Significant reduction of ammonia emissions while increasing crop yields using the 4R nutrient stewardship in an intensive cropping system

Ammonia (NH_3) emissions should be mitigated to improve environmental quality.Croplands are one of the largest NH_3sources,they must be managed properly to reduce their emissions while achieving the target yields.Herein,we report the NH_3 emissions,crop yield and changes in soil fertility in a long-term trial with various fertilization regimes,to explore whether NH_3 emissions can be significantly reduced using the 4R nutrient stewardship (4Rs),and its interaction with the organic amendments (i.e.,manure and straw) in a wheat–maize rotation.Implementing the 4Rs significantly reduced NH_3 emissions to 6 kg N ha~(–1) yr~(–1) and the emission factor to 1.72%,without compromising grain yield (12.37 Mg ha~(–1) yr~(–1))and soil fertility (soil organic carbon of 7.58 g kg~(–1)) compared to the conventional chemical N management.When using the 4R plus manure,NH_3 emissions (7 kg N ha~(–1) yr~(–1)) and the emission factor (1.74%) were as low as 4Rs,and grain yield and soil organic carbon increased to 14.79 Mg ha~(–1) yr~(–1) and 10.09 g kg~(–1),respectively.Partial manure substitution not only significantly reduced NH_3 emissions but also increased crop yields and improved soil fertility,compared to conventional chemical N management.Straw return exerted a minor effect on NH_3 emissions.These results highlight that 4R plus manure,which couples nitrogen and carbon management can help achieve both high yields and low environmental costs.

Responses of Crop Yields and Soil Fertility to Long-term Nutrient Lacking

[Objective] Long-term （over 18 years） fertilization experiments were con- ducted to study the responses of crop yields and soil fertility to long-term nutrient lacking at Zhengzhou in China. [Method] The present study consisted of five treat- ments： 1 CK （no fertilizer or manure）, （2） NP （nitrogen and phosphorus fertilizer applied）, 31 NK （nitrogen and potassium fertilizer applied）, 4 PK （phosphorus and potassium fertilizer applied） and ：5 NPK （nitrogen, phosphorus and potassium fertil- izer applied）. [Result] Lacking of nitrogen or phosphorus led to a low yield; however, there was no significant difference in grain yields between the NP and NPK treat- ments which maintained a higher yield. Receiving no phosphorus, soil available phosphorus content declined to about 2.5 mg/kg. The concentration of soil ex- changeable potassium remained constant at a level of 60 mg/kg under the treat- ments without potassium fertilizer addition. Soil potassium spontaneous supply ca- pacity fluctuated around 100%.[Conclusion] In fluvo-aquic soil, nitrogen and phos- phorus were two key limiting factors to grain yields, biomass and yield component factors of wheat and maize, while potassium was not. However, potassium defi- ciency may occur in the future if there was still no potassium fertilizer applied.

Assessing the Impacts of Climate Change on Crop Yields in Different Agro-climatic Zones of India

The study attempts to estimate and predict climate impact on crop yields using future temperature projections under two climate emissions scenarios of RCP 4.5 and 8.5 for three different time periods(2030s,2050s and 2080s)across Agro-climatic zones(ACZ)of India.During the period 1966-2011,a significant rise was observed in both the annual mean maximum and minimum temperature across ACZs.Rainfall recorded an annual decline in Himalayan Regions and Gangetic Plains and a rise in Coastal Regions,Plateau&Hills and Western Dry Region.Our results showed high heterogeneity in climate impact on kharif and rabi crop yields(with both negative and positive estimates)across ACZs.It was found that rainfall had a positive effect on most of crop yields,but was not sufficient enough to counterbalance the impact of temperature.Changes in crop yield were more pronounced for higher emission scenario of RCP 8.5.Thus,it was evident that the relative impacts of climate change and the associated vulnerability vary by ACZs,hence comprehensive crop and region-specific adaptation measures should be emphasized that helps in enhancing resilience of agricultural system in short to medium term.

Utilization of Compost as a Soil Amendment to Increase Soil Health and to Improve Crop Yields

Compost amendments have remarkable potential for improving soil structure, porosity and water holding capacity. Soil health is the ability to function as a living system, to sustain plant and animal productivity, to enhance water and air quality, and to promote plant and animal health. Soil health can be estimated by measuring the total living microbial biomass, retained carbon, odor, and texture. Poor or deteriorating soil health is threatening food security. The potential for compost to reverse these negative trends is transformative if means and methods for large scale composting and compost amendments can be developed. A field-scale compost soil amendment project was implemented in Rapid City, South Dakota. The compost was added to a soil plot at 5 wt% and 10 wt% and the results were compared with an adjacent untreated plot without any compost addition. Measurements of soil health characteristics indicate that compost amendments improve soil health, crop yields, and soil water content. Treating soils with compost has the potential to reverse global deteriorating soil health.

