Rice Phenology Research Articles

A new rice identification algorithm under complex terrain combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization synthetic aperture radar

Accurate mapping of rice-growing areas is essential to ascertain the spatial distribution of rice fields, and ensure food security. It is a challenging task to timely and accurate identify rice under the complex terrain due to its diversified land cover, small- or middle-sized rice fields with fragmented distribution. In this paper, the time series VV and VH backscatter coefficient datasets were first constructed based on 411 sentinel-1 synthetic aperture radar (SAR) images in Chongqing city with complex terrain. Then, the rice multi-characteristic parameters, including SAR backscatter features, composite features, rice phenological parameters, texture features and topographic features, were generated. On this basis, the homogeneous image objects were produced. Furthermore, a rice identification algorithm combining multi-characteristic parameters and homogeneous objects based on time series dual-polarization SAR (MPHO-DPSAR) was established. The research demonstrated that the MPHO-DPSAR algorithm can achieve accurate mapping of small and medium-sized and fragmented rice fields in regions under complex terrain according to the accuracy evaluation at three levels and the comparison with other three classical rice identification methods. The suitability and limitations of proposed MPHO-DPSAR algorithm were also discussed from the aspects of SAR data temporal and spatial resolution, rice phenology, and surface landscape complexity.

Phenological analysis and yield estimation of rice based on multi-spectral and SAR data in Maha Sarakham, Thailand

ABSTRACT Rice is one of the most essential food crops in the world and accurate estimation of rice yield is a major content of agricultural research. Recently, scholars have used machine learning algorithms for rice yield estimation. However, there are few studies on rice yield prediction based on rice phenological stages. In this study, a method for rice yield prediction on the basis of rice phenology analysis was proposed. For this study, the cumulative NDVI and EVI based Logistic regression curves were carried out to determine the phenological period. Comparing several regression models, the results of the random forest regression model developed using phenology-based regression analysis performed better. The R2 of training and validation samples were 0.96 and 0.95, respectively, with RMSE of 0.06 ton/ha. This method is feasible for governments to predict rice yield and make farm risk management decisions.

Study on Rice Phenological Differences under Heavy Metal Stress by Using Multi-source Remote Sensing Time-Series Images

Heavy metal pollution is surveyed rapidly and accurately, which is of great significance for soil pollution control. heavy metal pollution on farmland results in phenological changes, and we can use remote sensing technology to observe these changes. In the paper, Normalized Difference Vegetation Index (NDVI) time-series is built based on multi-source remote sensing images. Savitzky-Golay filter (S-G filter) was applied for denoising and reconstructing the time series. NDVI time series were used to calculate phenological indicators. Five phenological indicators were selected, which include seasonal amplitude, growth rate, seasonal length, seasonal integral and base level, to study on effect of heavy metal stress on rice phenology. The results showed that differences in phenological indexes at different levels of heavy metal stress as well as the phenological indicators under the stressed condition were generally lower than those under mild and moderate stress. From the findings, it can be concluded that the superiority of remote sensing phenological information in the monitoring of heavy metal stress in rice, and a new method for distinguishing heavy metal stress in rice will be provided.

Phenology, growth and productivity of rice (Oryza sativa l.) under reduced sunlight intensity and foliar nitrogen application

