Agricultural Practices Research Articles

Regenerative agriculture: A paradigm shift for food production in South Africa

Agriculture and other land use practices have severely affected the quality of soils and food production systems in South Africa. In addition, these practices significantly contribute to greenhouse gas (GHG) emissions. Soil quality is generally associated with soil organic matter (SOM) along with elemental Carbon (C) and Nitrogen (N). Approximately 58% of South African soils contain <0.5% organic C, 38% contain 0.5–2% organic C, and 4% contain >2% organic C. The extensive aridity and lack of SOM content in South African soils cause them to lack resilience and have a delicate nature compared to soils in temperate regions. In a country where almost 60% of the land is degraded, and 91% is prone to desertification, farming practices require a revised framework for land management. The challenge of producing food on degraded land that is often water-scarce and particularly vulnerable to the effects of climate change is a threat to South Africa's food and nutritional security. Therefore, this review aims to equip farmers with knowledge and principles of innovative and sustainable alternatives such as regenerative agriculture that naturally and progressively improves soil quality/fertility and ecosystem health; and reduces GHG emissions through carbon sequestration. Quantitative studies in South Africa show that during 5–90 years of crop cultivation in the Free State province resulted in a 10–73% reduction in C and N relative to natural grasslands. Similar studies reported a 50% reduction in C after 50 years of cultivation on Hutton soil in Pretoria; and a 50% reduction in C after only 3.5 years of cultivation in the Free State. Considering that the world's food systems are responsible for more than a third of anthropogenic GHG emissions, we are convinced that the shifting trend toward regenerative agriculture is a sustainable climate-smart solution for global food production systems.

Malian Farmers’ Perception of Sustainable Agriculture: A Case of Southern Mali Farmers

This study was conducted in the Klela district, Sikasso region of Mali, and aimed to evaluate farmers’ perceptions regarding sustainable agriculture while identifying key factors that influenced these perspectives. Using a face-to-face survey with 110 randomly selected farmers, a comprehensive 19-item scale was employed to measure the perception levels of sustainable agricultural practices, scored on a 5-point Likert scale. The analysis highlighted a spectrum of perception levels among participants: 12.7% exhibited the lowest perception, 38.2% had a low perception, 31.8% had a medium perception, and only 17.3% had a high perception. Notably, a majority (50.96%) held perceptions below the average level. Through multiple regression analysis, several factors were identified as influential in shaping these perceptions. Family involvement in farming and weekly working days were negatively associated, whereas daily working hours and household size demonstrated a positive correlation. Additionally, the sources of information regarding sustainable agriculture significantly impacted farmers’ perception levels, as indicated by the chi-square test results. The research underscores the necessity for targeted extension programs designed to augment farmers’ understanding of sustainable agriculture, aiming to translate these perceptions into attitudes and practical actions effectively. This study contributes valuable insights, emphasizing the significance of tailored interventions geared toward enhancing sustainable agricultural practices among farmers in Mali, with the potential to positively influence their agricultural behaviors.

Energy efficiency and economic analysis of mechanized vs conventional cotton production system: A comparative study under rainfed vertisols of Tamil Nadu

Energy efficiency plays a pivotal role in optimizing input use and improving the sustainability of agricultural practices, especially in energy-intensive crops like cotton. Due to its high economic importance, cotton cultivation has been in the need of the hour in developing mechanized cultivation practices aimed at improving production efficiency. This study evaluated energy consumption and performed an economic analysis of mechanized versus conventional cotton production under rainfed vertisols in Tamil Nadu. Among the various inputs, fertilizer application consumed the most energy in both systems, followed by labor energy differences between the 2 methods. Mechanized cultivation drastically reduced labor energy requirements (174.2 MJ/ha) compared to manual methods (2215.6 MJ/ha), highlighting the potential for labor savings. Mechanized cultivation also showed more efficient use of non-renewable energy sources, whereas conventional methods relied more on renewable energy inputs, highlighting the trade-offs between the 2 systems. Mechanized cultivation significantly reduced production costs (Rs. 74290/ha) compared to conventional methods (Rs. 140440/ha), largely due to a 47.8 % reduction in labor costs, demonstrating its economic viability. This study suggests that improving the energy efficiency of mechanized cotton production should prioritize efficient fertilizer use and reducing diesel fuel consumption through enhanced machinery performance.

