A two-year study was conducted at the Regional Research Station, Abohar, India, of Punjab Agricultural University to evaluate the effects of wheat cultivars and row spacing on weed dynamics, weed growth and wheat yield under different weed scenarios. Grass weed species tended to populate wheat cv. PBW 725, while broadleaf weeds were more in wheat cv. HD 3086 at one or the other observed stages. However, total weed biomass was similar in both the cultivars, which resulted in similar grain yield. Wide row spacing (20 cm) resulted in higher weed densities of most of the weed species, thereby had higher weed biomass and lesser wheat yield than narrow row spacing (15 cm). Broadleaf weeds accumulated higher weed biomass (67-92%) than grass weeds, however, grain yield reduction was more due to grass weeds (14-18%) as compared to broadleaf weeds (9-11%). Crop kept weed free for 45 days nullified the simple effect of cultivar and spacing on weed density and biomass. The study revealed that response of cultivar for weed suppression may be different for different weed species. Also, yield reduction in a crop may have more dependence on composition of weed flora in population, besides the weed biomass accumulated in the crop.

Abstract The soil biological properties and productivity of the rice-wheat system were examined after the long-term (19 years) application of chemical fertilizers (100% N, 100% NP, 100% NPK, and 150% NPK) alone and in combination with organic manures (100% NPK+Straw Incorporation (SI), 100% NPK+Green manure (GM), 100% NPK+Farmyard manure (FYM)). The integrated use of inorganic fertilizers and organic manure enhanced rice and wheat grain production by 5-20% and 12-15%, respectively, compared to balanced inorganic fertilization, and by 115% and 250% over the control (without fertilization). Long-term use of 100% NPK+FYM significantly improved the viable count of aerobic bacteria, fungi, actinobacteria, P-solubilizer, pseudomonads, free-living N-fixers, nitrate reducers, and ammonia oxidizers over the unfertilized control. Integrated usage of chemical fertilizer and organic manure increased the activity of soil enzymes such as dehydrogenase, alkaline phosphatase, urease, and protease. Significant relationships were found between the productivity of rice and wheat and soil biological factors such as soil microbial population (P-solubilizer, free-living N-fixer, pseudomonas, aerobic bacteria) and soil enzyme activities (dehydrogenase activity and microbial biomass carbon). The results showed that long-term application of chemical fertilizer and organic manure increased rice-wheat productivity via improving soil microbiological properties and biological functioning.

This study investigates the role of sodium nitroprusside (SNP) in enhancing drought tolerance and mitigating leaf spot disease in date palm (Phoenix dactylifera L.). Three SNP concentrations (0, 75, and 150 µM) were applied under progressive drought stress (1, 3, and 6 weeks). The results showed that drought stress significantly increased oxidative stress markers, including malondialdehyde (MDA) and hydrogen peroxide (H2O2), while SNP, particularly at 150 µM, reduced their levels. Additionally, SNP treatment enhanced antioxidant enzyme activities (catalase and peroxidase). SNP also reduced the severity of leaf spot symptoms caused by Alternaria alternata, limiting lesion size. These findings suggest that SNP modulates physiological and biochemical responses to drought stress, improving plant defense and disease resistance. Thus, SNP application could be a promising strategy to support the long-term viability of date palm cultivation, particularly in arid regions.

This research focuses on creating a real-time monitoring system for agricultural tractors that improves load management by continuously tracking essential parameters such as PTO RPM and fuel consumption. By accurately predicting engine load, the system allows operators to take timely actions to prevent overloading and enhance fuel efficiency. A detailed regression model was developed, yielding an R-squared value of 0.9368, indicating a strong correlation with engine load. The model also achieved a Root Mean Square Error (RMSE) of 448.26 and a Coefficient of Variation (CV) of 16.98%, showcasing its reliability and accuracy. These results demonstrate the system’s effectiveness in real-world agricultural applications, offering a cost-efficient approach to optimize tractor performance and promote sustainable farming practices. This advancement not only improves operational efficiency but also extends the lifespan of agricultural equipment, addressing the need for enhanced load management in contemporary farming.

