Statistical programs are essential tools for those who deal with scientific research and need to analyze experimental data. In agriculture, there are often uncontrolled factors, which determine the necessity of statistical analyses of the data. The Assistat software version 7.7 is one of these tools and this study aimed to demonstrate its functionality and efficiency in the analyses of experimental data of agricultural research and evaluate its acceptance. In order to exemplify its utilization, data of agricultural experiments were analyzed using the models of analysis of variance for randomized block and factorial experiments. In addition, the regression was used in the analysis of variance for quantitative treatments. It was concluded that the software was used in many papers published in journals and that it is functional and efficient in the analysis of experimental data of agricultural research. Key words: Analysis of variance (ANOVA), statistical software, Tukey's test.
The worldwide increases in both environmental damage and human population pressure have the unfortunate consequence that global food production may soon become insufficient to feed all of the world's people. It is therefore essential that agricultural productivity be significantly increased within the next few decades. To this end, agricultural practice is moving toward a more sustainable and environmentally friendly approach. This includes both the increasing use of transgenic plants and plant growth-promoting bacteria as a part of mainstream agricultural practice. Here, a number of the mechanisms utilized by plant growth-promoting bacteria are discussed and considered. It is envisioned that in the not too distant future, plant growth-promoting bacteria (PGPB) will begin to replace the use of chemicals in agriculture, horticulture, silviculture, and environmental cleanup strategies. While there may not be one simple strategy that can effectively promote the growth of all plants under all conditions, some of the strategies that are discussed already show great promise.
IntroductionEnvironmentally friendly pork production is crucial to the pig industry, where the enhancement of growth performance and feed efficiency with reduced environmental impacts is favored. This study aimed to evaluate the effect that protease supplementation in a low crude protein diet has on the growth performance, nutrient digestibility, nitrogen retention, and gut microbiome in growing pigs.MethodsEighty pigs (Landrace × Yorkshire × Duroc; 24.72 kg) were selected, and based on initial body weight and sex, randomly allocated to one of the following dietary treatments: H, 16% crude protein (CP) diet; L, 14% CP diet; L+E1, low CP diet + 0.1% protease; and L+E2, low CP diet + 0.2% protease. Each treatment comprised four replicates with five pigs per pen. ResultsPigs fed a low CP diet with protease supplementation showed a significantly higher body weight, average daily gain, and feed conversion ratio than those fed a high CP diet. In addition, ammonia emissions were lower in the L+E2 group than in the L group. Based on microbiome analysis, the L+E1 and L+E2 groups showed an increased Firmicutes-to-Bacteroidota ratio and elevated expression of pathways related to carbohydrate metabolism, coinciding with higher concentrations of short-chain fatty acids, such as butyrate and propionate, which support intestinal health. Additionally, the predicted function of the microbiota of pigs fed protease exhibited reduced nitrogen and sulfur metabolism, suggesting a potential reduction in excreted odorous compounds. DiscussionThese findings highlight the role of protease in enhancing growth performance and feed efficiency by modulating gut microbial composition and metabolic functions and reducing noxious gas emissions. Also, potential feed-cost savings are inferred from lower CP formulation.
The Lidia cattle breed is featured in several traditional popular bullfighting festivals throughout Spain, including the “Toro de Cuerda” event, in which the animals are subjected to intense physical exercise. However, the physiological impact and welfare implications of these activities remain poorly characterized. This study aimed to evaluate the stress response and muscle damage in Lidia breed bulls during roping bull celebrations through comprehensive blood analysis. Blood samples were collected from 53 adult male Lidia bulls before and after a standardized 45 min continuous running exercise during traditional roping bull events in four Spanish autonomous regions. Hematological parameters, muscle enzymes (creatine kinase, lactate dehydrogenase, lactate), and stress hormones (cortisol and ACTH) were analyzed. Significant increases (<i>p</i> < 0.05) were observed in leukocytes, lymphocytes, monocytes, eosinophils, neutrophils, erythrocytes, hematocrit, hemoglobin, and post-exercise platelets. Muscle enzymes showed marked elevations, with creatine kinase increasing up to 10-fold above baseline values. Stress hormones, cortisol and ACTH, also demonstrated significant increases. Despite the magnitude of these changes, all parameters remained within established reference ranges for the bovine species. This study provides the first physiological assessment of Lidia cattle during popular bullfighting celebrations, establishing baseline data for evidence-based welfare evaluation and management protocols.