Postmarital residence rules and transmission pathways in cultural hitchhiking

Cultural evolutionary processes can often lead to a statistical association between neutral and adaptive traits during episodes of population dispersal and the introduction of a beneficial technology in a geographic region. Here, we examine such cultural hitchhiking processes using an individual-based model that portrays the cultural interaction between a migrant and an incumbent population. Our model is loosely based on the interaction between farming and foraging populations during the initial stages of the adoption and diffusion of agricultural practices. The two populations are characterized by different variants for their neutral and adaptive cultural traits, with the latter set providing a reproductive advantage for the migrant communities over the incumbent ones. We explore how the neutral traits of the migrant population spread and how this process is conditioned by the following factors: 1) the possibility of transmission of the adaptive traits; 2) the extent of the increased reproductive advantage provided by the adaptive variants of the migrant population; 3) postmarital residence rules; and 4) how and when neutral traits are transmitted. Our results reveal a diverse range of outputs, highlighting the relevance of factors such as the nature of postmarital resocialization and the specific combination of postmarital residence rules and sex-biased transmission.

The role of agricultural cooperatives in mitigating opportunism in the context of complying with sustainability requirements: empirical evidence from Spain

AbstractThe convergence of emerging sustainability regulations and recommendations outlined in the European, national and regional agricultural policies, coupled with the growing demand from retailers for food produced through more sustainable agriculture practices, presents a substantial challenge for farmers. This challenge is further exacerbated by their limited access to essential information, knowledge, and resources necessary for compliance, which are often acquired through interactions with various stakeholders within the agri-food supply chain. Moreover, the inherent power asymmetry between small-scale farmers and their considerably larger counterparts, including input suppliers and agricultural product buyers, exposes farmers to opportunistic behaviours. In response to these challenges, agri-food cooperatives have been proposed as an organizational solution to mitigate opportunistic behaviour. However, empirical data-supported evidence of this proposition remains scarce. Drawing upon data obtained from Spanish farmers, our study investigates the impact of agri-food cooperatives on the incidence of opportunistic practices experienced by farmers during their interactions with suppliers and buyers. Through a propensity score matching analysis, our findings reveal that cooperative membership exerts a statistically significant negative influence on both supplier and buyer opportunism in the context of complying with sustainability requirements. These findings provide compelling empirical evidence of the pivotal role played by agri-food cooperatives in addressing opportunism within the supply chain. Importantly, they underscore the vital importance of cooperatives in mitigating the challenges associated with enhancing sustainability in agriculture.

Small Farmers’ Agricultural Practices and Adaptation Strategies to Perceived Soil Changes in the Lagoon of Venice, Italy

Farmers have a pivotal responsibility in soil conservation: they can either preserve or deplete it through their choices. The responsibility of agriculture increases when practised in delicate ecosystems, such as lagoonal ones. The Venetian Lagoon islands, which are increasingly subjected to natural and anthropic subsidence, occasional flooding events (acqua alta), and eustatic sea level rise, are constantly exposed to erosive processes that challenge farmers to play with their adaptive capability. This research was carried out on the islands of Sant’Erasmo and Vignole, the most representative of island agriculture in the Venetian Lagoon: they almost exclusively rely on agriculture, which is non-existent in the other islands. This empirical research aimed to explore farmers’ agricultural practices, perceptions of soil changes, and how they adapt to them. It was fundamental for this study that the field research involved direct human contact with farmers (through semi-structured interviews) for data collection and using qualitative methods for data analysis, integrating scientific and non-scientific forms of knowledge and actors. The final purpose was to demonstrate the sustainability (valued on the potential depletion or regeneration capability) of agricultural practices and adaptation strategies on a theoretical basis. Despite their polycultural landscape (maintained by low-input farming systems), escaped from the predominant landscape oversimplification, Sant’Erasmo and Vignole are also subjected to unsustainable agricultural practices, including heavy mechanisation and synthetic inputs. Coupled with natural soil salinity that is exacerbated by increasing drought periods, these practices can contribute to soil degradation and increased salinity. The reported adaptation strategies, such as zeroed, reduced, or more conscious use of machines, were guided by the need to reduce the negative impact of soil changes on productivity. Our research revealed some of them as sustainable and others as unsustainable (such as increasing irrigation to contrast soil salinity). Participatory action research is needed to support farmers in designing effective sustainable agricultural practices and adaptation strategies.

Vegetation disturbance and regrowth dynamics in shifting cultivation landscapes.

