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Rice is generally grown under completely flooded condition and providing food for more than half of the world’s population. Any changes in weather parameters might affect the rice productivity thereby impacting the food security of burgeoning population. So, the crop yield forecasting based on weather parameters will help farmers, policy makers and administrators to manage adversities. The present investigation examines the application of stepwise multiple linear regression (SMLR), artificial neural network (ANN) solely and in combination with principal components analysis (PCA) and penalised regression models (e.g. least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)) for rice yield prediction using long-term weather data. The R2 and root mean square error (RMSE) of the models varied between 0.22–0.98 and 24.02–607.29 kg ha−1, respectively during calibration. During validation with independent dataset, the RMSE and normalised root mean square error (nRMSE) ranged between 21.35–981.89 kg ha−1 and 0.98–36.7%, respectively. For evaluation of multiple models for multiple locations statistically, overall average ranks on the basis of R2 and RMSE of calibration; RMSE and nRMSE of validation were calculated and non-parametric Friedman test was applied to check the significant difference among the models. The ranking of the models revealed that LASSO (2.63) was the best performing model followed by ENET (3.07) while PCA-ANN (4.19) was the worst model which was found significant at p < 0.001. The reason behind good performance of LASSO and ENET is that these models prevent overfitting and reduce model complexity by penalising the magnitude of coefficients. Then, pairwise multiple comparison test was performed which indicated LASSO as the best model which was found similar to SMLR and ENET. So, for prediction of rice yield, these models can very well be utilised for west coast of India.

Rice grain yield prediction with UAV-driven multispectral images are re-emerging interests in precision agriculture, and an optimal sensing time is an important factor. The aims of this study were to (1) predict rice grain yield by using the estimated aboveground biomass (AGB) and leaf area index (LAI) from vegetation indices (VIs) and (2) determine the optimal sensing time in estimating AGB and LAI using VIs for grain yield prediction. An experimental trial was conducted in 2020 and 2021, involving two fertility conditions and five japonica rice cultivars (Aichinokaori, Asahi, Hatsushimo, Nakate Shinsenbon, and Nikomaru). Multi-temporal VIs were used to estimate AGB and LAI throughout the growth period with the extreme gradient boosting model and Gompertz model. The optimum time windows for predicting yield for each cultivar were determined using a single-day linear regression model. The results show that AGB and LAI could be estimated from VIs (R2: 0.56–0.83 and 0.57–0.73), and the optimum time window for UAV flights differed between cultivars, ranging from 4 to 31 days between the tillering stage and the initial heading stage. These findings help researchers to save resources and time for numerous UAV flights to predict rice grain yield.

The Mekong delta is Vietnam’s premier rice growing region, forming the livelihood basis for millions of farmers. At the same time, the region is facing various challenges, ranging from extreme weather events, saline water intrusion, and other anthropogenic pressures. This study examines how saline water intrusion and drought have affected rice yield in the Vietnamese Mekong Delta (VMD). Applying the Standardized Precipitation Index (SPI) and the maximum and minimum values of annual average salinity, we spatially examine the effects of drought and saline water intrusion on rice yields over a 40-year period (1980–2019). Our results highlight that 42% of the natural land area of the VMD has experienced increased drought occurrence during the winter-spring (WS) rice cropping season, while certain inland regions have additionally experienced increased drought occurrence during the summer-autumn (SA) rice cropping season. The Tri Ton Station, which has a significant Sen’s slope of −0.025 and a p-value of 0.05, represents an upstream semi-mountainous part of the delta, indicative of a rising severity of reoccurring drought. It should be noted that the yield decreases during the summer-autumn season as the positive SPI_SA increases. Salinity, on the other hand, is associated with SPI_WS during the winter-spring season. Our results highlight the need for improved evidence-based planning and investments in priority adaptation for both sustainable water infrastructure and to improve system resilience.

In this study assimilation of MODIS LAI (MOD15A2) into DSSAT-CERES-rice crop simulation model was used to develop advance yield estimates of rice crop during pre-harvest stage (F3) in Palakkad district of Kerala during Mundakan (September- January) season 2022-23 and 2023-24. The free parameters identified as inputs for the DSSAT-CERES-rice crop simulation model were adjusted and optimized sequentially during assimilation process until a minimum value of cost function is reached. This helped to minimize the deviation between MODIS- LAI and model generated LAI and the yield predicted by the model consequently is taken as the predicted yield. The average predicted yield during 2022-23 and 2023-24 was 5590 kgha-1 and 5124 kgha-1 respectively. The yield prediction by simulation model integrated with remote sensing products had higher accuracy than using simulation model alone during both the years with number of panchayats having the BIAS above ± 10 per cent reduced from 20 to 12 and 23 to 11 during 2022-23 and 2023-24 respectively. The findings clearly show that incorporating satellite data into crop simulation models can produce more accurate rice production forecasts than crop simulation techniques used alone.

