AgEvidence: a dataset to explore agro-ecological effects of conservation agriculture.

Conservation agriculture (CA) is a set of principles thought to be able to enhance crop productivity while minimizing impacts on the environment. The evidence base for CA can be challenging to synthesize because it encompasses many different practices and social and agroecological outcomes. To facilitate synthesis of CA evidence we have created a dataset organizing 218 response variables from five common categories of CA: cover crops, tillage management, pest management, nutrient management, and crop diversification. These data cover the Midwestern United States (U.S.) from 1980-2020. The dataset is also summarized and visualized on the AgEvidence website, which enables users to interactively explore, filter, and download data. We hope this dataset will help a wide variety of stakeholders, including researchers, policy makers, advocacy groups, and growers access the evidence needed to make informed and impactful decisions about how to produce food with less negative environmental impact.

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters.

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980-81 to 2020-21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

Botrytis fruit rot management: What have we achieved so far?

Botrytis cinerea is a destructive necrotrophic phytopathogen causing overwhelming diseases in more than 1400 plant species, especially fruit crops, resulting in significant economic losses worldwide. The pathogen causes rotting of fruits at both pre-harvest and postharvest stages. Aside from causing gray mold of the mature fruits, the fungus infects leaves, flowers, and seeds, which makes it a notorious phytopathogen. Worldwide, in the majority of fruit crops, B. cinerea causes gray mold. In order to effectively control this pathogen, extensive research has been conducted due to its wide host range and the huge economic losses it causes. It is advantageous to explore detection and diagnosis techniques of B. cinerea to provide the fundamental basis for mitigation strategies. Botrytis cinerea has been identified and quantified in fruit/plant samples at pre- and post-infection levels using various detection techniques including DNA markers, volatile organic compounds, qPCR, chip-digital PCR, and PCR-based nucleic acid sensors. In addition, cultural, physical, chemical, biological, and botanical methods have all been used to combat Botrytis fruit rot. This review discusses research progress made on estimating economic losses, detection and diagnosis, as well as management strategies, including cultural, physical, chemical, and biological studies on B. cinerea along with knowledge gaps and potential areas for future research.

Accumulation and translocation of lead in vegetables through intensive use of organic manure and mineral fertilizers with wastewater.

In many countries with wastewater irrigation and intensive use of fertilizers (minerals and organics), heavy metal deposition by crops is regarded as a major environmental concern. A study was conducted to determine the impact of mineral fertilizers, cow manure, poultry manure, leaf litter, and sugarcane bagasse on soil's trace Pb content and edible parts of vegetables. It also evaluated the risk of lead (Pb) contamination in water, soil, and food crops. Six vegetables (Daucus carota, Brassica oleracea, Pisum sativum, Solanum tuberosum, Raphanus sativus, and Spinacia oleracea) were grown in the field under twelve treatments with different nutrient and water inputs. The lead concentrations in soil, vegetables for all treatments and water samples ranged from 1.038-10.478, 0.09346-9.0639 mg/kg and 0.036-0.26448 mg/L, The concentration of lead in soil treated with wastewater in treatment (T6) and vegetable samples was significantly higher, exceeding the WHO's permitted limit. Mineral and organic fertilizers combined with wastewater treatment reduced lead (Pb) concentrations in vegetables compared to wastewater application without organic fertilizers. Health risk indexes for all treatments except wastewater treatment (T6) were less than one. Pb concentrations in mineral fertilizers, cow manure, poultry manure, leaf litter, and sugarcane bagasse treated were determined to pose no possible risk to consumers.

Farmland pest recognition based on Cascade RCNN Combined with Swin-Transformer.