Shifting cultivation, an age-old agricultural practice, is a major factor in forest cover change across Southeast Asia, where repeated cycles of vegetation disturbance and regrowth lead to far-reaching environmental and socio-economic impacts. The present study aims to assess the spatio-temporal patterns of vegetation disturbance and regrowth caused by shifting cultivation in Tripura state of India, over the past three decades, utilizing temporal segmentation of time-series Landsat data. The study analyzed vegetation disturbance and regrowth patterns in a shifting cultivation landscape from 1991 to 2020 using normalized burn ratio trends through LandTrendr, validated by the TimeSync tool. Six land use and land cover classes, viz., forests and trees outside forests, rubber plantation, shifting cultivation, water bodies, agriculture, and settlements, were mapped with an accuracy of 83.08% using a random forest classifier. This classification enabled the identification of the effective study area, which included forest areas with shifting cultivation patches. The analysis revealed that 2533.96 km2 of the study area remained undisturbed, 568.43 km2 experienced low-magnitude disturbance, 1501.11 km2 were moderately disturbed, and 184.82 km2 were highly disturbed. The shifting cultivation cycles in the study area show considerable variation. Low-magnitude disturbance indicates a single slash-and-burn event in the past three decades, moderate-magnitude disturbance involves fallow periods of over 10 years, and high-magnitude disturbance occurs with fallow periods of less than 8 years. The study revealed a shift from traditional shifting cultivation to more permanent agricultural practices in parts of Tripura. In response to incentives for commercial crops, cultivators are increasingly adopting long-term cultivation, including the growing of pineapple, areca nut, and rubber, as well as intercropping with papaya, banana, lemon, tapioca, pepper, and ginger. These plots are, however, abandoned if the yields become economically unviable. The expansion of monoculture cultivation significantly reduces the available area for shifting cultivation, thereby compelling jhum cultivators to revisit their jhum patches more frequently. This study is a first-of-its-kind attempt in the Indian context and sheds light on changing patterns in an age-old agricultural practice.

Uncertainty-based fuzzified environmental-socio-economic risk assessment of precision agricultural practices

Uncertainty-based fuzzified environmental-socio-economic risk assessment of precision agricultural practices

Soil Physico-chemical Responses to Integrated Nutrient Management in Lentil Intercropped with Bhimal (Grewia optiva) in Agroforestry vs. Open Systems

The growing concern over health risks associated with the heavy use of chemical fertilizers and pesticides in India, which have been linked to rising cancer cases, underscores the need for sustainable agricultural practices. Integrated Nutrient Management offers a promising solution to enhance soil fertility and support crop health, especially for lentil cultivation within agroforestry systems. This study, conducted at Dr. Y. S. Parmar University of Horticulture and Forestry in Nauni, Solan, Himachal Pradesh, examined the effects of INM on the soil’s physical and chemical properties when lentils were intercropped within a Grewia optiva (Bhimal) agroforestry system in the mid-hills region of Himachal Pradesh. The experiment evaluated various combinations of organic and inorganic fertilizers, including T1: Recommended Dose of Fertilizer, T2: Farmyard Manure, T3: Vermicompost, T4: Goat Manure, T5: 50% Recommended dose of fertilizer + 50% Farm yard manure, T6: 50% Recommended dose of fertilizer + 50% Vermicompost, T7: 50% Recommended dose of fertilizer + 50% Goat Manure, and T8: Control. These treatments were applied in plots both under the Bhimal-based agroforestry system (S1) and in open field conditions (S2), with three replications per treatment following a factorial randomized block design. Key physical parameters such as bulk density, particle density, porosity, and soil moisture were measured alongside chemical properties, including soil pH, electrical conductivity, organic carbon content, and the availability of nitrogen, phosphorus, and potassium. Data collected over two years was analyzed using R Statistical Software, revealing that INM significantly improved soil properties in the agroforestry system compared to the control. Notably, 100% FYM emerged as the most effective treatment, enhancing soil porosity, moisture, pH, and nutrient availability (N, P, and K). Improvements in soil structure and nutrient levels were particularly evident in the second year, positively impacting lentil growth and yield. The study highlights the potential of INM to promote sustainable, chemical-free agriculture in hilly areas, offering valuable recommendations for farmers and policymakers focused on sustainable agriculture. Specifically, 100% FYM is recommended for improving soil health and crop productivity in the mid-hill region of Himachal Pradesh, providing a model for similar agro-ecological zones aiming to reduce chemical inputs and support human health.

