Rice Crop Yield Research Articles

Evaluating rice crop phenology and crop yield in hilly region using satellite imagery and Google Earth Engine

Monitoring vegetation phenology is essential for understanding the impacts of climate change on agricultural production. This study leverages Sentinel-2 data to develop an algorithm in Google Earth Engine (GEE) for calculating phenological metrics of rice crop cultivated over the hilly area, allowing for high-resolution, efficient, and large-scale analysis without the need for data download. The study focuses on key metrics, including the start of the season and end of the season , length of growing season derived from various vegetation indices. The results demonstrate that NDVI-based phenological metrics closely align with the observed values at the experimental site, Malan. Moreover, the relationship of NDVI based length of growing season with the rice crop yield was found stronger with a R2 value of 0.68, depicting the capability of the satellite-based phenology metrics to estimate the rice crop yield in hilly region of Himachal Pradesh.

TIME SERIES, MACHINE LEARNING AND ENSEMBLE MODELS TO FORECAST RICE CROP YIELD IN NIGERIA UNDER CLIMATIC VARIABILITY

The rising demand for food globally due to unprecedented population growth has led to food insecurity in some populated regions, such as Africa. This leads to global agricultural production to be of increasing concern. The growing requirement for food in an era of a changing climate and scarce resources has inspired substantial crop yield prediction research. Another contributing factor to global food insecurity is climate change and its variability. The impact of increasing climate variability on rice crop yield is now evident. Predicting the potential effects of climate change on rice crops prompts the use of time series, machine learning, and ensemble models. The ensemble model was designed by assigning weights to the individual models based on their performance metrics, such as R-squared values and error rates. Results showed that the ensemble model achieved superior accuracy, with an R-squared value of 0.9523, surpassing the performance of the individual SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous Regressors) and LSTM (Long Short-Term Memory)models. The predicted yields aligned closely with observed values in both training and testing phases, demonstrating the model's ability to capture historical trends and generalise effectively to unseen data. This study shows the importance of integrating time-series forecasting and machine learning techniques to improve rice crop yield predictions.

Effect of mutation of secG gene in drug resistance and physiological and biochemical activities of Xanthomonas oryzae pv. oryzae.

Bacterial leaf blight caused by the bacterium Xanthomonas oryzae pv. oryzae has a substantial effect on the yield of rice crops. The secretory (Sec) pathway, essential for efflux transport in bacteria, remains insufficiently studied in X. oryzae pv. oryzae, especially regarding its roles in drug resistance and physiology. This study involved transcriptome analysis on two X. oryzae pv. oryzae strains: a secG deletion strain (∆secG) and its complemented strain (C: ∆secG). In comparison to the parental strain PXO99A, ∆secG exhibited slower growth, with reductions in swimming (20.67%) and swarming (12.59%), while maintaining 76.7% of its biofilm formation capacity and 63.6% of exopolysaccharide production. The minimum inhibitory concentration (MIC50) values for an n-butanol extract of Bacillus velezensis HN-2 (HN-2E) and bacitracin against ∆secG were 0.426 μg/mL (5.3% lower than that of PXO99A) and 10.905 μg/mL, respectively. Notably, ∆secG exhibited increased susceptibility to hydrogen peroxide (H2O2), being inhibited at 0.25 mm compared to 0.3 mm for PXO99A and C: ∆secG. In the presence of 0.2 mM H2O2, the susceptibility of ∆secG to HN-2E increased by 31.22% (MIC50 = 0.159 μg/mL), while PXO99A and C: ∆secG exhibited MIC50 values of 0.280 and 0.291 μg/mL, respectively. Our findings demonstrate that Bacillus-induced H2O2 production enhances the sensitivity of X. oryzae pv. oryzae to biocontrol agents, providing valuable insights for the prevention of bacterial leaf blight. These results highlight the significance of the Sec pathway in the behavior and resistance of X. oryzae pv. oryzae, as well as potential areas for further research on plant diseases. © 2024 Society of Chemical Industry.