The field experiments related to the study were conducted at Punjab Agricultural University, Ludhiana and Krishi Vigyan Kendra, Bahowal. There were 20 treatment combinations comprising of four levels of sunlight intensity [control (full sunlight), 50% reduction in sunlight light intensity during 15-45(R15-45), 46-75(R46-75) and 76-105(R76-105) DAT] and five levels of foliar nitrogen application [control (only recommended nitrogen, no foliar spray), spray of 3% urea before (NB), midway(NM), afterwards(NA) and midway-afterwards(NMA) the reduction in sunlight intensity in addition to the recommended nitrogen application]. Radiation level of R15-45 significantly preponed (2-5 days) the occurrence of phenological stages from maximum tillering to physiological maturity. Similarly, R46-75 significantly preponed (3-9 days) the occurrences of flag leaf initiation to physiological maturity. The nitrogen level of NB, significantly delayed the occurrence of flag leaf initiation to physiological maturity stages (2-5 days), NM delayed flag leaf initiation to physiological maturity (2-4 days), NA delayed anthesis to physiological maturity (1-4 days) and NMA delayed panicle emergence to physiological maturity (2-5 days). Plant height of rice was significantly increased due to R15-45 (5.21-5.26%) and R46-75 (6.95-7.61%) and also with foliar nitrogen application levels of NB, NM, NA and NMA by 9.95-9.89, 9.65-10.06, 8.06-7.57 and 9.65-9.29 per cent, respectively. Number of tillers m-2 was significantly reduced (4.71-15.99%) under low light conditions and was increased (5.59-23.34%) with foliar nitrogen application. Yield attributes were affected by the reduced sunlight intensity and foliar nitrogen application. Grain yield was significantly reduced as a result of R15-45 (9.19-11.41%), R46-75 (14.29-16.35%) and R76-105 (8.15-10.67%) and foliar nitrogen application levels of NB, NM and NMA resulted in significant increase the grain yield by 9.32-10.19, 5.74-6.38 and 4.59-5.74 per cent respectively, as compared to control.

A Physically Interpretable Rice Field Extraction Model for PolSAR Imagery

Reliable and timely rice distribution information is of great value for real-time, quantitative, and localized control of rice production information. Synthetic aperture radar (SAR) has all-weather and all-day observation capability to monitor rice distribution in tropical and subtropical areas. To improve the physical interpretability and spatial interpretability of the deep learning model for SAR rice field extraction, a new SHapley Additive exPlanation (SHAP) value-guided explanation model (SGEM) for polarimetric SAR (PolSAR) data was proposed. First, a rice sample set was produced based on field survey and optical data, and the physical characteristics were extracted using decomposition of polarimetric scattering. Then a SHAP-based Physical Feature Interpretable Module (SPFIM) combing the long short-term memory (LSTM) model and SHAP values was designed to analyze the importance of physical characteristics, a credible physical interpretation associated with rice phenology was provided, and the weight of physical interpretation was combined with the weight of original PolSAR data. Moreover, a SHAP-guided spatial interpretation network (SSEN) was constructed to internalize the spatial interpretation values into the network layer to optimize the spatial refinement of the extraction results. Shanwei City, Guangdong Province, China, was chosen as the study area. The experimental results showed that the physical explanation provided by the proposed method had a high correlation with the rice phenology, and spatial self-interpretation for finer extraction results. The overall accuracy of the rice mapping results was 95.73%, and the kappa coefficient reached 0.9143. The proposed method has a high interpretability and practical value compared with other methods.

Automated Rice Phenology Stage Mapping Using UAV Images and Deep Learning

Accurate monitoring of rice phenology is critical for crop management, cultivars breeding, and yield estimating. Previously, research for phenology detection relied on time-series data and orthomosaic and manually plotted regions, which are difficult to automate. This study presented a novel approach for extracting and mapping phenological traits directly from the unmanned aerial vehicle (UAV) photograph sequence. First, a multi-stage rice field segmentation dataset containing four growth stages and 2600 images, namely PaddySeg, was built. Moreover, an efficient Ghost Bilateral Network (GBiNet) was proposed to generate trait masks. To locate the trait of each pixel, we introduced direct geo-locating (DGL) and incremental sparse sampling (ISS) techniques to eliminate redundant computation. According to the results on PaddySeg, the proposed GBiNet with 91.50% mean-Intersection-over-Union (mIoU) and 41 frames-per-second (FPS) speed outperformed the baseline model (90.95%, 36 FPS), while the fastest GBiNet_t reached 62 FPS which was 1.7 times faster than the baseline model, BiSeNetV2. Additionally, the measured average DGL deviation was less than 1% of the relative height. Finally, the mapping of rice phenology was achieved by interpolation on trait value–location pairs. The proposed approach demonstrated great potential for automatic rice phenology stage surveying and mapping.