Influence of Long Term Fertilization with Different Mineral Fertilizers and Farmyard Manure on Some Soil Chemical Properties and Crop Yields *1

A long term fertilization experiment was carried out in an experimental field in Lyczyn near Warsaw, Poland. Application ofmineral fertilizers, especially N fertilizers with and without farmyard manure accelerated the acidification process of the soil. Application of 1.6 t CaO ha -1 every four years was essential to maintenance of the soil pH KCl at 5.5～6.6 and base saturation degree above 60%. Application of 50 t farmyard manure ha -1 every 4 years, which contained 46 kg P and 240 kg K, was sufficient to maintain both the K and P fertility of the soil. Besides, it was beneficial to alleviating soil acidification. As a result of long term unbalanced fertilization, yield responses to N, P and K fertilizers increased significantly with time. The efficiency of N from farmyard manure was found to be comparable to that of N fertilizer during 1988～1991.

Effects of Peat Extract on Crop Yields

This paper reports the results of plot experiments canducted in 1991～1993 on the effects of a new plant growth regulator Shibide (SBD) on the yields of 4 grain crops, 5 vegetables and 2 cash crops. It also reports the effect of this product on plant growth vigors such as plant height, leaf width and diameter of plant stem.

Straw return increases crop production by improving soil organic carbon sequestration and soil aggregation in a long-term wheat-cotton cropping system

Straw return is a promising strategy for managing soil organic carbon(SOC)and improving yield stability.However,the optimal straw return strategy for sustainable crop production in the wheat(Triticum aestivum L.)-cotton(Gossypium hirsutum L.)cropping system remains uncertain.The objective of this study was to quantify the long-term(10 years)impact of carbon(C)input on SOC sequestration,soil aggregation and crop yields in a wheat-cotton cropping system in the Yangtze River Valley,China.Five treatments were arranged with a single-factor randomized design as follows:no straw return(Control),return of wheat straw only(Wt),return of cotton straw only(Ct),return of 50%wheat and 50%cotton straw(Wh-Ch)and return of 100%wheat and 100%cotton straw(Wt-Ct).In comparison to the Control,the SOC content increased by 8.4 to 20.2%under straw return.A significant linear positive correlation between SOC sequestration and C input(1.42-7.19 Mg ha^(−1)yr^(−1))(P<0.05)was detected.The percentages of aggregates of sizes>2 and 1-2 mm at the 0-20 cm soil depth were also significantly elevated under straw return,with the greatest increase of the aggregate stability in the Wt-Ct treatment(28.1%).The average wheat yields increased by 12.4-36.0%and cotton yields increased by 29.4-73.7%,and significantly linear positive correlations were also detected between C input and the yields of wheat and cotton.The average sustainable yield index(SYI)reached a maximum value of 0.69 when the C input was 7.08 Mg ha^(−1)yr^(−1),which was close to the maximum value(SYI of 0.69,C input of 7.19 Mg ha^(−1)yr^(-1))in the Wt-Ct treatment.Overall,the return of both wheat and cotton straw was the best strategy for improving SOC sequestration,soil aggregation,yields and their sustainability in the wheat-cotton rotation system.

Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models

Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.

Analyzing the Impact of Feature Selection on Crop Yield Prediction

In agriculture sector, machine learning has been widely used by researchers for crop yield prediction. However, it is quite difficult to identify the most critical features from a dataset. Feature selection techniques allow us to remove the extraneous and noisy features from the original feature set. The feature selection techniques help the model to focus only on the important features of the data, thus reducing execution time and improving efficiency of the model. The aim of this study is to determine relevant subset features for achieving high predictive performance by using different feature selection techniques like Filter methods, Wrapper methods and embedded methods. In this work, different feature selection techniques like Rank-based feature selection technique, weighted feature selection technique and Hybrid Feature Selection Technique have been applied to the agricultural data. The optimal feature set returned by different feature selection techniques is used for yield prediction using Linear regression, Random Forest, and Decision Tree Regressor. The accuracy of prediction obtained using the above three methods has been analyzed by using different evaluation parameters. This study helps in increasing predictive accuracy with the minimum number of features.