Impacts of land use changes on water conservation in the Songhuajiang River basin in Northeast China using the SWAT model

The Songhuajiang River basin (SRB) in Northeast China underwent obvious changes in land use in recent years, which might lead to intense changes in basin hydrology and water conservation. Therefore, to evaluate the effects of land use changes on the hydrological cycle and associated water conservation in the SRB, a calibrated Soil and Water Assessment Tool (SWAT) model was used. The model auto-calibration was conducted using the SWAT-CUP (SWAT Calibration and Uncertainty Procedures) tool. Results revealed that calibration of three crop yields of corn (Zea mays L.), rice (Oryza sativa L.), and soybean (Glycine max L.) during the study period (2001–2018) had the Percent BIAS (PBIAS) values of −8.7 %∼3.0 %, 6.2 %∼19.7 %, and −8.7 %∼7.2 %, respectively in 15 zones. Good agreements were obtained for simulated results of streamflow in comparison with measured data from 12 hydrological stations, in which the overall Nash-Sutcliffe Efficiency (NSE) values for the calibration (2009–2012) and validation (2013–2015) periods were 0.60 and 0.64, respectively, and the overall PBIAS values were 7.7 % and 15.4 %. For the land use changes, the hydrological cycle of corn to rice (all hydrological variables change by more than 18 %) was more complex and intense than that of corn to soybean (all hydrological variables change not exceed 5 %). According to the simulation results of water conservation, this study found that the implementation of the policy-driven replacement of corn with soybean in the SRB was feasible. As for the economy-driven conversion from corn to rice, it is necessary to consider the substantial impacts on the hydrological cycle and the decline in water conservation. Based on simulation results, it is not recommended that producers in the Western SRB promote corn to rice conversion.

Kharif rice crop acreage and yield estimation using microwave and optical remote sensing time series satellite data: A case study of the eastern region of Maharashtra

Aim of the studyThe study aims to assess the feasibility of using remote sensing to estimate crop area and yield in a major rice-growing region of Maharashtra.Material and methodsRecent advancements in remote sensing technology, including improvements in image resolution and availability, allowed for timely data collection. The study employs a random forest classifier to identify rice crop using sentinel-1A SAR temporal backscatter satellite images. Additionally, a semi-physical method that incorporates remote sensing and physiological concepts such as Photo-synthetically Active Radiation and a fraction of PAR absorbed by the crop is used to estimate crop yield.Results and conclusionsNet Primary Product was calculated using the Monteith model. The calculation of Kharif rice yield involved considering the actual NPP, Radiation use efficiency, and Harvest index. The present study was conducted throughout two kharif seasons, 2020 and 2021. Although there are minor differences in kharif rice area and yield estimations, the model is still applicable in other significant kharif rice-growing regions of India.

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

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

Multiseasonal analysis of rice crop yield prediction with Sentinel-2 time series and UAV imagery in Lambayeque (Peru)

Multiseasonal analysis of rice crop yield prediction with Sentinel-2 time series and UAV imagery in Lambayeque (Peru)

Evaluation of Genetic Variability for Yield and Associated Traits in Breeding Lines Derived from the Cross RNR-15048 × Tunga

