Wheat Phenology Research Articles

Impacts of climate change on phenology, yield, and water productivity of wheat in a semi-arid region of India using the CERES-Wheat model

ABSTRACT In the present study, the Crop Environment Resource Synthesis (CERES)-Wheat model was used to study the impacts of climate change on phenology, yield, and water productivity of wheat. The model was run with the baseline period (1980–2010) and three future periods, namely, the 2030s, 2050s, and 2070s under two representative concentration pathway (RCP) scenarios, namely, RCP 4.5 and RCP 8.5. The results indicated a substantial decline in phenology, grain yield, biomass, and crop water productivity (CWP) under both scenarios. The grain yield of wheat showed a decline by 12.3, 20.5, and 19.8% during the 2030s, 2050s, and 2070s, respectively, under RCP 4.5 at baseline CO2 concentration, while at elevated concentration of CO2, the reduction was 10.2, 15.7, and 14.9%, respectively. Under RCP 8.5, the yield reduction was 18.8, 26.5, and 27.3% during the 2030s, 2050s, and 2070s, respectively, with baseline concentration of CO2, while with increased CO2 the yield reduction was 7, 12.6, and 8.9%, respectively. CWP decreased at baseline CO2 by 9, 17.6, and 18.3% for RCP 4.5 and 10.6, 20.6, and 22.6% for RCP 8.5 during the 2030s, 2050s, and 2070s, respectively. However, beneficial impact of CO2 fertilization on CWP was noticed in both RCP scenarios, resulting in relatively less reduction under future CO2 concentration.

A deep learning approach for deriving wheat phenology from near-surface RGB image series using spatiotemporal fusion

Accurate monitoring of wheat phenological stages is essential for effective crop management and informed agricultural decision-making. Traditional methods often rely on labour-intensive field surveys, which are prone to subjective bias and limited temporal resolution. To address these challenges, this study explores the potential of near-surface cameras combined with an advanced deep-learning approach to derive wheat phenological stages from high-quality, real-time RGB image series. Three deep learning models based on three different spatiotemporal feature fusion methods, namely sequential fusion, synchronous fusion, and parallel fusion, were constructed and evaluated for deriving wheat phenological stages with these near-surface RGB image series. Moreover, the impact of different image resolutions, capture perspectives, and model training strategies on the performance of deep learning models was also investigated. The results indicate that the model using the sequential fusion method is optimal, with an overall accuracy (OA) of 0.935, a mean absolute error (MAE) of 0.069, F1-score (F1) of 0.936, and kappa coefficients (Kappa) of 0.924 in wheat phenological stages. Besides, the enhanced image resolution of 512 × 512 pixels and a suitable image capture perspective, specifically a sensor viewing angle of 40° to 60° vertically, introduce more effective features for phenological stage detection, thereby enhancing the model’s accuracy. Furthermore, concerning the model training, applying a two-step fine-tuning strategy will also enhance the model’s robustness to random variations in perspective. This research introduces an innovative approach for real-time phenological stage detection and provides a solid foundation for precision agriculture. By accurately deriving critical phenological stages, the methodology developed in this study supports the optimization of crop management practices, which may result in improved resource efficiency and sustainability across diverse agricultural settings. The implications of this work extend beyond wheat, offering a scalable solution that can be adapted to monitor other crops, thereby contributing to more efficient and sustainable agricultural systems.

Temperature increase may not necessarily penalize future yields of three major crops in Xinjiang, Northwest China