Plant Growth Promoting Microorganisms and Emerging Biotechnological Approaches for Sugarcane Disease Management

Sugarcane (Saccharum officinarum L.) is a highly valuable agricultural crop, cultivated globally in tropical and subtropical regions, primarily for its sugar content. Out of the 110 countries that grow sugarcane, India and Brazil together account for half of the world’s total production. Being an annual crop, it is prone to many diseases. The major diseases that can affect the sugarcane are red rot, wilt, sett rot, grassy shoot and pokkah boeng. Among the diseases Colletotrichum falcatum, Fusarium sacchari, Ceratocystis paradoxa, Candidatus Phytoplasma and Fusarium fujikuroi pathogen leads to more yield loss. Traditional disease management strategies, such as chemical treatments, conventional methods and biological control, offer limited protection throughout the crop cycle and raise concerns regarding environmental impact and sustainability. In recent years, plant growth-promoting microorganisms (PGPMs) have emerged as a promising alternative, offering environmentally friendly solutions to enhance plant health and manage diseases. RNAi has been explored to manage various diseases caused by viruses, fungi, and bacteria. By silencing key pathogenicity genes in the causal organisms, sugarcane mosaic virus (SCMV), smut, and leaf scald. The integration of PGPMs with RNAi emerging biotechnological tools and transcriptomics presents a sustainable approach to disease control, potentially reducing the dependency on chemical pesticides and promoting eco-friendly agricultural practices.

U.S. climate-smart agricultural and forestry incentives: from offsets to clean energy credits

Abstract Climate-smart agricultural and forestry (CSAF) practices prevent greenhouse gas emissions from occurring or increase carbon sequestration. I examine how the development of U.S. CSAF incentives shifted from offset markets to biofuel regulations over time. My perspective is informed by two professional roles I had in developing these standards. First, as an economist at the Chicago Climate Exchange, I developed CSAF offset protocols between 2007 and 2010. Second, between 2022 and 2024, I was an economist at the U.S. Department of Agriculture (USDA) when USDA developed a climate-smart agriculture pilot protocol for the sustainable aviation fuel blender’s tax credit. Twenty years ago, there were no U.S. biofuel mandates and CSAF incentives were conceptualized as occurring through offset markets. I explain how, in contrast to cap-and-trade programs, greenhouse gas emissions from agricultural and forestry practices are already included in the baseline of biofuel regulations. This implies that additionality issues, which have impeded the development of CSAF offset standards, are not as applicable to biofuel programs. Specifically, I examine the development of agricultural soil carbon, livestock digester, and forest carbon protocols as case studies. I conclude by discussing how CSAF crediting may evolve in the future.

Comparative Analysis of Machine Learning Models for Predicting Rice Yield: Insights from Agricultural Inputs and Practices in Rwanda

Food security is a global challenge, especially in developing countries like Rwanda. With a growing population and limited agricultural land, Rwanda struggles to meet increasing food demands. Rice is a staple food crop in Rwanda, playing a crucial role in the country’s food security. However, factors like climate variability, soil nutrient management, and limited access to high-quality inputs hinder rice yield optimization. This paper investigates the most effective machine learning model for predicting rice crop yield in Rwanda, using agricultural inputs and practices. The study used secondary datasets from the National Institute of Statistics of Rwanda (NISR) for rice yield prediction. Eight supervised machine learning algorithms were used, including Linear Regression, Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, eXtreme Gradient Boosting Tree, and AdaBoost. The models were evaluated based on their accuracy in predicting rice yields, with RMSE, MAE, and Relative Error as primary metrics. The feature importance analysis was also conducted to identify significant factors influencing yield predictions. The study’s findings revealed that the Adaptive Boosting Tree model outperformed the other machine learning models in predicting rice yield. This model achieved RMSE, MAE and Relative Error of 0.69, 0.46 and 12.4%, respectively, indicating a high level of predictive accuracy. The feature importance analysis further highlighted the key factors that contributed to rice yield predictions, with the quantity of inorganic fertilizer, degree of erosion, season, and seed type emerging as the most influential variables. The study demonstrates the effectiveness of machine learning models, particularly the Adaptive Boosting Tree, in improving rice yield predictions and highlighting the crucial role of agricultural inputs like fertilizer and seed type, in influencing crop yields. The output from this study will help the farmers and stakeholders to make data-driven decisions about resource use and crop management.