Agricultural pests and diseases pose major losses to agricultural productivity, leading to significant economic losses and food safety risks. However, accurately identifying and controlling these pests is still very challenging due to the scarcity of labeling data for agricultural pests and the wide variety of pest species with different morphologies. To this end, we propose a two-stage target detection method that combines Cascade RCNN and Swin Transformer models. To address the scarcity of labeled data, we employ random cut-and-paste and traditional online enhancement techniques to expand the pest dataset and use Swin Transformer for basic feature extraction. Subsequently, we designed the SCF-FPN module to enhance the basic features to extract richer pest features. Specifically, the SCF component provides a self-attentive mechanism with a flexible sliding window to enable adaptive feature extraction based on different pest features. Meanwhile, the feature pyramid network (FPN) enriches multiple levels of features and enhances the discriminative ability of the whole network. Finally, to further improve our detection results, we incorporated non-maximum suppression (Soft NMS) and Cascade R-CNN's cascade structure into the optimization process to ensure more accurate and reliable prediction results. In a detection task involving 28 pest species, our algorithm achieves 92.5%, 91.8%, and 93.7% precision in terms of accuracy, recall, and mean average precision (mAP), respectively, which is an improvement of 12.1%, 5.4%, and 7.6% compared to the original baseline model. The results demonstrate that our method can accurately identify and localize farmland pests, which can help improve farmland's ecological environment.

Bacterial communities associated with Acrobeles complexus nematodes recovered from tomato crops in South Africa.

The productivity of agricultural ecosystems is heavily influenced by soil-dwelling organisms. To optimize agricultural practices and management, it is critical to know the composition, abundance, and interactions of soil microorganisms. Our study focused on Acrobeles complexus nematodes collected from tomato fields in South Africa and analyzed their associated bacterial communities utilizing metabarcoding analysis. Our findings revealed that A. complexus forms associations with a wide range of bacterial species. Among the most abundant species identified, we found Dechloromonas sp., a bacterial species commonly found in aquatic sediments, Acidovorax temperans, a bacterial species commonly found in activated sludge, and Lactobacillus ruminis, a commensal motile lactic acid bacterium that inhabits the intestinal tracts of humans and animals. Through principal component analysis (PCA), we found that the abundance of A. complexus in the soil is negatively correlated with clay content (r = -0.990) and soil phosphate levels (r = -0.969) and positively correlated with soil sand content (r = 0.763). This study sheds light on the bacterial species associated to free-living nematodes in tomato crops in South Africa and highlights the occurrence of various potentially damaging and beneficial nematode-associated bacteria, which can in turn, impact soil health and tomato production.

A global dataset for assessing nitrogen-related plant traits using drone imagery in major field crop species.

Enhancing rapid phenotyping for key plant traits, such as biomass and nitrogen content, is critical for effectively monitoring crop growth and maximizing yield. Studies have explored the relationship between vegetation indices (VIs) and plant traits using drone imagery. However, there is a gap in the literature regarding data availability, accessible datasets. Based on this context, we conducted a systematic review to retrieve relevant data worldwide on the state of the art in drone-based plant trait assessment. The final dataset consists of 41 peer-reviewed papers with 11,189 observations for 11 major crop species distributed across 13 countries. It focuses on the association of plant traits with VIs at different growth/phenological stages. This dataset provides foundational knowledge on the key VIs to focus for phenotyping key plant traits. In addition, future updates to this dataset may include new open datasets. Our goal is to continually update this dataset, encourage collaboration and data inclusion, and thereby facilitate a more rapid advance of phenotyping for critical plant traits to increase yield gains over time.

Epigenetic differences between wild and cultivated grapevines highlight the contribution of DNA methylation during crop domestication.

The domestication process in grapevines has facilitated the fixation of desired traits. Nowadays, vegetative propagation through cuttings enables easier preservation of these genotypes compared to sexual reproduction. Nonetheless, even with vegetative propagation, various phenotypes are often present within the same vineyard due to the accumulation of somatic mutations. These mutations are not the sole factors influencing phenotype. Alongside somatic variations, epigenetic variation has been proposed as a pivotal player in regulating phenotypic variability acquired during domestication. The emergence of these epialleles might have significantly influenced grapevine domestication over time. This study aims to investigate the impact of domestication on methylation patterns in cultivated grapevines. Reduced-representation bisulfite sequencing was conducted on 18 cultivated and wild accessions. Results revealed that cultivated grapevines exhibited higher methylation levels than their wild counterparts. Differential Methylation Analysis between wild and cultivated grapevines identified a total of 9955 differentially methylated cytosines, of which 78% were hypermethylated in cultivated grapevines. Functional analysis shows that core methylated genes (consistently methylated in both wild and cultivated accessions) are associated with stress response and terpenoid/isoprenoid metabolic processes. Meanwhile, genes with differential methylation are linked to protein targeting to the peroxisome, ethylene regulation, histone modifications, and defense response. Collectively, our results highlight the significant roles that epialleles may have played throughout the domestication history of grapevines.