Climate variability and crop yields synergies in Tanzania’s semiarid agroecological zone

smallholders in most of Sub-Saharan Africa.These impacts have been more enormous to crop production and other attached livelihoods.However,the comprehensive assessment of these impacts has suffered numerous challenges because crop productivity is also susceptible to other factors involved in the production process.This study aimed to understand how crop yields are affected by climate change in the semiarid zone of Tanzania.The findings would establish a thorough literature within smallholder adaptation in the area.Furthermore,they will intensify strategies to cope with reduced yields attributed by climate-change impacts.Outcomes:There has been a dramatic decrease in rainfall(R^(2)=0.21)and increase in temperature(R^(2)=0.30).In addition,we found that rainfall and temperature variability had positive(R^(2)~0.5)and negative(R^(2)~0.3)correlations with crop yields,respectively.Discussion:The decline in yields at both local and national levels elevated the magnitude of food shortage and poverty.The increasing climate impacts necessitate undertakings of various studies to plan,design,recommend,and implement various useful adaptation measures,especially in the vulnerable communities.Conclusion:To limit climate effects,we need to increase investments in adaptation and mitigation measures.

A review on statistical models for identifying climate contributions to crop yields

Statistical models using historical data on crop yields and weather to calibrate rela- tively simple regression equations have been widely and extensively applied in previous studies, and have provided a common alternative to process-based models, which require extensive input data on cultivar, management, and soil conditions. However, very few studies had been conducted to review systematically the previous statistical models for indentifying climate contributions to crop yields. This paper introduces three main statistical methods, i.e., time-series model, cross-section model and panel model, which have been used to identify such issues in the field of agrometeorology. Generally, research spatial scale could be categorized into two types using statistical models, including site scale and regional scale （e.g. global scale, national scale, provincial scale and county scale）. Four issues exist in identifying response sensitivity of crop yields to climate change by statistical models. The issues include the extent of spatial and temporal scale, non-climatic trend removal, colinearity existing in climate variables and non-consideration of adaptations. Respective resolutions for the above four issues have been put forward in the section of perspective on the future of statistical models finally.

Using SWAT to simulate crop yields and salinity levels in the North Fork River Basin,USA

Crop yields and salinity levels in the North Fork of the Red River(North Fork River)basin,located in southwestern Oklahoma and the Texas Panhandle,were analyzed based on the diverse climate in the region.Saline irrigation water is a major problem in the basin.The Elm Fork Creek flows through salt deposits,making the creek and its receiving stream,the North Fork River,too saline to use for irrigation.This greatly reduces the number of hectares that can be utilized for agricultural crops within the basin.A baseline SWAT model was setup,calibrated and validated to simulate streamflow and wheat and cotton yields.The SWAT model and a regression equation were used to analyze variable weather impacts on crop yields and salinity levels.Using the weather generator WXGEN and 58 years of observed weather data,ten 50-year weather datasets were generated.Output from the weather generator was input into the calibrated SWAT model to simulate wheat and dryland and irrigated cotton yields for the ten weather scenarios.Using an empirical relationship between ionic strength and streamflow,salinity levels were estimated.Though the crop yields varied greatly from year to year,the yields were not significantly different over the 50-year simulation period.The electrical conductivity(EC,expressed in decisiemens per meter or dS/m)at the US Geological Survey gage station just downstream of the salt deposits was significantly different with levels ranging from 40 to 65 dS/m.Though the water in the Elm Fork is much too saline to use for irrigation,the water in the North Fork River may be used as long as the flow rates in the river are greater than 0.60 m3/s.In order to optimize the available cropland,a salinity control must be installed upstream of the salt deposits on the Elm Fork Creek.

Distribution of Surface Ozone Concentration in the Clean Areas of China and Its Possible Impact on Crop Yields

From August 1994 to July 1995, ozone and its precursors were measured in the clean areas of China. The results show that in the period of crop growth, hourly mean ozone concentration, ozone concentration averaged in seven hours of daytime and accumulated ozone concentration in long period have approached or overpassed the harmful level in environmental and health standard of U. S. A. and Canada.

Ensemble learning prediction of soybean yields in China based on meteorological data

The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 years.Three effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.

Effects of residual plastic film on crop yield and soil fertility in a dryland farming system