Self Supervised Learning(SSL) has emerged as a prominent paradigm for label-efficient learning, and has been widely utilized by remote sensing foundation models(RSFMs). Recent RSFMs including SatMAE, DoFA, primarily rely on masked autoencoding(MAE), contrastive learning or some combination of them. However, these pretext tasks often overlook the unique temporal characteristics of agricultural landscape, namely nature's cycle. Motivated by this gap, we propose three novel agriculture-specific pretext tasks, namely Time-Difference Prediction(TD), Temporal Frequency Prediction(FP), and Future-Frame Prediction(FF). Comprehensive evaluation on SICKLE dataset shows FF achieves 69.6% IoU on crop mapping and FP reduces yield prediction error to 30.7% MAPE, outperforming all baselines, and TD remains competitive on most tasks. Further, we also scale FF to the national scale of India, achieving 54.2% IoU outperforming all baselines on field boundary delineation on FTW India dataset.
Agricultural residues represent a vast, underutilized resource for renewable energy. This study combines empirical analysis from 179 countries with a case study of a pelletization facility to evaluate the global potential of agricultural pelletization for fossil fuel replacement. The findings estimate a technical availability of 1.44 billion tons of crop residues suitable for pellet production, translating to a 4.5% potential displacement of global fossil fuel energy use, equating to 22 million TJ and equivalent to 917 million tons of coal annually. The economically optimized scenario projects annual savings of $163 billion and a reduction of 1.35 billion tons of CO2 equivalent in emissions. Utilizing the custom-developed CLASP-P and RECOP models, the study further demonstrates that agricultural pellets can achieve competitive pricing against conventional fossil fuels in many markets. Despite logistical and policy challenges, agricultural pelletization emerges as a scalable, market-driven pathway to support global decarbonization goals while fostering rural economic development. These results reinforce the need for targeted investment, technological advancement, and supportive policy to unlock the full potential of agricultural pellets in the renewable energy mix.
Abhay Vijayvargia, Ajay Nagpal, Kundeshwar Pundalik
et al.
Indian farmers often lack timely, accessible, and language-friendly agricultural advice, especially in rural areas with low literacy. To address this gap in accessibility, this paper presents a novel AI-powered agricultural chatbot, Krishi Sathi, designed to support Indian farmers by providing personalized, easy-to-understand answers to their queries through both text and speech. The system's intelligence stems from an IFT model, subsequently refined through fine-tuning on Indian agricultural knowledge across three curated datasets. Unlike traditional chatbots that respond to one-off questions, Krishi Sathi follows a structured, multi-turn conversation flow to gradually collect the necessary details from the farmer, ensuring the query is fully understood before generating a response. Once the intent and context are extracted, the system performs Retrieval-Augmented Generation (RAG) by first fetching information from a curated agricultural database and then generating a tailored response using the IFT model. The chatbot supports both English and Hindi languages, with speech input and output features (via ASR and TTS) to make it accessible for users with low literacy or limited digital skills. This work demonstrates how combining intent-driven dialogue flows, instruction-tuned models, and retrieval-based generation can improve the quality and accessibility of digital agricultural support in India. This approach yielded strong results, with the system achieving a query response accuracy of 97.53%, 91.35% contextual relevance and personalization, and a query completion rate of 97.53%. The average response time remained under 6 seconds, ensuring timely support for users across both English and Hindi interactions.