Current and future scenarios of suitability and expansion of cassava brown streak disease, Bemisia tabaci species complex, and cassava planting in Africa.

Cassava (Manihot esculenta) is among the most important staple crops globally, with an imperative role in supporting the Sustainable Development Goal of 'Zero hunger'. In sub-Saharan Africa, it is cultivated mainly by millions of subsistence farmers who depend directly on it for their socio-economic welfare. However, its yield in some regions has been threatened by several diseases, especially the cassava brown streak disease (CBSD). Changes in climatic conditions enhance the risk of the disease spreading to other planting regions. Here, we characterise the current and future distribution of cassava, CBSD and whitefly Bemisia tabaci species complex in Africa, using an ensemble of four species distribution models (SDMs): boosted regression trees, maximum entropy, generalised additive model, and multivariate adaptive regression splines, together with 28 environmental covariates. We collected 1,422 and 1,169 occurrence records for cassava and Bemisia tabaci species complex from the Global Biodiversity Information Facility and 750 CBSD occurrence records from published literature and systematic surveys in East Africa. Our results identified isothermality as having the highest contribution to the current distribution of cassava, while elevation was the top predictor of the current distribution of Bemisia tabaci species complex. Cassava harvested area and precipitation of the driest month contributed the most to explain the current distribution of CBSD outbreaks. The geographic distributions of these target species are also expected to shift under climate projection scenarios for two mid-century periods (2041-2060 and 2061-2080). Our results indicate that major cassava producers, like Cameron, Ivory Coast, Ghana, and Nigeria, are at greater risk of invasion of CBSD. These results highlight the need for firmer agricultural management and climate-change mitigation actions in Africa to combat new outbreaks and to contain the spread of CBSD.

Tissue culture and Agrobacterium-mediated genetic transformation of the oil crop sunflower.

Sunflower is one of the four major oil crops in the world. 'Zaoaidatou' (ZADT), the main variety of oil sunflower in the northwest of China, has a short growth cycle, high yield, and high resistance to abiotic stress. However, the ability to tolerate adervesity is limited. Therefore, in this study, we used the retention line of backbone parent ZADT as material to establish its tissue culture and genetic transformation system for new variety cultivating to enhance resistance and yields by molecular breeding. The combination of 0.05 mg/L IAA and 2 mg/L KT in MS was more suitable for direct induction of adventitious buds with cotyledon nodes and the addition of 0.9 mg/L IBA to MS was for adventitious rooting. On this basis, an efficient Agrobacterium tumefaciens-mediated genetic transformation system for ZADT was developed by the screening of kanamycin and optimization of transformation conditions. The rate of positive seedlings reached 8.0%, as determined by polymerase chain reaction (PCR), under the condition of 45 mg/L kanamycin, bacterial density of OD600 0.8, infection time of 30 min, and co-cultivation of three days. These efficient regeneration and genetic transformation platforms are very useful for accelerating the molecular breeding process on sunflower.

Assessment of induced allelopathy in crop-weed co-culture with rye-pigweed model.