Plastic film mulch in agricultural production becomes essential to maintaining crop yields in arid and semiarid areas.However,the presence of residual film in farmland soil has also drawn much attention.In this study,three experiments were conducted.The first two experimental designs included 0,450,1350,and 2700 kg ha^(-1) of residual film pieces of approximately 5 cm side length added to field soil(0-20 cm soil depth)for seven years and added to pots for four years.In the third experiment,1350 kg ha^(-1)of the residual film with different side lengths(2-5,5-10,10-15,and 15-20 cm)was added to field soil for six years to explore the effect of residual film fragment size on soil nutrients,soil microorganisms,crop growth and yields.The residual film had little effect on the soil moisture at a field depth of 0-2(or 0-1.8)m.There were no significant effects on organic carbon,total nitrogen,inorganic nitrogen,total phosphorus or available phosphorus in the 0-20 cm soil layer.The presence of residual film decreased the richness and diversity of the bacterial community of the surface soil of the residual film,but it had no significant effect on the microbial community of the non-surface soil.The emergence rates of wheat and lentils occasionally decreased significantly with different amounts of residue fragments added to the field.At 450-2700 kg ha^(-1),the residual film reduced the plant height and stem diameter of maize and significantly reduced the shoot biomass of harvested maize by 11-19%.The average yields of maize and potato over the seven years decreased,but there were almost no significant statistical differences among the treatments.These results provide important data for a comprehensive scientific understanding of the effects of residual film on soil and crops in dryland farming systems.

In vitro and in silico studies of salicylic acid on systemic induced resistance against bacterial leaf blight disease and enhancement of crop yield

Salicylic acid(SA)is an effective elicitor to promote plant defenses and growth.This study aimed to investigate rice(Oryza sativa L.)cv.Khao Dawk Mali 105 treated with salicylic acid(SA)-Ricemate as an enhanced plant protection mechanism against bacterial leaf blight(BLB)disease caused by Xanthomonas oryzae pv.oryzae(Xoo).Results indicated that the use of SA-Ricemate as a foliar spray at concentrations of more than 100 mg L^(-1)can reduce the severity of BLB disease by 71%.SA-Ricemate treatment also increased the hydrogen peroxide(H_(2)O_(2))content of rice leaf tissues over untreated samples by 39–61%.Malondialdehyde(MDA)in rice leaves treated with SA-Ricemate also showed an increase of 50–65%when comparing to non-treated samples.The differential development of these defense compounds was faster and distinct when the SA-Ricemate-treated rice was infected with Xoo,indicating plant-induced resistance.Besides,SA-Ricemate elicitor at a concentration of 50–250 mg L^(-1)was correlated with a substantial increase in the accumulation of total chlorophyll content at 2.53–2.73 mg g^(-1)of fresh weight which suggests that plant growth is activated by SA-Ricemate.The catalase-and aldehyde dehydrogenase-binding sites were searched for using the CASTp server,and the findings were compared to the template.Chemsketch was used to design and optimize SA,which was then docked to the catalase and aldehyde dehydrogenase-binding domains of the enzymes using the GOLD 3.0.1 Software.SA is shown in several docked conformations with the enzymes catalase and aldehyde dehydrogenase.All three catalase amino acids(GLN7,VAL27,and GLU38)were discovered to be involved in the creation of a strong hydrogen bond with SA when SA was present.In this mechanism,the aldehyde dehydrogenase amino acids LYS5,HIS6,and ASP2 were all implicated,and these amino acids created strong hydrogen bonds with SA.In field conditions,SA-Ricemate significantly reduced disease severity by 78%and the total grain yield was significantly increased which was an increase of plant height,tiller per hill,and panicle in three field trials during Aug–Nov 2017 and 2018.Therefore,SA-Ricemate can be used as an alternative elicitor on replacing harmful pesticides to control BLB disease with a high potential of increasing rice defenses,growth,and yield components.

AI Method for Improving Crop Yield Prediction Accuracy Using ANN

Crop Yield Prediction(CYP)is critical to world food production.Food safety is a top priority for policymakers.They rely on reliable CYP to make import and export decisions that must be fulfilled before launching an agricultural business.Crop Yield(CY)is a complex variable influenced by multiple factors,including genotype,environment,and their interactions.CYP is a significant agrarian issue.However,CYP is the main task due to many composite factors,such as climatic conditions and soil characteristics.Machine Learning(ML)is a powerful tool for supporting CYP decisions,including decision support on which crops to grow in a specific season.Generally,Artificial Neural Networks(ANN)are usually used to predict the behaviour of complex non-linear models.As a result,this research paper attempts to determine the correlations between climatic variables,soil nutrients,and CYwith the available data.InANN,threemethods,Levenberg-Marquardt(LM),Bayesian regularisation(BR),and scaled conjugate gradient(SCG),are used to train the neural network(NN)model and then compared to determine prediction accuracy.The performance measures of the training,as declared above,such as Mean Squared Error(MSE)and correlation coefficient(R),were determined to assess the ANN models that had been built.The experimental study proves that LM training algorithms are better,while BR and SCG have minimal performance.