Aims: To evaluate the genetic variability of 45 rice breeding lines for 12 quantitative and 2 qualitative traits, aiming to identify and enhance traits that contribute to improved crop yield and nutritional quality in rice. Study Design: The experiment was carried out in Randomised Complete Block Design with two replications. Place and Duration of Study: During kharif 2023 at Agricultural and Horticultural Research Station, Kathalagere, Davangere. Methodology: The research utilized 45 breeding lines resulting from the cross RNR15048 × Tunga, sourced from the Department of Genetics and Plant Breeding, College of Agriculture Shimoga, along with 5 checks. Data was recorded and analyzed statistically using R software. Results: The significant differences among the breeding lines identified through the analysis of variance highlight the potential for selecting superior genotypes within the studied rice breeding lines. High to moderate values of the genotypic coefficient of variation (GCV) and phenotypic coefficient of variation (PCV) suggest that there is substantial genetic diversity for the traits evaluated, indicating a promising opportunity for genetic improvement through selective breeding. Conclusion: The findings of this study highlight the importance of focusing yield improvement programs on specific traits, including plant height, number of tillers per plant, productive tillers per plant, grains per panicle, filled grains per panicle, test weight, protein content, and overall grain yield. Emphasizing these traits is essential for enhancing the productivity and nutritional value of rice, ultimately supporting food security efforts.

Cavalcade Legume (Centrosema pascuorum) Used as Soil Amendment in RD41 Rice Fields: Short-term Effects on the Soil Nematode Community, Soil Properties, and Yield Components

Numerous phytoparasitic nematodes have been identified in Thailand’s paddy fields, which routinely cause substantial reductions in rice crop yields. However, effective strategies for their management have yet to be documented. In this study, cavalcade legume was used as a soil amendment in RD41 rice fields to examine its effects on the soil nematode community, soil properties, and yield components compared to untreated control plots. The results demonstrated that the population densities of plant-parasitic nematodes (PPNs) in the order Tylenchida, primarily Hirschmanniella sp., significantly decreased in cavalcade-treated plots across all soil sampling periods. Moreover, there was an increase in the populations of beneficial nematodes within the orders Dorylaimida and Araeolaimida. In contrast, greater PPN populations were observed in the control plots compared to the initial nematode population. In addition to reducing PPN populations, this legume showed other benefits, specifically increased soil properties (available P) and rice plant growth (plant height and number of tillers). While there was no statistically significant difference in soil organic matter (SOM) content, the application of this legume tended to increase SOM content, in contrast to a decrease in SOM content observed in the control plots. Overall, this study provides valuable insights into the substantial advantages of using cavalcade legumes in RD41 rice fields.

Estimating Total Rice Biomass and Crop Yield at Field Scale Using PlanetScope Imagery Through Hybrid Machine Learning Models

Near real-time crop monitoring has been a challenging due to the lack of high-resolution remote sensing images suitable for agricultural applications. The PlanetScope constellation, comprising approximately 130 Dove satellites, collects images of the entire Earth daily, with a resolution of 3.7 m. The high-resolution images from the PlanetScope satellite, along with vegetation indices, geo-environmental data, and soil and crop parameters, were utilized and analysed using machine learning models to enhance the accuracy of predicting total biomass and rice crop yield at the field scale. The study area, covering nearly 214 sample rice plots, was located in the Tarekswar block of Hooghly, West Bengal, India. Alongside ten vegetation indices and three Principal Component Analysis (PCA) soil nutrient levels, approximately thirty-six factors were analyzed to predict rice total biomass and crop yield using ten machine learning (ML) models, namely Random forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), Bagging Tree (Treebag), Generalized Additive Models (gamSpline), Elastic Net (enet), Ordinary regression with LASSO penalty (rqlasso), Tree Models from Genetic Algorithm (evtree), Bayesian Regularized Neutral Networks (brnn), cubist models, and there hybrid of ensembles. Boruta and multi-collinearity analysis were also conducted for the selected factors to explore their influence levels. The study area exhibited robust rice yields ranging from 5 to 10 t/ha, accompanied by healthy biomass growth. Four ML models ─cubist, random forest, enet, and the hybrid model—showed promising predictions with R2 > 0.88. Most models classified less than 20 ha of the study area as falling into the “very-low suitable class”, showing the region’s suitability for rice cultivation due to its highly fertile alluvial soil. Boruta sensitive analysis revealed that nearly 24 individual factors significantly influenced the final crop yield including, organic carbon (OC), phosphorus (P), electrical conductivity (EC), mechanization level, and the majority of the vegetation indices. A critical analysis carried out through the Map query tool showed that five vegetation indices estimated via PlanetScope displayed strong correlations (exceeding 89%) in identifying areas with high to very high rice yields. The study can serve as a guideline for near-real-time crop monitoring in the near future, using high-resolution PlanetScope images.