Feature-based algorithm for large-scale rice phenology detection based on satellite images

Knowledge of rice phenology is essential for understanding the agricultural practices and studying its impact on ecosystem services. However, so far, available global-scale rice phenology maps do not provide fine spatiotemporal details of rice phenology in a consistent framework because they rely on the compilation of statistical data. Thus, this paper proposes an algorithm that combines the complementary advantages of Sentinel-1 and Sentinel-2 satellite images to produce large-scale maps that depict rice phenology dynamics. The novelty of this algorithm lies in the correlation with rice phenology features, i.e., rice in water condition and rice color change. The time series of backscattering at Vertical-Horizontal (VH) polarization and Enhanced Vegetation Index (EVI) are proposed to recognize rice planting and heading dates, respectively. For the same time, the Normalized Difference Yellow Index (NDYI) is utilized to detect the rice harvest date for the first time. The proposed algorithm is applied to multiple spatial scales (prefecture, 0.5° gridcell, and site scales) and to multiple rice cropping systems (single, double, and triple croppings) in monsoon Asia. Results reveal that the algorithm is able to accurately detect the rice planting and harvest dates across two rice paddy field distribution maps with moderate-to-high spatial resolution, different validation data, and different rice cropping systems. The bias values of detected planting dates are 2, 0, and 4 days, while that of harvest dates are -2, -5, and -13 days at the prefecture, 0.5° gridcell, and site scales, respectively. These results highlight the potential of this algorithm to generate national, continental, or even global maps of rice phenology dynamics in an efficient manner, which can facilitate research on the impact of rice phenology on rice ecosystem services that echoes environmental and climate change.

Improving rice phenology simulations based on the Bayesian model averaging method

Crop simulation models play an increasingly important role in agricultural management. Therefore, improving crop models’ accuracy and reliability has become a key issue. Previous studies have shown that multi-model ensembles (MMEs) perform better than individual models and are capable of assessing model uncertainty. Here, we study the use of the Bayesian model averaging (BMA) method to improve the simulation performance of rice phenology models. The main objective of this paper is to apply BMA to five individual rice phenology models (CERES-Rice, the beta model, ORYZA2000, the rice clock model, and the simulation model for the rice-weather relationship) and to compare its simulation performance with an MME based on the mean value (Mean-MME). Furthermore, we quantify and evaluate the uncertainty of the individual and ensemble models. Finally, we analyze the prediction results of two ensemble models and five individual models under future climate scenarios. The result indicates that, compared with the accuracies of individual models, the accuracies of BMA and Mean-MME are 12.75% and 11.48% higher, respectively. BMA performs the best when the individual models had the best parameters, effectively optimizing the phenology simulation results of the individual models. For individual and ensemble models, the squared bias accounts for 48–88%, which is the largest contributor to overall prediction uncertainty. Under the future climate scenarios, the five models predict a range of 0.40–9.78 days in the phenological period, whereas the two ensemble models predict a smaller range of 0.54–1.63 days, which shows that the MMEs reduce the uncertainty of the individual models. We conclude that BMA is suitable for improving the accuracy and reliability of crop modeling.

Agricultural Field Boundary Delineation with Satellite Image Segmentation for High-Resolution Crop Mapping: A Case Study of Rice Paddy

Parcel-level cropland maps are an essential data source for crop yield estimation, precision agriculture, and many other agronomy applications. Here, we proposed a rice field mapping approach that combines agricultural field boundary extraction with fine-resolution satellite images and pixel-wise cropland classification with Sentinel-1 time series SAR (Synthetic Aperture Radar) imagery. The agricultural field boundaries were delineated by image segmentation using U-net-based fully convolutional network (FCN) models. Meanwhile, a simple decision-tree classifier was developed based on rice phenology traits to extract rice pixels with time series SAR imagery. Agricultural fields were then classified as rice or non-rice by majority voting from pixel-wise classification results. The evaluation indicated that SeresNet34, as the backbone of the U-net model, had the best performance in agricultural field extraction with an IoU (Intersection over Union) of 0.801 compared to the simple U-net and ResNet-based U-net. The combination of agricultural field maps with the rice pixel detection model showed promising improvement in the accuracy and resolution of rice mapping. The produced rice field map had an IoU score of 0.953, while the User‘s Accuracy and Producer‘s Accuracy of pixel-wise rice field mapping were 0.824 and 0.816, respectively. The proposed model combination scheme merely requires a simple pixel-wise cropland classification model that incorporates the agricultural field mapping results to produce high-accuracy and high-resolution cropland maps.