As crop productivity is greatly influenced by weather conditions, many attempts have been made to estimate crop yields using meteorological data and have achieved great progress with the development of machine learning. However, most yield prediction models are developed based on observational data, and the utilization of climate model output in yield prediction has been addressed in very few studies. In this study, we estimate rice yields in South Korea using the meteorological variables provided by ERA5 reanalysis data (ERA-O) and its dynamically downscaled data (ERA-DS). After ERA-O and ERA-DS are validated against observations (OBS), two different machine learning models, Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), are trained with different combinations of eight meteorological variables (mean temperature, maximum temperature, minimum temperature, precipitation, diurnal temperature range, solar irradiance, mean wind speed, and relative humidity) obtained from OBS, ERA-O, and ERA-DS at weekly and monthly timescales from May to September. Regardless of the model type and the source of the input data, training a model with weekly datasets leads to better yield estimates compared to monthly datasets. LSTM generally outperforms SVM, especially when the model is trained with ERA-DS data at a weekly timescale. The best yield estimates are produced by the LSTM model trained with all eight variables at a weekly timescale. Altogether this study shows the significance of high spatial and temporal resolution of input meteorological data in yield prediction, which can also serve to substantiate the added value of dynamical downscaling.

Water and carbon footprint assessment can be a good indicator of sustainable agricultural production. The present research quantifies the potential impact of near-future (2026–2050) climate change on water footprint (WF) and carbon footprint (CF) of farm-level kharif rice production of three locally grown varieties (Khandagiri, Lalat, and Swarna) in Odisha, India, under the two RCP scenarios of 4.5 and 8.5. The crop yield, water resources utilization, and greenhouse gas (GHG) emissions were estimated using the calibrated and validated DSSAT crop simulation model. The precipitation and temperature estimates from three regional climate models (RCM), namely HadGEM3-RA, RegCM4, and YSU-RSM were downscaled using the quantile mapping method. The results revealed a considerably high increase in the total WF of the Khandagiri, Lalat, and Swarna rice varieties elevating up to 101.9%, 80.7%, and 71.8% respectively during the mid-century for RCP 4.5 scenario, and 67.3%, 66.6%, and 67.2% respectively for RCP 8.5 scenario relative to the baseline WF. Moreover, compared to the green WF, the blue WF was projected to increase significantly (~ 250–450%) in the future time scales. This could be attributed to increasing minimum temperature (~ 1.7 °C) and maximum temperature (~ 1.5 °C) and reduced precipitation during the rice-growing periods. Rice yield was projected to continually decline in the future period (2050) with respect to the baseline (1980–2015) by 18.8% and 20% under RCP 4.5 and 8.5 scenarios respectively. The maximum CF of Swarna, Lalat, and Khandagiri rice were estimated to be 3.2, 2.8, and 1.3 t CO 2 eq/t respectively under RCP 4.5 and 2.7, 2.4, and 1.3 t CO 2 eq/t respectively under RCP 8.5 scenario. Fertilizer application (40%) followed by irrigation-energy use (30%) and farmyard manure incorporation (26%) were the three major contributors to the CF of rice production. Subsequently, management of N-fertilizer dose was identified as the major mitigation hotspot, simultaneously reducing carbon footprint and grey water footprint in the crop production process.

Rational water and fertilizer management approaches and technologies could improve water use efficiency and fertilizer use efficiency in paddy rice cultivation. A promising water-conserving technology for paddy rice farming is the alternate wetting and drying irrigation system, established by the International Rice Research Institute. However, the strategy has still not been widely adopted, because water level measurement is challenging work and sometimes leads to a decrease in the rice yield. For the easy implementation of alternate wetting and drying among farmers, we analyzed a dataset obtained from a farmer’s water management study carried out over a three-year period with three cropping seasons at six locations (n = 82) in An Giang Province, Southern Vietnam. We observed a significant relationship between specific water level management and the rice yield and greenhouse gas emissions during different growth periods. The average water level during the crop period was an important factor in increasing the rice yield and reducing greenhouse gas emissions. The average water level at 2 days after nitrogen fertilization also showed a potential to increase the rice yield. The greenhouse gas emissions were reduced when the number of days of non-flooded soil use was increased by 1 day during the crop period. The results offer insights demonstrating that farmers’ implementation of multiple drainage during whole crop period and nitrogen fertilization period has the potential to contribute to both the rice yield increase and reduction in greenhouse gas emissions from rice cultivation.