Effect of Crop Establishment Method, Residue and Nutrient Management on Productivity of Rice-Wheat Cropping System at Rupandehi, Bhairahawa, Nepal

The puddling operation and multiple tillage operations done in traditional rice-wheat system have negative impact on soil resulting into low productivity of rice - wheat system. A field experiment was conducted to find the alternate practices for enhancing the productivity of rice-wheat system at National Wheat Research Program, Bhairahawa during 2018/19. Three crop establishment methods: surface seeded wheat (SSW) followed by (fb) unpuddled transplanted rice (U-TPR), zero tilled wheat (ZTW) fb zero tilled direct seeded rice (ZT-DSR) and conventionally tilled wheat (CTW) fb puddled transplanted rice (PTR) were tested in two levels of residue management: residue removed (R0) and residue retention (R50). Three levels of nutrient management: recommended dose of NPK (F100), 25% higher dose of NPK (F125) and farmer’s practice (FP) were assigned in wheat. Strip-split plot design with 3 replications was used in wheat while strip-plot design with 9 replications was used in rice. Crop establishment methods (CSM) were assigned in vertical strips; residue management in horizontal blocks and nutrient management were assigned in subplots. The number of tillers at maximum tillering stage, maximum leaf area index, effective tiller per square meter and number of grains per spike of wheat were significantly affected by CSM, where ZTW had shown better results (413.7 tillers, 2.12 LAI, 224.1 effective tiller per square meter and 34.27 grains per spike) than SSW and CTW but the CSM had no significant effect on grain yield of both rice and wheat crop. R0 had produced significantly higher straw yield than R50 of both rice and wheat, whereas, R50 had produced significantly higher harvest index than R0 in rice. The application of 25% more nutrients than the recommended dose resulted in the significantly better growth, yield attributes, and yield of wheat. The ZTW fb ZT-DSR had produced significantly higher system yield than SSW fb U-TPR where, CTW fb PTR was statistically at par with both systems. The recommended dose of nutrients (F100) and 25% higher than F100 (F125) had produced significantly higher system yield than farmer’s practice (FP) dose. Research results revealed that ZTW fb ZT-DSR system can be suggested instead of traditional CTW fb PTR system without significant yield differences.

Impact of plant-based soil amendments on improvement of mortgaged Farmland

A field experiment was conducted in Maoming, Guangdong, China to evaluate the effects of different plant-based soil amendments on soil quality, nutrient availability, plant growth, and land value. The application of plant-based soil amendments significantly increased the plant height, stem diameter, and biomass yield of corn, soybean, and rice crops. The greatest improvement was observed in corn crops, with a 12.6% increase in plant height, a 15% increase in stem diameter, and a 25.6% increase in biomass yield. Plant based soil amendments significantly improve soil quality, increase nutrient availability, and promote plant growth and productivity. Economic analysis shows that the use of plant-based soil amendments significantly increases the value of land, providing potential benefits for mortgage lenders and borrowers. This study emphasizes the importance of sustainable land use practices and the potential of plant-based soil improvement as an effective means of increasing land value. Bangladesh J. Bot. 53(3): 823-833, 2024 (September) Special

Phenotyping of Panicle Number and Shape in Rice Breeding Materials Based on Unmanned Aerial Vehicle Imagery.

