisely. Moreover, we evaluate how this integration can lead to more sustainable farming practices. We look at its impact on soil health, water conservation, nutrient management, and taking care of the environment. By bringing together recent research and practical examples, this review provides valuable insights for researchers, policymakers, and farmers alike. It emphasizes the importance of using data to make better decisions in agriculture, ultimately improving productivity while caring for the environment. In summary, this review sheds light on the exciting possibilities of integrating real-time soil sensor data with precision agriculture, offering practical guidance for creating a more sustainable future in farming.