This study evaluates induced allelopathy in a rye-pigweed model driven by rye's (Secale cereale L.) allelopathic potential as a cover crop and pigweed's (Amaranthus retroflexus L.) notoriety as a weed. The response of rye towards pigweed's presence in terms of benzoxazinoids (BXs) provides valuable insight into induced allelopathy for crop improvement. In the 2 week plant stage, pigweed experiences a significant reduction in growth in rye's presence, implying allelopathic effects. Rye exhibits increased seedling length and BXs upsurge in response to pigweed presence. These trends persist in the 4 week plant stage, emphasizing robust allelopathic effects and the importance of different co-culture arrangements. Germination experiments show rye's ability to germinate in the presence of pigweed, while pigweed exhibits reduced germination with rye. High-performance liquid chromatography with diode-array detection (HPLC-DAD) analysis identifies allelopathic compounds (BXs), 2,4-dihydroxy-1,4-benzoxazin-3-one (DIBOA) and 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA) in rye. Rye significantly increases BX production in response to pigweed, age-dependently. Furthermore, pigweed plants are screened for possible BX uptake from the rhizosphere. Results suggest that allelopathy in rye-pigweed co-cultures is influenced by seed timing, and age-dependent dynamics of plants' allelopathic compounds, providing a foundation for further investigations into chemical and ecological processes in crop-weed interactions.

Cover crops support the climate change mitigation potential of agroecosystems.

Cover crops have the potential to mitigate climate change by reducing negative impacts of agriculture on ecosystems. This study is first to quantify the net climate change mitigation impact of cover crops including land-use effects. A systematic literature and data review was conducted to identify major drivers for climate benefits and costs of cover crops in maize (Zea maize L.) production systems. The results indicate that cover crops lead to a net climate change mitigation impact (NCCMI) of 3.30 Mg CO2e ha-1 a-1. We created four scenarios with different impact weights of the drivers and all of them showing a positive NCCMI. Carbon land benefit, the carbon opportunity costs based on maize yield gains following cover crops, is the major contributor to the NCCMI (34.5% of all benefits). Carbon sequestration is the second largest contributor (33.8%). The climate costs of cover crops are mainly dominated by emissions from their seed production and foregone benefits due to land use for cover crops seeds. However, these two costs account for only 15.8% of the benefits. Extrapolating these results, planting cover crops before all maize acreage in the EU results in a climate change mitigation of 49.80 million Mg CO2e a-1, which is equivalent to 13.0% of the EU's agricultural emissions. This study highlights the importance of incorporating cover crops into sustainable cropping systems to minimize the agricultural impact to climate change.

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI.

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.

Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India.

Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause economic deflation due to the loss of lands, properties, and agricultural production. Hence, assessing the impact of such hazards in the existing agricultural system is of utmost importance to understand the probable crop loss. In this paper, we studied the efficiency of the remotely sensed microwave data to map the croplands affected by the flash flood that occurred in July 2023 in Himachal Pradesh, a mountainous state in the Indian Himalayan Region. The Una, Hamirpur, Kangra, and Sirmaur districts were identified as the most affected areas, with about 9%, 6%, 5.74%, and 3.61% of the respective districts' total geographical area under flood. Further, four machine learning algorithms (random forest, support vector regressor, k-nearest neighbor, and extreme gradient boosting) were evaluated to forecast maize and rice crop production and potential loss during the Kharif season in 2023. A regression algorithm with ten predictor variables consisting of the cropland area, two vegetation indices, and seven climatic parameters was applied to forecast the maize and rice production in the state. Amongst the four algorithms, random forest showed outstanding performance compared to others. The random forest regressor estimated the production of maize and rice with R2 more than 0.8 in most districts. The mean absolute error and the root mean squared error obtained from the random forest regressor were also minimal compared to the others. The maximum production loss of maize is estimated for Solan (54.13%), followed by Una (11.06%), and of rice in Kangra (19.1%), Una (18.8%) and Kinnaur (18.5%) districts. This indicated the utility of the proposed approach for a quick in-season forecast on crop production loss due to climatic hazards.

Spatial and temporal distribution of optimal maize sowing dates in Nigeria.