The number of panicles per unit area (PNpA) is one of the key factors contributing to the grain yield of rice crops. Accurate PNpA quantification is vital for breeding high-yield rice cultivars. Previous studies were based on proximal sensing with fixed observation platforms or unmanned aerial vehicles (UAVs). The near-canopy images produced in these studies suffer from inefficiency and complex image processing pipelines that require manual image cropping and annotation. This study aims to develop an automated, high-throughput UAV imagery-based approach for field plot segmentation and panicle number quantification, along with a novel classification method for different panicle types, enhancing PNpA quantification at the plot level. RGB images of the rice canopy were efficiently captured at an altitude of 15 m, followed by image stitching and plot boundary recognition via a mask region-based convolutional neural network (Mask R-CNN). The images were then segmented into plot-scale subgraphs, which were categorized into 3 growth stages. The panicle vision transformer (Panicle-ViT), which integrates a multipath vision transformer and replaces the Mask R-CNN backbone, accurately detects panicles. Additionally, the Res2Net50 architecture classified panicle types with 4 angles of 0°, 15°, 45°, and 90°. The results confirm that the performance of Plot-Seg is comparable to that of manual segmentation. Panicle-ViT outperforms the traditional Mask R-CNN across all the datasets, with the average precision at 50% intersection over union (AP50) improved by 3.5% to 20.5%. The PNpA quantification for the full dataset achieved superior performance, with a coefficient of determination (R 2) of 0.73 and a root mean square error (RMSE) of 28.3, and the overall panicle classification accuracy reached 94.8%. The proposed approach enhances operational efficiency and automates the process from plot cropping to PNpA prediction, which is promising for accelerating the selection of desired traits in rice breeding.

Yield Loss Quantification Due to Stalk-Eyed Fly (Diopsidae) Infestations on Rice Crops (Oryza Sativa) in Fogera Plain

Stalk-Eyed Flies (Diopsidae) pose a significant threat to rice crops (Oryza sativa), causing yield loss and affecting crop development. This study aims to quantify yield loss due to Stalk-Eyed Fly infestations and identify susceptible growth stages of rice crops to this insect. Field experiments were conducted to assess the yield loss of rice due to stalk eyed fly at Fogera, in 2021 and 2022. With the treatments Both Thiamethoxam seed treatment and Chlorpyrifos systemic foliar insecticide were applied to combat the infestation, with untreated varieties serving as controls. And the pot experiment was conducted to assess susceptibility of rice growth stage to the stalk eye fly artistically analyses, were employed to evaluate treatment impacts. With the treatments of different crop stages, Seedling Stage, Tillering Stage, Stem Elongation Stage, Panicle Initiation Stage, Booting, Heading, Flowering. The effect of rice growth stage on stalk eyed fly infestation showed that there is statistically significant difference among the different growth stage, tillering stage appears to be the most susceptible to Stalk-Eyed Fly infestations in terms of the impact on tiller growth in rice crops. During this stage, the mean percentage change of tillers is the highest at 59.3%, indicating a substantial impact of infestations on tiller growth. This suggests that rice crops are particularly vulnerable to stalked fly infestations during the tillering stage, highlighting the importance of effective pest management strategies during this critical growth phase to mitigate potential yield losses. In the field experiment, significant variations in yield loss were observed across treatments. The presence of dead hearts, indicative of infestation, varied notably between treated and untreated rice. Rice varieties incorporating pesticide applications demonstrated lower dead heart percentages, suggesting there is rice yield loss if the stalk-eyed fly is not controlled. Notably, treatments influenced grain yield, with insect protection measures resulting in higher yields and lower yield loss compared to controls. In conclusion, stalk-eyed fly infestation impacts rice crop productivity, particularly during vulnerable growth stages. Pesticide applications show promise in reducing infestation severity and mitigating yield loss. We recommend implementing targeted treatment strategies, focusing on susceptible growth stages, to effectively manage infestations and optimize rice crop yields.