Asian Rice Calendar Dynamics Detected by Remote Sensing and Their Climate Drivers

Detecting crop calendar changes is critically important for crop monitoring and management, but the lack of annual, Asia-wide, and long-term rice calendar datasets limits our understanding of rice phenological changes and their climate drivers. In this study, we retrieved key rice phenological dates from the GLASS AVHRR LAI through combining threshold-based and inflection-based detection methods, analyzed the changes during the period 1995–2015, and identified the key climate drivers of the main rice seasons in Asia. The retrieved phenological dates had a high level of agreement with the referenced observations. All R2 were greater than 0.80. The length of the vegetation growing period (VGP) was mostly shortened (by an average of −4 days per decade), while the length of the reproductive growing period was mostly prolonged (by an average of 2 days per decade). Moreover, solar radiation had the most significant impact on the rice calendar changes, followed by the maximum and minimum temperatures. The VGP in tropical areas is the most sensitive to climate change. Our study extends the annual rice phenology dynamics to a higher spatial–temporal resolution and provides new insights into rice calendar changes and their climate drivers, which will assist governments and researchers regarding food security and agricultural sustainability.

Climate Change Impact on Yield and Water Use of Rice-Wheat Rotation System in the Huang-Huai-Hai Plain, China.

Simple SummaryQuantitatively exploring the impact of climate change on crop production and water consumption (i.e., crop evapotranspiration during crop growth period, ET) is very important to achieve sustainable regional agricultural development. In this study, based on daily downscaled climate data from 22 Global Climate Models (GCMs), we applied the Agricultural Production Systems sIMulator (APSIM) to investigate the possible impact of climate change (e.g., temperature (Temp), solar radiation (Rad), precipitation (Prec) and CO2) on crop phenology, yield and water consumption for the rice (Oryza sativa L.) -wheat (Triticum aestivum L.) rotation. Due to the increase in Temp, the key phenological periods (flowering and maturity) of wheat in the future mainly tend to advance, while the phenological changes of rice show different trends at different sites. Both rice and wheat yields were negatively correlated with Temp, but positively correlated with Rad, Prec, and CO2 concentration ([CO2]). However, crop ET was positively correlated with Rad, but negatively correlated with [CO2], as elevated [CO2] decreased stomatal conductance. Moreover, the water use efficiency (WUE) of rice and wheat was negatively correlated with Temp, but positively correlated with [CO2]. Overall, climate change will have a significant impact on the crop growth process, yield and water consumption.Global climate change has had a significant impact on crop production and agricultural water use. Investigating different future climate scenarios and their possible impacts on crop production and water consumption is critical for proposing effective responses to climate change. In this study, based on daily downscaled climate data from 22 Global Climate Models (GCMs) provided by Coupled Model Intercomparison Project Phase 6 (CMIP6), we applied the well-validated Agricultural Production Systems sIMulator (APSIM) to simulate crop phenology, yield, and water use of the rice–wheat rotation at four representative stations (including Hefei and Shouxian stations in Anhui province and Kunshan and Xuzhou stations in Jiangsu province) across the Huang-Huai-Hai Plain, China during the 2041–2070 period (2050s) under four Shared Socioeconomic Pathways (i.e., SSP126, SSP245, SSP370, and SSP585). The results showed a significant increase in annual mean temperature (Temp) and solar radiation (Rad), and annual total precipitation (Prec) at four investigated stations, except Rad under SSP370. Climate change mainly leads to a consistent advance in wheat phenology, but inconsistent trends in rice phenology across four stations. Moreover, the reproductive growth period (RGP) of wheat was prolonged while that of rice was shorted at three of four stations. Both rice and wheat yields were negatively correlated with Temp, but positively correlated with Rad, Prec, and CO2 concentration ([CO2]). However, crop ET was positively correlated with Rad, but negatively correlated with [CO2], as elevated [CO2] decreased stomatal conductance. Moreover, the water use efficiency (WUE) of rice and wheat was negatively correlated with Temp, but positively correlated with [CO2]. Overall, our study indicated that the change in Temp, Rad, Prec, and [CO2] have different impacts on different crops and at different stations. Therefore, in the impact assessment for climate change, it is necessary to explore and analyze different crops in different regions. Additionally, our study helps to improve understanding of the impacts of climate change on crop production and water consumption and provides data support for the sustainable development of agriculture.