Climate change and inter-annual variability cause variation in rainfall commencement and cessation which has consequences for the maize growing season length and thus impact yields. This study therefore sought to determine the spatially explicit optimum maize sowing dates to enable site specific recommendations in Nigeria. Gridded weather and soil data, crop management and cultivar were used to simulate maize yield from 1981-2019 at a scale of 0.5°. A total of 37 potential sowing dates between 1 March and 7 November at an interval of 7 days for each year were evaluated. The optimum sowing date was the date which maximizes yield at harvest, keeping all other management factors constant. The results show that optimum sowing dates significantly vary across the country with northern Nigeria having notably delayed sowing dates compared to southern Nigeria which has earlier planting dates. The long-term optimal sowing dates significantly (p<0.05), shifted between the 1980s (1981-1990), and current (2011-2019), for most of the country. The most optimum planting dates of southern Nigeria shifted to later sowing dates while most optimum sowing dates of central and northern Nigeria shifted to earlier sowing dates. There was more variation in optimum sowing dates in the wetter than the drier agro-ecologies. Changes in climate explain changes in sowing dates in wetter agro-ecologies compared to drier agro-ecologies. The study concludes that the optimum sowing dates derived from this study and the corresponding methodology used to generate them can be used to improve cropping calendars in maize farming in Nigeria.

Seasonal fluctuation and alternative host plants of vegetable crop-infesting tephritids in non-vegetable growing areas in South Sudanese zone of Burkina Faso.

This study was carried out in 3 types of biotopes where vegetable crops are not grown to highlight their contribution to the dynamics of vegetable-infesting flies. To this end, a trapping system based on a sexual attractant, the Cuelure associated with an insecticide was set up in 18 biotopes (6 natural areas, 6 mango orchards, and 6 agroforestry parks) in the regions of Hauts Bassins and Cascades in the South-West of Burkina Faso. During the trapping monitoring, which was done every 2 wk to collect insects captured, fruits present in 3 types of biotopes were sampled and incubated for insect emergence. Ten Dacus (Fabricius) [Diptera: Tephritidae] species and Zeugodacus cucurbitae (Coquillett) [Diptera: Tephritidae] were trapped in the study area. The predominant species captured was Z. cucurbitae (52.93%) followed by Dacus punctatifrons (Karsch) [Diptera: Tephritidae] (29.89%) and Dacus humeralis (Bezzi) (12.71%). Six tephritid species were emerged from 6 wild fruit species belonging to Cucurbitaceae, Apocynaceae, and Passifloraceae families. Fruit flies were more abundant from Jul to Nov with peaks observed in Aug or Oct depending on the species. Citrullus colocynthis L. (Cucurbitaceae), Lagenaria sp. (Cucurbitaceae), Passiflora foetida L. (Passifloraceae), and Passiflora sp. acted as reservoir host plants of Dacus ciliatus (Loew), Dacus bivittatus (Bigot), Dacus vertebratus (Bezzi) [Diptera: Tephritidae], D. punctatifrons, and Z. cucurbitae, the major vegetable insect pests in West Africa. The 3 types of biotopes acted as suitable refuge areas of vegetable crop-infesting fruit flies either for the favorable microclimate or for the alternative host plants.

Effects of experimental drought and plant diversity on multifunctionality of a model system for crop rotation.

In low-diversity productive grasslands, modest changes to plant diversity (richness, composition and relative abundance) may affect multiple ecosystem functions (multifunctionality), including yield. Despite the economic importance of productive grasslands, effects of plant diversity and environmental disturbance on multifunctionality are very rarely quantified. We systematically varied species richness, composition, and relative abundance of grassland ley communities and manipulated water supply (rainfed and drought) to quantify effects of diversity and environmental disturbance on multifunctionality. We then replaced the grassland leys with a monoculture crop to investigate 'follow-on' effects. We measured six agronomy-related ecosystem functions across one or both phases: yield, yield consistency, digestibility and weed suppression (grassland ley phase), legacy effect (effect on follow-on crop yield), and nitrogen fertiliser efficiency (full rotation). Drought reduced most ecosystem functions, although effects were species- and function-specific. Increased plant diversity affected mean performance, and reduced variation, across the six functions (contributing to multifunctional stability). Multifunctionality index values across a wide range of mixture diversity were higher than the best monoculture under both rainfed and drought conditions (transgressive over-performance). Higher-diversity, lower-nitrogen (150N) mixtures had higher multifunctionality than a low-diversity, higher-nitrogen (300N) grass monoculture. Plant diversity in productive grasslands is a practical farm-scale management action to mitigate drought impacts and enhance multifunctionality of grassland-crop rotation systems.