Optimized Tillage Method Increased Rice Yield in Rice Ratooning System

Ratoon rice occupies an important position in rice production owing to its time-saving, labor-saving and low-pollution planting, and increased benefits. However, the impact of tillage management on the yield in rice ratooning system has not yet been reported. Thus, field experiments were carried out to investigate the impact of seven tillage methods on the yield of ratoon rice crop in Jingzhou City in 2021–2022. The managements included winter plowing + rotary 2 times (PTw + RT2) or 3 times (PTw + RT3), spring plowing + rotary 2 times (PTs + RT2) or 3 times (PTs + RT3), no plowing + rotary 2 times (P0 + RT2) or 3 times (P0 + RT3) and no tillage (NT). PTw + RT3 had the highest total rice yield. The experimental data were collected in 2021 and 2022. In terms of main season rice yield, the order of ranking was PTw > PTs ≈ NT ≈ P0, while for ratoon rice yield, the ranking was NT > PTw ≈ PTs > P0. Generally, the root function ranked as PTw > PTs > P0 > NT. The photosynthetic capacity of the main season rice always maximized in PTw, those of the ratoon rice all maximized in NT, and those of both the main season rice and ratoon rice always minimized in P0. In the three tillage modes (PTw, PTs, P0), an additional rotary tillage did not affect the growth or yield of rice. PTw + 3RT was the highest yielding tillage management, but it is still necessary to explore other PTw + 3RT methods and more economical tillage management to increase the yield of ratoon rice.

Quantifying chlorophyll content index for efficient nitrogen management in rice (Oryza sativa L.)

Applying nitrogen fertilizer strategically enhances rice productivity in nitrogen-deficient soils. Splitting fertilizer doses across growth stages optimizes nutrient use efficiency. Monitoring chlorophyll levels correlates closely with leaf nitrogen content, crucial for assessing crop health. Non-destructive chlorophyll content meter techniques, such as the Chlorophyll Content Index (CCI), allow for precise nitrogen management adjustments in the field. However, its critical value may differ depending on crops and varieties. Hence, to find out the critical CCI value for rice cv. ADT 43, observational trials were conducted at the Experimental Farm of Annamalai University during two seasons Kuruvai (June-Oct 2023) and Navarai (Dec-March 2024). The pooled data of the present study indicated that the average LNC (maximum tillering, panicle initiation and booting stages) has a linear relationship (R2=0.8545) with the yield of rice crop. The corresponding higher yield ( greater than 7t/ha) coincided with LNC of 3.1%. Likewise, the average CCI has a linear relationship (R2=0.9031) with the LNC of rice plants. The corresponding LNC ( greater than 7t/ha) was matched with the CCI value of 29. This CCI value of 29 is considered as the critical CCI value. CCI can be used as a decision-support tool for N-fertilization of short duration rice crop, and when CCI reduced below this critical value the farmers can fertilise the field with adequate N.

Fine classification of rice fields in high-resolution remote sensing images

Fine-grained management of rice fields can enhance the yield and quality of rice crops. Challenges in achieving fine classification include interference from similar vegetation, the irregularity of natural field shapes, and complex scale variations. This paper introduces Rice Attention Cascade Network (RACNet), for the fine classification of rice fields in high-resolution satellite remote sensing imagery. The network employs the Hybrid Task Cascade network as the base framework and uses spectral and indices mixed multimodal data as input to reinforce the feature differentiation of similar vegetation. Initially, a Channel Attention Deformable-ResNet (CAD-ResNet) was designed to enhance the feature representation of rice on different channels. Deformable convolution improves the ability of CAD-ResNet to capture irregular field shapes. Then, to address the issue of complex scale changes, the multi-scale features extracted by the CAD-ResNet are progressively fused using an Asymptotic Feature Pyramid, reducing the loss of scale information between non-adjacent layers. Experiments on the Meishan rice dataset show that the proposed method is capable of accurate instance segmentation for fragmented or irregularly shaped rice fields. The evaluation metric AP50 of RACNet reaches 50.8%.