Spatiotemporal Change of Heat Stress and Its Impacts on Rice Growth in the Middle and Lower Reaches of the Yangtze River

Heat stress will restrict rice yield in the middle and lower reaches of the Yangtze River. An understanding of the meteorological conditions of heat stress of rice production is important for improving the accuracy of the phenology simulation. Based on the observations of phenology and heat stress of rice agrometeorological stations in this region, as well as meteorological observations and future scenarios, this study analyzed the spatiotemporal change of heat stress and its impacts on rice growth in this region from 1990 to 2009. The results showed that the heat stress frequency of early rice increased in this region from 2000 to 2009, and that of late rice and single-season rice decreased. Moreover, rice phenology will advance under heat stress conditions. The spatiotemporal consistency of the observations and the meteorological index of heat stress shows that the change in heat stress is attributed to climate changes and extreme meteorological events. Under future climate scenarios, it is found that the frequency of heat stress will increase, which will have a serious impact on rice production. The results suggest that positive and effective measures should be taken to adapt to climate change for rice production.

Estimating plant–insect interactions under climate change with limited data

Climate change may disrupt species–species interactions via phenological changes in one or both species. To predict and evaluate the influence of climate change on these interactions, long-term monitoring and sampling over large spatial areas are required; however, funding and labor constraints limit data collection. In this study, we predict and evaluate the plant–insect interactions with limited data sets. We examined plant–insect interaction using observational data for development of the crop plant rice (Oryza sativa) and an effective accumulated temperature (EAT) model of two mirid bugs (Stenotus rubrovittatus and Trigonotylus caelestialium). We combined 11 years of records monitoring rice phenology and the predicted phenology of mirid bugs using spatially–explicit EAT models based on both spatially and temporally high resolutions temperature data sets, then evaluated their accuracy using actual pest damage records. Our results showed that the predicted interactions between rice and mirid bugs explained rice damage to some degree. Our approach may apply predicting changes to plant–insect interactions under climate change. As such, combining plant monitoring records and theoretical predictions of insect phenology may be effective for predicting species–species interactions when available data are limited.

Rice Phenology Retrieval Based on Growth Curve Simulation and Multi-Temporal Sentinel-1 Data

The accurate estimation and monitoring of phenology is necessary for modern agricultural industries. For crops with short phenology occurrence times, such as rice, Sentinel-1 can be used to effectively monitor the growth status in different phenology periods within a short time interval. Therefore, this study proposes a method to monitor rice phenology based on growth curve simulation by constructing a polarized growth index (PGI) and obtaining a polarized growth curve. A recursive neural network is used to realize the classification of phenology and use it as prior knowledge of rice phenology to divide and extract the phenological interval and date of rice in 2021. The experimental results show that the average accuracy of neural network phenological interval division reaches 93.5%, and the average error between the extracted and measured phenological date is 3.08 days, which proves the application potential of the method. This study will contribute to the technical development of planning, management and maintenance of renewable energy infrastructure related to phenology.

Rice Monitoring and Yield Estimation in Dumangas, Iloilo, Philippines Using Satellite Imagery

An agricultural monitoring procedure utilizing the combination of remotely sensed data, vegetation indices, ground truth information, and local knowledge on the phenology and development of rice, which is the staple crop produced in Dumangas, Iloilo, Philippines, was established and analyzed in this study. The areas with standing crops and their corresponding growth stage were reported for the 2nd and 3rd cropping of 2020. The spatio-temporal analysis determined that NDVI is directly correlated with the photosynthetic activity and development of the crop. Dry season cropping lasted from October to January, while the 3rd cropping lasted from February to April. The percentage of the rice areas planted per month were also studied and observed that the area spatially and numerically increased as farmers planted during the start of the season. The ground data from the 20 GCPs were compared with the Sentinel 2 NDVI. The phenological metrics for each point characterized the development of the crop. From the investigation, the municipality commonly used hybrid varieties, which have a growth duration of 85 to 100 days. The NDVI ranges were 0.163 to 0.309, 0.35 to 0.682, 0.535 to 0.671, and 0.235 to 0.533 for seedling, vegetative, reproductive, and ripening. APSIM was used for yield modeling of the cropping season 2019 and observed to have a good correlation during the wet season. Underestimation was detected for cropping seasons during times with limited rainfall. This study was beneficial for the LGU in crop estimation as it produced highly reliable spatial and temporal results.