Emerging Nanochitosan for Sustainable Agriculture

Chemical-intensive agriculture challenges environmental sustainability and biodiversity and must be changed. Minimizing the use of agrochemicals based on renewable resources can reduce or eliminate ecosystems and biodiversity threats. Nanochitosan as a sustainable alternative offers promising solutions for sustainable agricultural practices that work at multiple spatial and temporal scales throughout the plant growth cycle. This review focuses on the potential of nanochitosan in sustainable agricultural production and provides insights into the mechanisms of action and application options of nanochitosan throughout the plant growth cycle. We emphasize the role of nanochitosan in increasing crop yields, mitigating plant diseases, and reducing agrochemical accumulation. The paper discusses the sources of nanochitosan and its plant growth promotion, antimicrobial properties, and delivery capacity. Furthermore, we outline the challenges and prospects of research trends of nanochitosan in sustainable agricultural production practices and highlight the potential of nanochitosan as a sustainable alternative to traditional agrochemicals.

The Impact of Farmers' Social Lifestyles on Agricultural Sustainability in Rural Area of Turkey

This study investigates how farmers' social lifestyles influence agricultural sustainability in rural Turkey. Through data analysis from 312 producers across five provinces, we examine how participation in social activities such as religious gatherings, café meetings, and agricultural fairs shapes farming decisions and sustainability outcomes. Using logistic regression modeling, we find that social network participation significantly impacts agricultural performance and sustainability practices. Results reveal that different types of social engagement lead to distinct patterns of innovation adoption and resource management. Coffee-house and religious gatherings influence not only sales decisions but also agronomic practices, such as adopting new technologies and innovative methods. The lack of female involvement and potential pathways for their integration are also important. This research provides crucial insights for policymakers seeking to leverage existing social networks to promote sustainable agricultural practices in rural communities.

Early detection of verticillium wilt in eggplant leaves by fusing five image channels: a deep learning approach.

As one of the world's most important vegetable crops, eggplant production is often severely affected by verticillium wilt, leading to significant declines in yield and quality. Traditional multispectral disease-imaging equipment is expensive and complicated to operate. Low-cost multispectral devices cannot capture images and cover less information. The traditional approach to early disease diagnosis involves using multispectral disease-imaging equipment in conjunction with machine learning technology. However, this approach has significant limitations in early disease diagnosis, including challenges such as high costs, complex operation, and low model performance. The aim of this study was to combine low-cost multispectral cameras with deep learning technology to detect early stage Verticillium wilt in eggplant effectively. Using the Manual FS-3200T-10GE-NNC multispectral camera to perform multispectral imaging of the leaves of eggplant seedlings at the early infection stage, information fusion was performed on the collected multispectral images, and a five-channel image information fusion model was established. Image information fusion technology was combined with deep learning technology, among which the VGG16-triplet attention model performed the best, achieving a precision of 86.73% on the test set. Model validation on 48- and 72-hour data reached a precision of 75% and 82%, respectively, achieving an early diagnosis of Verticillium wilt. This highlighted the potential of multispectral cameras for early disease detection. In this study, we successfully developed a method for the non-destructive detection of the early stages of eggplant wilt disease by combining multispectral imaging technology with deep learning algorithms. While ensuring high accuracy, this method significantly reduces the cost of experimental equipment. The application of this method can reduce the cost of agricultural equipment and provide a scientific basis for agricultural production practices, helping to reduce losses caused by diseases.

Understanding the Impact of Soil Characteristics and Field Management Strategies on the Degradation of a Sprayable, Biodegradable Polymeric Mulch

The use of non-degradable plastic mulch has become an essential agricultural practice for increasing crop yields, but continued use has led to contamination problems and in some cropping areas decreases in agricultural productivity. The subsequent emergence of biodegradable plastic mulches is a technological solution to these issues, so it is important to understand how different soil characteristics and field management strategies will affect the rate at which these new materials degrade in nature. In this work, a series of lab-scale hydrolytic degradation experiments were conducted to determine how different soil characteristics (type, pH, microbial community composition, and particle size) affected the degradation rate of a sprayable polyester–urethane–urea (PEUU) developed as a biodegradable mulch. The laboratory experiments were coupled with long-term, outdoor, soil degradation studies, carried out in Clayton, Victoria, to build a picture of important factors that can control the rate of PEUU degradation. It was found that temperature and acidity were the most important factors, with increasing temperature and decreasing pH leading to faster degradation. Other important factors affecting the rate of degradation were the composition of the soil microbial community, the mass loading of PEUU on soil, and the degree to which the PEUU was in contact with the soil.

