Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies

Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.

Development and Fabrication of Manually Push-Pull Type Conical Weeder for Bangladesh Condition

In Bangladesh, the use of machinery in agriculture production is fast rising. Researchers are developing technology to replace traditional hand weeding to manage weeds in rice fields. The present study has been taken to increase weeding efficiency and reduce the drudgery in weeding and mulching. A line-to-line distance of 20 cm, the operation is push-pull, and field operating condition at 2 - 4 cm standing water (for softening the field) was the designed hypothesis. The weeder consists of a skid/float, float holder, float adjuster, main body frame, rotor, axel, bush, rotor holder, rotor holder adjuster, handle, handle griper, handle holder, handle height adjuster, nut, bolt, etc. The designed weeder was fabricated using MS sheet, MS pipe, MS flat bar, MS nut-bolt, etc. When the rotors perform back and forth, the weeder’s two conical rotors with six plain blades and six serrated blades work together to uproot and bury the weeds. It also contains a 2 mm thick float assembly with a precise angle of 22 degrees. Weeds are uprooted by the weeder’s blades and buried in the muddy soil. It causes topsoil disturbance and enhances aeration. The weeding efficiency and capacity of the conical weeder were 81.92% and 0.0203 ha/h respectively. With a push-pull operation, the weeder can uproot and bury the weeds in a single row at a time. The pushing force and weight of weeder were 43.42 N and 5.6 kg respectively. Farmers can use this weeder to increase their comfort and reduce the drudgery associated with weeding and mulching in their fields.

Cultivation system influenced the critical period for weed control in cotton field

Background The critical period of weed control(CPWC) refers to the period of time during the crop growth cycle when weeds must be controlled to prevent yield losses.Ultra-narrow row(UNR) is a method of planting of cotton in rows that are 25 cm or less apart.Amongst cultural techniques for weed control,the use of narrow row spacing is considered to be a most promising approach that can effectively suppress weed growth and provide greater yields in cotton.This cultivation system can shorten the length of the critical weed-crop interference duration and results in greater yield.The current research aimed to determination of critical time of weed control in cotton(Gossypium hirsutum L.) under conventional and ultra-narrow row spacing conditions.Field experiments were arranged as factorial experiment in a randomized complete block design with three replications.Factors were cultivation system(conventional(50 cm row spacing) and ultra narrow row(25 cm row spacing and weed treatment including 30,45,60,and 75 days weeding after emergence during the growing season(weed free),and 30,45,60,and 75 without weeding(weed infested) in the growing season along with weedy and weed-free from sowing to harvesting.A four-parameter loglogistic model was fit to the two sets of relating relative crop yield to data obtained from increasing durations of weed interference and lengths of weed-free period.Results In both years and cultivation systems,the relative yield of cotton decreased with the increasing duration of weed-interference but increased with the increasing duration of weed-free period.Ultra-narrow row cultivation delayed the beginning of the CPWC in cotton.Under ultra-narrow row condition,the CPWC ranged from 21 to 99 days after germination in 2021 and 23 to 91 days in 2022 based on the 5% acceptable yield loss.Under conventional cultivation CPWC ranged from 17 to 102 days after emergence in 2021 and 18 to 95 days after emergence in 2022.Conclusions Under both conventional and Ultra-narrow row conditions,weed interference reduces seed yield.Under ultra-narrow row condition,weed interference until 21.1–23.5 days after cotton emergence and under conventional condition,weed interference until 16.9–18.5 days after cotton emergence had not significant reduction on cotton yield.

Weed Species Composition in Paddy Field of Usur Town,Bade Local Government,Yobe State,Nigeria

Farmers are eager to know the various types of weeds in paddy fields.This will help in choosing the best weed management practice for effective weed control as well as reducing rice yield losses.The objectives of the study are to identify the weeds species affecting the rice field,to assess the composition of weeds species,to classify the weed species into different families,genera,species,common names,Hausa names,lifecycles,life forms,native/exotic species,propagation and uses,and to determine the dominant weed species.Random vegetation surveys were conducted.Weeds observed were photographed,and prepared as herbarium specimens.Standard key manuals and checklists were utilized for weed identification and later organized using the Angiosperm Phylogeny Group(APG)classification system.A total number of 72 plants species distributed within 16 families and 50 genera were inventoried.The annuals(66.67%)were the dominant weed followed by perennials(33.33%)while biennials were the least.The broad leaves were the dominant weed(44.61%)identified followed by Poaceae(27.7%)and Sedges(11.11%).Results obtained from this study could be useful in choosing the best management practice and in making a decision on the choice of herbicides and directing research towards improved weed control measures.

Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)approaches.Weed plays a vital role in influencing crop productivity.The wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are increased.Since the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision agriculture.The proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as weeds.The proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed classification.The parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

Effect of Bt traits on transgenic rice's growth and weed competitiveness

Transgene escape could lead to genetically modified rice establishing wild populations in the natural environment and competing for survival space with weeds.However,whether the expression of the Bacillus thuringiensis(Bt)gene in rice will alter the relationship between transgene plants and weeds and induce undesirable environmental consequences are poorly understood.Thus,field experiments were conducted to investigate the weed competitiveness and assess the ecological risk of transgenic Bt rice under herbicide-free and lepidopterous pest-controlled environments.Results showed that weed–rice competition in the direct-sowing(DS)field was earlier and more severe than that in the transplanting(TP)field,which resulted in a significant decrease in biomass and yield in DS.However,conventional Bt and non-Bt rice yield was not significantly different.The weed number,weed coverage ratio,and weed diversity of conventional Bt rice were significantly higher than those of non-Bt rice at the early growth and mature stages,especially in DS plots,suggesting that Bt traits did not increase the weed competitiveness of transgenic rice and had no negative effect on weed diversity.Grain yield and weed number varied between different hybrid rice lines,but those differences were insignificant between Bt and non-Bt rice.The number of insects increased with the increase of weeds in hybrid rice plots,whereas the insect number and diversity did not display a significant difference between Bt and non-Bt rice.Therefore,the ecological risk of transgenic Bt rice is comparable to non-Bt rice.

Evaluation of Pre-Emergence and Post-Emergence Herbicides for Weed Management in Miscanthus sacchariflorus and Miscanthus sinensis

Miscanthus,is a promising bioenergy crop,considered superior to other bioenergy crops because of its higher water and nutrient use efficiency,cold tolerance,and higher production of biomass.Broadleaf weeds and grass weeds,cause major problems in the Miscanthus field.A field experiment was conducted in 2018 and 2019,to assess the effects of pre-emergence(alachlor and napropamide)and post-emergence herbicides(nicosulfuron,dicamba,bentazon,and glufosinate ammonium)on broadleaf and grass weeds in M.sinensis and M.sacchariflorus fields.The weed control efficiency and phytotoxicity of pre-and post-emergence herbicides were evaluated at 30 days after treatment(DAT)and compared to those of the control plots.The results showed wide variations in the susceptibility of the weed species to the treated herbicides.Treatment with nicosulfuron 40 g.a.i.ha^(-1) provided the most effective overall weed control(with 10%visual injury),without affecting the height and biomass of neither Miscanthus species in the field.Post-emergence herbicides such as glufosinate ammonium 400 g.a.i.ha^(-1) and dicamba 482 g.a.i.ha^(-1) were effective and inhibited the growth and density of the majority of weeds to a 100%;however,they showed significant phytotoxicity(toxicity scale of 1-10)to both species of Miscanthus.The application of glufosinate ammonium caused severe injuries to the foliar region(90%visual injury)of both Miscanthus sps.Comparatively,M.sinensis showed a slightly higher tolerance to the herbicides nicosulfuron,bentazon and napropamide with 10%visual injury at the recommended dose than M.sacchariflorus.The present study clearly showed that infestation of broadleaf and grass weeds in Miscanthus fields can cause significant damage to the growth and biomass of Miscanthus and applying pre-emergence and post-emergence herbicides effectively controls the high infestation of these weeds.

Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

The overgrowth of weeds growing along with the primary crop in the fields reduces crop production.Conventional solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used herbicides.The application of herbicide is effective but causes environmental and health concerns.Hence,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary plants.Motivated by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this paper.The key objective of this study is to detect plant and non-plant(weed)parts from crop images.With the help of object detection,the precise location of weeds from images can be achieved.The dataset is collected manually from a private farm in Gandhinagar,Gujarat,India.The combined approach of classification and object detection is applied in the proposed model.The Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object detection.We have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed work.CenterNet outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.