Harnessing Phytohormones: Advancing Plant Growth and Defence Strategies for Sustainable Agriculture.

Phytohormones, pivotal regulators of plant growth and development, are increasingly recognized for their multifaceted roles in enhancing crop resilience against environmental stresses. In this review, we provide a comprehensive synthesis of current research on utilizing phytohormones to enhance crop productivity and fortify their defence mechanisms. Initially, we introduce the significance of phytohormones in orchestrating plant growth, followed by their potential utilization in bolstering crop defences against diverse environmental stressors. Our focus then shifts to an in-depth exploration of phytohormones and their pivotal roles in mediating plant defence responses against biotic stressors, particularly insect pests. Furthermore, we highlight the potential impact of phytohormones on agricultural production while underscoring the existing research gaps and limitations hindering their widespread implementation in agricultural practices. Despite the accumulating body of research in this field, the integration of phytohormones into agriculture remains limited. To address this discrepancy, we propose a comprehensive framework for investigating the intricate interplay between phytohormones and sustainable agriculture. This framework advocates for the adoption of novel technologies and methodologies to facilitate the effective deployment of phytohormones in agricultural settings and also emphasizes the need to address existing research limitations through rigorous field studies. By outlining a roadmap for advancing the utilization of phytohormones in agriculture, this review aims to catalyse transformative changes in agricultural practices, fostering sustainability and resilience in agricultural settings.

County-level intensity of carbon emissions from crop farming in China during 2000-2019.

Agriculture is an important contributor to global carbon emissions. With the implementation of the Sustainable Development Goals of the United Nations and China's carbon neutral strategy, accurate estimation of carbon emissions from crop farming is essential to reduce agricultural carbon emissions and promote sustainable food production systems in China. However, previous long-term time series estimates in China have mainly focused on the national and provincial levels, which are insufficient to characterize regional heterogeneity. Here, we selected the county-level administrative district as the basic geographical unit and then generated a county-level dataset on the intensity of carbon emissions from crop farming in China during 2000-2019, using random forest regression with multi-source data. This dataset can be used to delineate spatio-temporal changes in carbon emissions from crop farming in China, providing an important basis for decision makers and researchers to design agricultural carbon reduction strategies in China.

Ecological weed management and square planting influenced the weed management, and crop productivity in direct-seeded rice.

Herbicide use may pose a risk of environmental pollution or evolution of resistant weeds. As a result, an experiment was carried out to assess the influence of different non-chemical weed management tactics (one hoeing (HH) at 12 DAS followed by (fb) one hand weeding at 30 DAS, one HH at 12 DAS fb Sesbania co-culture and its mulching, one HH at 12 DAS fb rice straw mulching @ 4t ha-1, one HH at 12 DAS fb rice straw mulching @ 6 t ha-1) on weed control, crop growth and yield, and economic returns in direct-seeded rice (DSR). Experiment was conducted during kharif season in a split-plot design and replicated thrice. Zero-till seed drill-sown crop (PN) had the lowest weed density at 25 days after sowing (DAS), while square planting geometry (PS) had the lowest weed density at 60 DAS. PS also resulted in a lower weed management index (WMI), agronomic management index (AMI), and integrated weed management index (IWMI), as well as higher growth attributes, grain yield (4.19 t ha-1), and net return (620.98 US$ ha-1). The cultivar Arize 6444 significantly reduced weed density and recorded higher growth attributes, yield, and economic return. In the case of weed management treatments, one HH at 12 DAS fb Sesbania co-culture and its mulching had the lowest weed density, Shannon-weinner index and eveness at 25 DAS. However, one hoeing at 12 DAS fb one hand weeding at 30 DAS (HH + WH) achieved the highest grain yield (4.85 t ha-1) and net returns (851.03 US$ ha-1) as well as the lowest weed density at 60 DAS. PS × HH + WH treatment combination had the lowest weed persistent index (WPI), WMI, AMI, and IWMI, and the highest growth attributes, production efficiency, and economic return.