Northeast Thailand is the largest rice cultivation region in Thailand, but the rice yield there is quite low. Soil salinity is one of the major yield restricted factors, is derived from underground rock salt, and is predicted to expand in the future. This study focused on evaluating rice productivity related to salinity conditions in Khon Kaen Province, Northeast Thailand. The field investigations were conducted from 2017 to 2019 in farmer fields in severe, moderate, and slight soil salinity classes determined by the Land Development Department of Thailand. The soil salinity on the basis of the electric conductivity of saturated soil extract (ECe) varied year to year, which seemed to be associated with precipitation. The difference in soil salinity between classes was obvious only in the drought year 2018, and reflected in the rice yield, although severe drought devastated rice yield in some fields. Plenty of rainfall may have alleviated soil salinity and rice yield reduction in other years, causing differences in rice yield that were not significant among soil salinity classes. However, salinity level evaluation by the USDA based on ECe showed that rice yield was damaged depending on the level. This study indicates that ECe-based evaluation is recommended for soil salinity in relation to rice productivity. The spatial and temporal evaluation for rice production may benefit farmers. The results in this study also showed rice production largely varied even in similar salinity levels, implying that salinity damage can be alleviated by farmer management.

Seasonal climate forecasts (SCFs) have gained popularity in agriculture for climate risk management studies.  The available forms of SCFs are not conducive to decision making because of a mismatch in scales over space and time. In this study, available SCFs were disaggregated using the FResampler1 technique to simulate rice yield (cultivar PR 114) under different nitrogen levels and planting dates using DSSAT (Decision Support System for Agrotechnology Transfer) for Sitamarhi district, Bihar, India. Results showed that the late planting of rice predicted the highest yield (3800 kg ha-1) with high variability under SCF (wet) and 200 kg ha-1 application of nitrogen fertilizer. Similarly, for SCF (dry), the late planting of rice simulated high yield (3100 kg ha-1) attributes with 200 kg ha-1 of nitrogen fertilizer. However, rice yield under climatology (3450 kg ha-1) was more than SCF (dry) (3100 kg ha-1). Planting of rice on 15th June 2019 under the SCF (normal) predicted low uncertainty with high mean yields as compared to the mid (05th July 2019), and late (25th July 2019) planting. The present study showed that by applying SCF, we can have a better understanding on “relative” changes in yield attributes with fertilizer doses and planting dates, which may be adopted by the climate adviser to offset the climate risk without compromising productivity.

Given that rice husk biochar has been shown to modulate salinity in salt-affected acid soils, the objective of this study was to investigate the effects of organic amendment of salinized acid soils on P fractions, enzyme activities, and associated rice yield. Four treatments, viz. Rice–Rice–Rice, [RRR]; Fallow–Rice–Rice, [FRR]; Fallow–Rice–Rice + 3 Mg ha−1 of compost [FRR + Comp]; and Fallow–Rice–Rice + 10 Mg ha−1 of biochar [FRR + BC] were established at Ben Tre and Kien Giang sites, Viet Nam, over six consecutive crops. Soil properties at harvest of the sixth crop showed that there were diverse patterns of fractionation between P forms with respect to treatment. Overarchingly, biochar increased labile and moderately labile inorganic P and organic P by 30% to 70%, respectively, whilst compost had a relatively modest effect on these pools. Soil phosphatase activities at crop tillering increased following the FRR + Comp and FRR + BC treatments compared with those in RRR, except for acid phosphatase at Ben Tre. At harvest, there were no significant differences between the enzyme activities among the treatments. Rice yield was positively correlated with the more labile forms of P, soil C, and acid phosphatase activity. In the absence of organic amendments, there was no effect of triple versus double rice crops being grown in one-year cycle. Repeated application of biochar (10 Mg ha−1 × 5 times) showed potential to increase grain yields and total soil C in salt-affected acid soils, via modulation of P transformations to more plant-available forms.