Performance Evaluation of Rice Variety MTU 1281 through on Farm Trial in West Godavari District of Andhra Pradesh, India

An on-farm trial was conducted by Krishi Vigyan Kendra, Undi in farmer’s fields of West Godavari district to evaluate the performance of rice variety MTU 1281 on yield and economics of Rice crop during the kharif season for two succeeding years 2022 and 2023. The results of the study revealed that MTU 1281 variety registered higher yield i.e., 7261, 7078 and 7170 kg/ha than MTU 7029 variety (6504, 6625 and 6565 kg/ha) during both the years of study and in pooled data as well. When compared to farmers' practice (Rs. 58,145 and Rs. 71,603/ha and 1.78:1, 1.98:1 during 2022–23, 2023–24, respectively) net returns and the B:C ratio were higher with MTU 1281 rice variety (Rs. 75,198 and Rs. 83,273/ha and 2.03:1, 2.16:1).

Nitrogen scavenging by winter fallow weeds and its availability to rice crops

Weedy fallow presents a promising strategy for nitrogen (N) scavenging and reducing N losses from croplands. The study aimed to (1) reassess the N scavenging potential of winter fallow weeds, specifically foxtail grasses, within rice cropping systems and (2) determine the availability of N scavenged by winter fallow weeds in rice paddies to rice crops. A two-year on-farm investigation (2022 and 2023), a one-year 15N-tracing microplot experiment (2021), and a two-year field experiment (2022 and 2023) were conducted in Hunan Province, China. The on-farm investigation revealed that winter fallow weeds scavenged on average 10.6 g N m–2 in 2022 and 10.9 g N m–2 in 2023. In the 15N-tracing microplot experiment, a 24 % average recovery efficiency of N from winter fallow weeds, applied in conjunction with two rice varieties and two urea-N application rates, was observed. The field experiment showed that total N uptake by rice plants and grain yield of rice crops were linearly and quadratically related to the total N application rate (the sum of N in applied winter fallow weeds and urea), respectively, in both 2022 and 2023. These findings highlight the potential of winter fallow weeds in rice paddies to scavenge N effectively and reveal this scavenged N to be highly available to rice crops. This study offers insights into the dual advantages of winter fallow weeds within rice cropping systems, shedding light on their environmental benefits as well as their positive agronomic outcomes.

Rice (Oryza sativa L.) yield evaluation comparing two soil preparation methods

Contextualization: Rice (Oryza sativa L.) cultivation, throughout its vegetative process, generates an increase in bulk density and penetration resistance in soil. The most representative component of production costs is management, even so, land preparation and adaptation reach, and exceed, 20% of the costs. Knowledge gap: As it is a high economic yield crop and little technified in the region, the conservation plows implementation that provide economic and environmental benefits to the community is sought. Purpose: With the aim of promoting a more sustainable production and agriculture, it was proposed to evaluate the soil’s physical properties and the rice crop yield, modifying the conventional preparation with plow to the chisel use. Methodology: The experimental area made up of one hectare was divided into experimental plots, T1: One pass with a vibratory chisel, and T2: Two passes with conventional tillage (plow), in which the soil’s physical properties were evaluated (Real Density, Apparent Density, Porosity and Texture) and the phenological follow-up to the crop development (Germination, Maximum Tillering, Maximum Engrossed, Maturity and Harvest). Soil tests were carried out before and after tillage between 15 and 20 cm depth. The statistical analysis was carried out by means of Analysis of Variance (ANOVA) and graphically, and the techno-economic analysis was carried out based on the production and operation costs comparing between the two types of tillage. Results and conclusions: The results showed that after soil preparation the bulk density decreased by 5,0% for T1 and 13,5% for T2; Likewise, these effects led to an increase in porosity of 13,6% and 29,34%, respectively, and production costs to be $1159,9 USD for T1 and 5,37% higher for T2. At the end, it is concluded that, when evaluating the yield at harvest, there were no significant differences in production because of machining, but a statistical difference was observed between the cost of conventional and reduced tillage, the latter being 148,9% cheaper and less aggressive for the soil.