Response of rice phenology to climate warming weakened across China during 1981–2018: did climatic or anthropogenic factors play a role?

Climate warming has substantially shifted plant phenology, which alters the length of growing season and consequently affects plant productivity. Recent studies showed a stalled or reversed impact of climate change on vegetation phenology since 1998, as well as an asymmetric warming effect. However, how field crop phenology responded to the recent climate warming and the asymmetric warming remains unknown. In addition, the relative roles of climate change, sowing date and cultivars shifts in the spatiotemporal changes of crop phenology at different regions need to be better understood. Here, using the latest 9,393 phenological records at 249 agro-meteorological stations across China over 1981–2018, we critically investigated the spatiotemporal dynamics of rice phenology and disentangled the effects of different drivers by exploiting the physiological relationship between crop phenology and thermal accumulation. The results showed that length of growing period (GP) increased by 3.24 ± 0.15 days/decade for single rice, 1.90 ± 0.22 days/decade for early rice and 0.47 ± 0.14 days/decade for late rice. Although climate warming during rice GP did not slow down, the trends in rice GP and the correlations between GP and temperature decreased generally from 1981–1999 to 2000–2018. The weakened phenological response to climate change was mainly caused by agronomic managements, especially cultivar shifts. Climate warming shortened GP by 0.84 ± 1.80, 1.23 ± 0.77, and 1.29 ± 1.24 days/decade for single rice, early rice and late rice, respectively. However, cultivar shifts prolonged it respectively by 3.28 ± 3.68, 2.15 ± 2.38, and 2.31 ± 3.36 days/decade, totally offsetting the negative effects of climate warming. Rice responded to daytime and night-time warming differently with night-time temperature affecting GPs more. Our study provided new insights that rice phenology responded to night-time warming more than daytime warming across China however the response to climate warming weakened, and cultivar shifts outweighed climate change in affecting rice phenology.

Mapping paddy rice and rice phenology with Sentinel-1 SAR time series using a unified dynamic programming framework

AbstractMonitoring rice planting areas and their phenological phases is crucial for yield estimation and informed decision-making. This study proposed a unified method for mapping rice field and rice phenology with a dynamic time wrapping (DTW) distance-based classifier and its variant sub-DTW algorithm using Sentinel-1’s synthetic aperture radar (SAR) VH band. Field samplings were conducted for broad landcover types in one of the areas of interest (AOIs). We implemented a pixel-wisek-nearest neighbor classification model with DTW distance to identify paddy rice pixels. Standard rice phenological profiles of the SAR VH band were defined by ground monitoring of a sample rice field. Based on rice planting maps and the standard phenological profiles, rice phenological phases were estimated by pattern matching strategy with the sub-DTW algorithm. Experiments on six counties in Northeast China presented promising results. The overall producer and user accuracy reached 92.9 and 91.9% for rice mapping, respectively. The mean root mean square error (RMSE) for phenology estimation was 3.5 days. Rice planting and rice phenology maps were generated for the six AOIs. The phenological variances of the AOIs implied the effects of climate and rice cultivars on phenological development.

Evaluating the impact of intra-seasonal change in temperature and solar radiation on phenology and yield of rice using CERES-rice model in Punjab