Fungal Flora on Weeds in the Cashew (Anacardium occidentale L.) Orchard in Côte d’Ivoire

Since 2015, Côte d’Ivoire has been the world’s largest cashew producer. However, cashew orchards in Côte d’Ivoire are infected by fungal diseases that weaken production. And the contribution of weeds to the spread of these diseases is not yet understood. This study was initiated with the aim of establishing the role of weeds in the proliferation of pathogenic fungi in orchards. It consisted of a survey of weeds showing disease symptoms in cashew orchards in Côte d’Ivoire from February 2021 to July 2022. The itinerant method was used for the weed inventory. Symptomatic leaves were collected and sent to the laboratory for diagnosis on PDA (Potatoes Dextrose Agar) medium. In total, 50 species in 46 genera and 23 families were recorded. Laboratory diagnosis of the samples showed that 80% of the weeds identified harboured pathogenic fungi. The highest infection rates were obtained on Danielia oliveri R. (99.33% to 100%), Vitellaria paradoxa G. (100%), Pterocarpus erinaceus P. (83.91% to 99.33%), Micuna pruriens L. (98.33% to 100%) and Isoberlinia doka C. et S. (56.33% to 100%). The diagnosis revealed the presence of Lasiodiplodia sp, Colletotrichum sp, Pestalotia sp, Alternaria sp and Curvularia sp on weeds in the cashew orchard in Côte d’Ivoire.

An Adaptive Edge Detection Algorithm for Weed Image Analysis

Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops.Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy.The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields.Some weed methods have been proposed for these fields;however,these algorithms still have challenges as they were implemented against controlled environments.Therefore,in this paper,a weed image analysis approach has been proposed for the system of weed classification.In this system,for preprocessing,a Homomorphic filter is exploited to diminish the environmental factors.While,for feature extraction,an adaptive feature extraction method is proposed that exploited edge detection.The proposed technique estimates the directions of the edges while accounting for non-maximum suppression.This method has several benefits,including its ease of use and ability to extend to other types of features.Typically,low-level details in the formof features are extracted to identify weeds,and additional techniques for detecting cultured weeds are utilized if necessary.In the processing of weed images,certain edges may be verified as a footstep function,and our technique may outperform other operators such as gradient operators.The relevant details are extracted to generate a feature vector that is further given to a classifier for weed identification.Finally,the features have been used in logistic regression for weed classification.The model was assessed against logistic regression that accurately identified different kinds of weed images in naturalistic domains.The proposed approach attained weighted average recognition of 98.5%against the weed images dataset.Hence,it is assumed that the proposed approach might help in the weed classification system to accurately identify narrow and broad weeds taken captured in real environments.

Estimating Carbon Capture Potential of Fallow Weeds in Rice Cropping Systems

Weeds occurred during the fallow season can well perform the function of carbon(C)capture due to receiving little human disturbance.This study aimed to evaluate the C capture potential of fallow weeds in rice(Oryza sativa L.)cropping systems.A six-region,two-year on-farm investigation and a three-year tillage experiment were conducted to estimate C capture in fallow weeds in rice cropping systems.The on-farm investigation showed that the average mean C capture by fallow weeds across six regions and two years reached 112 g m^(-2).The tillage experiment indicated that no-tillage practices increased C capture by fallow weeds by 80%on average as compared with conventional tillage.The results of this study not only contribute to an understanding of C capture potential of fallow weeds in rice cropping systems,but also provide a reference for including fallow weeds in the estimation of vegetative C sink.

Intelligent Fish Behavior Classification Using Modified Invasive Weed Optimization with Ensemble Fusion Model

Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management deci-sions about recirculating the aquaculture system while decreasing labor.The classic detection approach involves placing sensors on the skin or body of the fish,which may interfere with typical behavior and welfare.The progress of deep learning and computer vision technologies opens up new opportunities to understand the biological basis of this behavior and precisely quantify behaviors that contribute to achieving accurate management in precision farming and higher production efficacy.This study develops an intelligent fish behavior classification using modified invasive weed optimization with an ensemble fusion(IFBC-MIWOEF)model.The presented IFBC-MIWOEF model focuses on identifying the distinct kinds of fish behavior classification.To accomplish this,the IFBC-MIWOEF model designs an ensemble of Deep Learning(DL)based fusion models such as VGG-19,DenseNet,and Effi-cientNet models for fish behavior classification.In addition,the hyperparam-eter tuning of the DL models is carried out using the MIWO algorithm,which is derived from the concepts of oppositional-based learning(OBL)and the IWO algorithm.Finally,the softmax(SM)layer at the end of the DL model categorizes the input into distinct fish behavior classes.The experimental validation of the IFBC-MIWOEF model is tested using fish videos,and the results are examined under distinct aspects.An Extensive comparative study pointed out the improved outcomes of the IFBC-MIWOEF model over recent approaches.

Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield.Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns.Weed control has become one of the significant problems in the agricultural sector.In traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same way.For more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type.This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture.This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision Agriculture.The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture.For image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise removal.In addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature vectors.To detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance.The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset.The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.

Effects of Different Herbicides on Weed Control in Alfalfa Field

[Objectives]The paper was to systematically study the technology of weed control in alfalfa field.[Methods]Reviving alfalfa field and newly sown alfalfa field after emergence were selected,and the effects of different herbicides on weed control and alfalfa yield were discussed.[Results]The optimal herbicides after alfalfa reviving were 5%imazethapyr and 10%imazethapyr,and the optimal dosages were 1.5 and 1.05 L/hm 2,respectively.The optimal herbicides after emergence of newly born alfalfa were 5%imazethapyr and 10%imazethapyr,and the optimal dosages were 1.5 and 0.75 L/hm 2,respectively.[Conclusions]This study will provide a technical support for high quality production of alfalfa.

Weed Classification Using Particle Swarm Optimization and Deep Learning Models

Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and quality.Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity.To address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image classification.Throughout this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed datasets.The Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed images.An effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification accuracy.The proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed classification.This work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.

Weed Management Practices in Nursery Propagation

Weeds are inimical to the nursery growers as they negatively interfere with the growth and aesthetic value of nursery crops. Propagated crops are more vulnerable to weed competition. Nursery growers are adopting hand weeding, mulching, and different herbicides to get rid of the weeds in propagation. However, the most effective and efficient methods for weed control in propagation are still obscure. In this study, we comprehensively review the most used propagation techniques and weed management practices along with their pros and cons. Hand weeding is the most common method of weed control, but it is labor intensive and costly. Nowadays, herbicides are widely used for weeds management. But there are a limited number of registered and labelled herbicides for greenhouse use. Most of the herbicides contain dinitroanilines (DNAs) which inhibit root growth. Along with the leaching problem, several detrimental effects of herbicides have been revealed in propagation. Considering drawbacks of the use of herbicides, mulching in propagation is gaining popularity. But mulch type and depth may affect rooting of cuttings and weed control efficacy. Therefore, it is crucial to conduct additional research aimed at discovering efficient mulching materials and preemergence herbicides for weed control during propagation, while preserving root initiation, plant development, and growth.

Taxonomic Studies of Weed Communities Growing in Date Palm and Christ’s Thorn Jujube Farms in Ad-Dawadimi, KSA

The problem of food shortage is one of the most important problems facing many countries in the world. Various factors affect the decline in crop productivity. Where weeds are the most important reasons that cause a huge loss in crop productivity. Studying agricultural ecosystems, knowing their components and explaining the relationship between all of their components, helps a lot in achieving the highest productivity of different crops in addition to benefiting from some types of weeds, as well as, identifying appropriate methods to control the growth of weeds. In this study, 60 species were listed. The most frequent plant families were Asteraceae, Poaceae and Zygophyllaceae. Annuals were the most common life span, as well as, therophytes were the most frequent life form. The most frequent floristic categories were Saharo-Sindian-Sudano-Zambezian and Saharo-Sindian regions. The most famous indicators of biodiversity (species richness, species evenness and species diversity) have been calculated. For managing and classifying data PC-ORD program (TWINSPAN and DCA analyses) was used.

A Proposed Method for Evaluating Management Feasibility When Determining Weed Control Priorities after Major Fires and Floods

Major fires and floods have enormous impacts on natural ecosystems and are predicted to increase in frequency with global warming.Land managers need to make decisions on the prioritisation of weeds for control in post-disturbance landscapes,but little is available in the way of guidance to support timely decision making.Semi-quantitative models(e.g.,scoring systems)have been employed routinely in weed risk assessment,which considers the potential impacts posed by weeds,as well as the likelihood of these impacts being realised.Some progress has been made in the development of similar models addressing the topic of weed risk management.Under conditions prevailing after major disturbances,changes(both positive and negative)can be expected in the multiple factors that determine weed management feasibility,relative to pre-disturbance conditions.A semi-quantitative model is proposed that is based on the key factors that contribute to weed management feasibility in post-disturbance environments,along with annotated modules that could be used by land managers in both post-fire and post-flood situations.The fundamental challenge for weed management in these scenarios lies in the identification of differences between weeds and native species in relation to(1)patterns of seedling emergence;and(2)detectability relative to the growth stage.These two factors will determine the timing of control actions that are designed to address the trade-off between weed control and off-target damage during the period when both types of plants are recovering from a major disturbance event.The model is intuitively sound,but field testing is required to determine both its practical value and any necessary improvement.