An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine learning(ML)models effectively deal with such challenges.This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024.In addition,it analyses the effectiveness of various input parameters considered in crop yield prediction models.We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield.The total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is 125.We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers.Each study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel performance.We also discuss the ethical and social impacts of AI on agriculture.However,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven models.Therefore,thorough research is required to deal with challenges in predicting agricultural output.

Combined Application of Biostimulants and EDTA Improved Wheat Productivity under Cadmium Stress

Wheat(Triticum aestivum L.)exhibits a greater capacity for cadmium(Cd)absorption compared to other cereal crops,leading to elevated daily Cd intake,and posing a significant threat to public health.For the mitigation of Cd stress in sustainable and environmentally friendly way,a pot study was designed by using exogenous application of various biostimulants,i.e.,Nigella sativa and Ocimum sanctum extracts:0%,10%,and 20%in combination with the chelating agent ethylenediaminetetraacetic acid(EDTA)using 0 and 5 mg kg^(-1) under various levels of Cd stress(i.e.,0,5,10,and 15 mg kg^(-1) soil).Results revealed that Cd stress significantly reduced the seed emergence,growth,root,and allometric characters and yield-related parameters of wheat crops.The most observable reduc-tion was documented in wheat plants exposed to a higher Cd concentration(15 mg kg^(-1)),followed by the lower Cd level(control).The combined application of bio-stimulants and EDTA minimized the negative impacts of Cd stress.The highest increase in seedling emergence(5.44%),leaf area(50.60%),number of tillers(31.02%),grain yield per plant(24.28%),biological yield(13.97%),and decrease in Cd levels in grains(40%)was noticed where 20%foliar application of N.sativa and 10%of O.sanctum biostimulants were done using 5 mg kg^(-1) of soil-applied EDTA.This intervention demonstrated a notable reduction in Cd-induced negative effects,highlighting the potential of these substances in promoting sustainable wheat cultivation in contaminated environments.Moreover,it is an eco-friendly and approachable method at thefield level able to ensure food safety.

Various Organic Nutrient Sources in Combinations with Inorganic Fertilizers Influence the Yield and Quality of Sweet Corn(Zea mays L.saccharata)in New Alluvial Soils of West Bengal,India

Nutrient management plays a crucial role in the yield and quality of sweet corn.A field experiment was conducted in consecutive two kharif seasons in 2018 and 2019 to investigate the effect of various organic sources of nutrients in combination with inorganic sources on the yield and quality of sweet corn under new alluvial soils of West Bengal,India.Treatments were:T_(1):Control(without fertilizers);T_(2):100%recommended dose(RDF)of chemical fertilizers(CF)(RDF CF_(100%));T_(3):100%recommended dose of N(RDN)through vermicompost(VC)(RDN VC_(100%));T_(4):50 RDN through CF+50%RDN through VC(RDN CF_(50%)+RDN VC50%);T_(5):50%RDF through CF+50%RDN through organic source(OS)1,Soligro(Ascophyllum nodosum)granular(RDN CF_(50%)+RDN OS_(150%));T6:50%RDN through CF+50%RDN through OS 2,Bioenzyme(liquid)(RDN CF_(50%)+RDN OS250%);T7:50%RDN through CF+50%RDN through OS 3,Opteine(Ascophyllum nodosum)filtrate[RDN CF_(50%)+RDN OS350%];T8:50%RDN through VC+50%RDF through OS 1,Soligro(Ascophyllum nodosum)granular(RDN VC50%+RDN OS_(150%)).The OS of fertilizers were VC,SoliGro Gr(OS1)(Ascophyllum nodosum),Bioenzyme liquid(OS2),and Opteine(Ascophyllum nodosum)filtrate(OS3).The inorganic source was traditional CF applied at the RDF(150:75:75 kg ha^(−1) of N:P2O5:K2O).The VC was used to supply 100%RDN as one source or 50%RDN when combined with CF or OS.Maximum fruit yield(10.75 and 10.79 t ha^(−1) in 2018 and 2019,respectively)was recorded when RDF was substituted through CF only,being statistically at par with 50%CF+50%VC on a nitrogen equivalent basis(9.92 and 10.00 t ha^(−1) in 2018 and 2019,respectively)and 100%VC(8.22 and 8.32 t ha^(−1) in 2018 and 2019,respectively).Compared to chemical sources of nutrients,VC-based treatments produced a larger percentage of large-size cob(>25 cm).The 100%VC increased antioxidant(8.35 and 8.45 mg g^(−1)),carotenoid(0.59 and 0.61 mg/100 g),and phenol(55.06 and 55.02 mg 100 g^(−1))content compared with its 50%dose in combination with other sources.The study revealed the potentiality of organic sources towards achieving improved cob quality of sweet corn.