Rice is a staple food of global as well as Indian population and its productivity is vulnerable to climate change. Keeping in view the observed trends in climate variability, phenology and yield of rice were simulated using CERES-Rice model under climatic scenarios of changes in temperature (+0.5, +1.0, +1.5, +2.0, +2.5, +3.0 °C from normal) and solar radiation (+2.5, +5.0, +10.0, +12.5, +15.0, +20.0% from normal) and their combined interactive effects during differential growth periods, i.e., whole season, vegetative phase, grain filling phase, 0-30 days after transplanting (DAT), 30-60 DAT, 60-90 DAT. In general, with a rise in temperature from normal, the CERES-Rice model predicted advancement in phenological development in rice and vice versa. In general, the increase in yield of rice cultivars for different transplanting dates was observed with decrease in temperature and increase in radiation and vice-versa. With rise in temperature from normal, CERES-Rice model showed maximum decrease in grain yield of rice during whole season change followed by grain filling phase change and vegetative phase change in decreasing order. Among the growth periods, maximum decrease in yield was observed in 0-30 DAT (early vegetative) followed by 30-60 DAT (late vegetative) and 60-90 DAT (grain filling) in decreasing order. Under the conditions of heat and radiative stress, grain filling phase and early vegetative phase was more vulnerable. The CERES-Rice model predictions showed that rice cv. PR-118 (long duration cultivar) was more tolerant to heat and radiative stress than cv. PR-115 (short duration cultivar) and hence may be recommended for cultivation due to its tolerant traits towards maintaining its good grain yield and harvest index.

Crop Monitoring Strategy Based on Remote Sensing Data (Sentinel-2 and Planet), Study Case in a Rice Field after Applying Glycinebetaine

World agriculture is facing a great challenge since it is necessary to find a sustainable way to increase food production. Current trends in advancing the agriculture sector are based on leveraging remote sensing technology and the use of biostimulants. However, the efficient implementation of both of these on a commercial scale for the purposes of productivity improvement remains a challenge. Thus, by proposing a crop monitoring strategy based on remote sensing data, this paper aims to verify and anticipate the impact of applying a Glycinebetaine biostimulant (GB) on the final yield. The study was carried out in a rice-producing area in Eastern Spain (Valencia) in 2021. GB was applied by drone 33 days after sowing (tillering phase). Phenology was monitored and crop production parameters were determined. Regarding satellite data, Sentinel-2 cloud-free images were obtained from sowing to harvest, using the bands at 10 m. Planet data were used to evaluate the results from Sentinel-2. The results show that GB applied 33 days after sowing improves both crop productive parameters and commercial yield (13.06% increase). The design of the proposed monitoring strategy was based on the dynamics and correlations between the visible (green and red) and NIR bands. The analysis showed differences when comparing the GB and control areas, and permitted the determination of the moment in which the effect of GB on yield (tillering and maturity) may be greater. In addition, an index was constructed to verify the crop monitoring strategy, its mathematical expression being: NCMI = (NIR − (red + green))/(NIR + red + green). Compared with the other VIs (NDVI, GNDVI and EVI2), the NCMI presents a greater sensitivity to changes in the green, red and NIR bands, a lower saturation phenomenon than NDVI and a better monitoring of rice phenology and management than GNDVI and EVI2. These results were evaluated with Planet images, obtaining similar results. In conclusion, in this study, we confirm the improvement in rice crop productivity by improving sustainable plant nutrition with the use of biostimulants and by increasing the components that define crop yield (productive tillers, spikelets and grains). Additionally, crop monitoring using remote sensing technology permits the anticipation and understanding of the productive behavior and the evolution of the phenological stages of the crop, in accordance with crop management.

Evaluation of global gridded crop models (GGCMs) for the simulation of major grain crop yields in China

Abstract Multimodel ensembles are powerful tools for evaluating agricultural production. Multimodel simulation results provided by the Global Gridded Crop Model Intercomparison (GGCMI) facilitate the evaluation of the grain production situation in China. With census crop yield data, the performance of nine global gridded crop models (GGCMs) in China was evaluated, and the yield gaps of four crops (maize, rice, soybean, and wheat) were estimated. The results showed that GGCMs better simulated maize yields than those of other crops in the northeast, north, northwest, east, and center. GEPIC (CLM-CROP) performed best in simulating maize (wheat) yield in the north, east, and northwest (southwest and south), due to reasonable parameter (cultivar and phenology parameters) settings. Because the rice phenology parameters were calibrated against phenological observation networks and a simple nitrogen limitation index was introduced, ORCHIDEE-CROP performed well in rice yield simulation and soybean yield simulation (center and southwest). Among four crops, wheat has the largest yield gap (7.3–14.1%), in which the poor soil of northwest (14.1%) exposes wheat to relatively high nutritional stress. Thus, in northwest China, optimizing nitrogen management in wheat production can effectively mitigate the negative impact of climate change on crop production.