Influence of Adjuvants on the Efficacy of Tolpyralate plus Atrazine for the Control of Annual Grass and Broadleaf Weeds in Corn with and without Roundup WeatherMAX^®

Tolpyralate is a new HPPD-inhibiting herbicide that is efficacious on annual grass and broadleaf weed species in corn. For maximum herbicide performance of tolpyralate, it is recommended that atrazine is tank mixed with tolpyralate along with the adjuvants methylated seed oil concentrate (MSO) plus urea ammonia nitrate (UAN). A common use pattern of tolpyralate plus atrazine will be in a tank mix with Roundup WeatherMAX®due to the high proportion of corn acres that are seeded to Roundup Ready®hybrids in Eastern Canada. There is no information in the peer-reviewed literature if the adjuvant system in Roundup WeatherMAX®is adequate for optimal herbicide performance of tolpyralate plus atrazine, or if MSO and UAN are still required. Six field trials were conducted over two years near Ridgetown and Exeter, ON, Canada to determine if adjuvants are still required when tolpyralate plus atrazine is tank mixed with Roundup WeatherMAX®in corn. Tolpyralate plus atrazine plus MSO and Roundup WeatherMAX®plus tolpyralate plus atrazine provided excellent control of velvetleaf, pigweed spp, common ragweed, lambsquarters, ladysthumb, wild mustard, flower-of-an-hour, barnyardgrass and green foxtail in this study. Results of this study show that in the absence of Roundup WeatherMAX®, weed control with tolpyralate plus atrazine was improved substantially with the addition of MSO;however, there was little to no increase in weed control with the addition of UAN. When tolpyralate plus atrazine was co-applied with Roundup WeatherMAX®, there was no improvement in weed control with the addition of MSO and/or UAN.

Weed growth, herbicide efficacy, and rice productivity in dry seeded paddy field under different wheat stubble management methods

To ascertain the influence of wheat stubble management options and chemical weed control methods on weed growth and productivity of dry direct-seeded fine rice, a two years' field study was undertaken at the Agronomic Research Farm, University of Agriculture, Faisalabad, Pakistan in 2013 and 2014. Different wheat stubble management methods, viz., incorporation, burning and retention were executed during seed-bed preparation. While, herbicide treatments comprised of a weed check, weed free, pendimethalin followed by tank mixture of fenoxaprop p-ethyl+ethoxysulfuron ethyl, and bispyribac sodium followed by tank mixture of fenoxaprop p-ethyl+ethoxysulfuron ethyl. Results revealed that weed control efficacy of both herbicide treatments ranged from 84 to 94%. Herbicide treatments significantly reduced weed density(88–90%) and dry weight(86–88%), while improved the rice growth attributes compared with weed check. Application of bispyribac sodium followed by tank mixture of fenoxaprop p-ethyl+ethoxysulfuron ethyl in stubble retention recorded 226 and 273% increase in kernel yield over weedy check in 2013 and 2014, respectively. In stubble incorporation, pendimethalin followed by tank mixture of fenoxaprop p-ethyl+ethoxysulfuron ethyl was more effective in increasing(256–293%) rice yields over weedy check. Among different treatment combinations, the maximum net benefits(1 397.49^(–1) 472.22 USD ha^(–1)), net returns(636–700 USD ha^(–1)), benefit cost ratio(1.77^(–1).83) and marginal rate of return(2 187–2 330%) were recorded with the application of bispyribac sodium followed by fenoxaprop p-ethyl+ethoxysulfuron ethyl in stubble retention. In crux, application of bispyribac sodium followed by tank mixture of fenoxaprop p-ethyl+ethoxysulfuron ethyl in stubble retention is efficient approach to control weeds, and get maximum rice productivity and net economic returns under dry seeded system.