Black gram is a highly nutritious legume crop primarily cultivated for its grains in Asian countries. Despite its significant importance due to its health benefits and organoleptic properties, its productivity remains low. The exploration and cataloging of genetic diversity are crucial initial steps towards maximizing production potential. The objective of this study was to evaluate 482 germplasm accessions of black gram for 14 quantitative traits (including grain yield and related characteristics) and 13 qualitative traits (such as plant growth, leaf structure, branching pattern, and pod morphology). Analysis of the phenotypic data revealed extensive variation in both quantitative and qualitative traits among the germplasm under study. Several germplasm accessions, including IC0619217, IC0279521, IC0472035, IC0393537, IC0321149, IC0331231, IC0530627, and IC0330889, exhibited numerous desirable traits that surpassed those of commercial checks. Heritability and genetic advance as a percentage of population means (GAM) were highest for PDM (60.795), GyPp (58.82), and PpP (56.89). Principal component analysis and correlation analysis indicated strong associations of traits such as PdL, PSD, PdpP, PpP with GyPp, while, days to maturity showed a negative association with grain yield. Furthermore, germplasm with traits such as insect tolerance (pod and leaf pubescence), drought tolerance (lanceolate leaves), and heat tolerance (central branching) were present in germplasm. This selected germplasm holds promise for use in urdbean genetic improvement programs.

This study examines the impact of climate factors on vegetable prices, a key aspect influencing agricultural production decisions. As climate change increasingly disrupts weather patterns, understanding the relationship between climate variables and vegetable price fluctuations becomes essential for shaping agricultural policies, market predictions, and supply chain management. Using the ARIMAX model, this research analyzes the effects of temperature and rainfall on the prices of four major vegetables viz. tomato, onion, brinjal, and okra in Tamil Nadu from 2005 to 2019. The findings reveal that tomato and onion prices are significantly affected by climate conditions, while brinjal and okra prices show minimal sensitivity to weather variations. Additionally, the study employs the ARIMA model to forecast vegetable prices for 2020-2029, identifying optimal forecasting models for each vegetable based on the lowest AIC and BIC values. The forecast indicates substantial price fluctuations over the next decade, providing valuable insights for farmers to optimize crop planning and marketing strategies.

This study investigates the impact of the Minimum Support Price (MSP) system on farm harvest prices (FHPs) for paddy and wheat in India. As a government intervention aimed at stabilizing agricultural markets and safeguarding farmers against price volatility, the MSP establishes a price floor for designated crops prior to planting, thereby guaranteeing a minimum price for farmers’ produce. Our research assesses the effectiveness of the MSP in influencing FHPs for paddy and wheat across key producing states from 2002 to 2021. Utilizing a comprehensive dataset, we employ a panel model to analyze the relationship between MSP-related procurement volumes and FHPs, while controlling for variables such as production costs, market prices, and overall production levels. The results indicate that government procurement under the MSP significantly enhances FHPs for both crops, highlighting the crucial role of such interventions in improving farmers’ incomes. In light of these findings, we recommend expanding MSP-based procurement to encompass a wider array of crops and regions.

The present study aimed to assess the training needs of farmers regarding Integrated Farming Systems (IFS) in the North Western Zone of Tamil Nadu. Utilizing an ex-post facto research design and a multistage sampling technique, the research involved the random selection of 16 villages from two chosen blocks across each of the four districts in the study area. A sample size of 160 respondents was achieved by selecting 10 farmers from each village. Data collection was facilitated through a well-structured interview schedule and a training need index specifically designed for this purpose. The findings indicate that the majority of respondents identified several key areas in which training is necessary: vermicomposting (51.25%), general aspects of IFS (48.75%), agriculture (43.75%), and animal husbandry (38.75%). Furthermore, the analysis revealed that a significant proportion of farmers (56.25%) exhibited a high level of training need, as evidenced by a mean score index of 73.07. In conclusion, the study highlights an urgent demand among farmers for information, skill acquisition, and knowledge enhancement related to IFS, with the aim of improving both productivity and profitability.