Harvest Harmony: Integrating Linear and Nonlinear Machine Learning Models for Precision Crop Recommendation

This research presents a comprehensive crop recommendation system that combines linear and nonlinear machine learning models to forecast suitable crops based on soil and environmental factors. The study evaluates the performance of Logistic Regression (linear) alongside Random Forest, Gradient Boosting, and XGBoost (nonlinear models) in delivering accurate crop suggestions. Precision, recall, and F1-score metrics are employed to assess the models' effectiveness. The dataset incorporates vital soil characteristics, such as nitrogen, phosphorus, and potassium levels, and environmental parameters like temperature, humidity, pH, and rainfall. The results demonstrate that while linear models like Logistic Regression offer simplicity, nonlinear models such as XGBoost excel in capturing complex data patterns, providing more precise recommendations. This dual-model strategy empowers farmers to make informed decisions tailored to specific farming conditions, boosting agricultural productivity. The study underscores the importance of integrating both linear and nonlinear machine learning techniques to achieve optimal crop recommendations, encouraging resource efficiency and sustainable agricultural practices.

Circular bioeconomy accounting tool (CBEAT): a holistic framework for agriculture and agri-food system circularity practice

Circular bioeconomy accounting tool (CBEAT): a holistic framework for agriculture and agri-food system circularity practice

Performance evaluation of agrivoltaic system for the synergy among greengram (Vigna radiata L. Wilczek) production and solar electric power generation

AbstractThe concept of agrivoltaic, combining agriculture and solar photovoltaic system, is ideal for populous countries like India as it provides access to eco‐friendly power and crop production from the same land. The main objective of the study was to evaluate the performance of agrivoltaic systems for different geometry of solar photovoltaic strings and their suitability for agricultural practices for the greengram (Vigna radiata L. Wilczek) crop under North‐Gujarat agro‐climatic conditions of India. Eight equal‐capacity strings with different geometry were designed to evaluate power generation and crop production beneath the strings. The experiment involved eight strings taken as eight treatments and the traditional system of greengram (V. radiata L. Wilczek) cultivation practices is taken as the ninth treatment. The greengram (V. radiata L. Wilczek) was shown and all other cultural practices was kept same upto grain yields. The results revealed that 10.5 feet height string with continuous solar panel pattern) provided the highest gross income (Rs. 24,364.00) from power generation and greengram (V. radiata L. Wilczek) yield. In terms of net realization, a 6.0‐feet string with continuous solar panel pattern provided the highest net return of Rs. 12,417.00, as the capital cost was less for the system. Treatment‐5, which involved transparent panels, was found to be better for the photosynthesis process of the greengram (V. radiata L. Wilczek) crop, as it provided the highest yield (12.99 kg) under the agrivoltaic system.

Effect of organic farming on root microbiota, seed production and pathogen resistance in winter wheat fields

Societal Impact StatementAgricultural intensification is a major driver of biodiversity decline in agrosystems. For instance, it has been shown that conventional farming leads to a decline in soil microbial diversity and triggers a strong selection process, altering the functioning of the whole ecosystem. The present study shows that organic farming increases diversity and affects composition of crop plant microbiota, mostly as a response to field management and soil characteristics. Furthermore, crop plant microbiota influences crop production and resistance to pathogens. Therefore, agricultural practices affect plant performance through microorganism‐mediated changes, which may be important pillars of future sustainable crop production.Summary Agricultural intensification threatens biodiversity, but the effects of intensification on microorganisms are still overlooked despite their role in ecosystem functioning. Microorganisms associated with plants provide many services that affect plant growth and health. Organic farming is expected to strongly affect species composition, richness, and their interactions. We analyzed the effect of the farming system on endophytic microbial assemblages associated with winter wheat plants and plant performance in the field. We collected environmental data through farmer interviews, soil analyses, and plant inventories and analyzed root microbiota at vegetative and flowering stages. Organic farming increased fungal and bacterial diversity associated to wheat plants and affected species composition in most phyla. This effect was mostly due to soil characteristics and field management and a little to plant diversity in the field. Microbial responses were more pronounced at the late developmental stage, likely as a result of accumulative effect of management actions during plant development. Seed production and resistance to pathogens were related to specific phyla that are important for seed production and/or wheat resistance to septoriose. This work advances our understanding of how agricultural practices affect plant performance through microorganism‐mediated changes and supports the use of microorganisms as pillars of sustainable crop production.