Future food production is at risk due to climate change, particularly in arid regions with limited water resources and extensive irrigated agriculture. This study utilized the DSSAT model in conjunction with downscaled data from 21 global climate models (GCMs) under two shared socioeconomic pathways (SSP2–4.5 and SSP5–8.5) to assess the impact of projected climate change on irrigated crop phenology, yield, and evapotranspiration (ETc) of cotton, maize, and winter wheat in the Shihezi region of Xinjiang, China. The results indicated that temperature and precipitation in the region were expected to increase gradually from 2021 to 2100. Climate change has resulted in earlier anthesis and physiological maturity of all three crops. Future climatic conditions could reduce maize yields to 16.02 %. Conversely, the yields of cotton and winter wheat increased, with cotton yields rising by 1.23–10.94 % and winter wheat yields by 3.19–14.07 %. Additionally, ETc for cotton, maize, and winter wheat could rise in the future. The irrigation water demands could increase by 41.1–96.4 mm for cotton and 27.3–37.9 mm for maize, while the demand for winter wheat could decrease by 0.5–36.2 mm. Warming was significantly correlated with the changes in the yield and water use efficiency (WUE) of cotton, maize, and winter wheat. The temperature increases of +0.5°C to +3.0°C (relative to baseline) at 0.5°C intervals were analyzed to evaluate their effects on yield and WUE. The yields varied from −0.93 % to 6.15 % for cotton, −43.42 % to −7.99 % for maize, and 4.28–9.92 % for winter wheat. The WUE changes ranged from −29.03 % to −1.08 % for cotton, −43.06 % to −7.66 % for maize, and 0.69–3.47 % for winter wheat. Contrary to the common belief that rising temperatures generally harm crop yields, our study suggests that temperature fluctuations may benefit certain crops in specific regions. These results could provide theoretical guidance for implementing adaptive measures to future climate change in regions with conditions similar to Shihezi, Xinjiang, China, to ensure crop security and sustainable water management.

Improving the estimation accuracy of wheat maturity date by coupling WheatGrow with satellite images

Accurate estimation of wheat maturity date (MD) is helpful to make reasonable harvest planning and guarantee crop yield and quality. In this study, wheat phenology extracted from satellite images was assimilated into WheatGrow model to develop wheat maturity date estimation model. Theoretical uncertainty was introduced into assimilation system as the error covariance matrix of remote sensing observations, which improved the performance of maturity date estimation model. Compared with the simulated maturity date of crop growth model and assimilation system combined with the constant uncertainty (Assimilation1), the accuracy of assimilation system combined with the theoretical uncertainty (Assimilation2) was higher (r = 0.81, RMSE = 4.5 d). Assimilation2 has better performance and robustness in different years and different subregions. The mean relative errors between the estimated values of Assimilation2 and the observations were generally small and concentrated in the range of −5 % to 5 %. The estimated maturity date showed latitude variation in spatial distribution in the Huang-Huai-Hai Plain (HHHP). In addition, the trend of wheat maturity date from 2001 to 2020 in the central region of HHHP was significant (p < 0.05), and the mean change rate of maturity date reached 3–6 d/10a. However, the overall change trend of maturity date in the HHHP was not significant. Temperature was main driver affecting the spatiotemporal variation of wheat maturity date. The regional wheat maturity date estimation model can provide technical support for wheat maturity date estimation at regional scale.

Automatic mapping of winter wheat planting structure and phenological phases using time-series sentinel data

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky–Golay (S–G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S–G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability.

Mitigating drought-induced oxidative stress in wheat (Triticum aestivum L.) through foliar application of sulfhydryl thiourea

Drought stress is a major abiotic stress affecting the performance of wheat (Triticum aestivum L.). The current study evaluated the effects of drought on wheat phenology, physiology, and biochemistry; and assessed the effectiveness of foliar-applied sulfhydryl thiourea to mitigate drought-induced oxidative stress. The treatments were: wheat varieties; V1 = Punjab-2011, V2 = Galaxy-2013, V3 = Ujala-2016, and V4 = Anaaj-2017, drought stress; D1 = control (80% field capacity [FC]) and D2 = drought stress (40% FC), at the reproductive stage, and sulfhydryl thiourea (S) applications; S0 = control-no thiourea and S1 = foliar thiourea application @ 500 mg L−1. Results of this study indicated that growth parameters, including height, dry weight, leaf area index (LAI), leaf area duration (LAD), crop growth rate (CGR), net assimilation rate (NAR) were decreased under drought stress-40% FC, as compared to control-80% FC. Drought stress reduced the photosynthetic efficiency, water potential, transpiration rates, stomatal conductances, and relative water contents by 18, 17, 26, 29, and 55% in wheat varieties as compared to control. In addition, foliar chlorophyll a, and b contents were also lowered under drought stress in all wheat varieties due to an increase in malondialdehyde and electrolyte leakage. Interestingly, thiourea applications restored wheat growth and yield attributes by improving the production and activities of proline, antioxidants, and osmolytes under normal and drought stress as compared to control. Thiourea applications improved the osmolyte defense in wheat varieties as peroxidase, superoxide dismutase, catalase, proline, glycine betaine, and total phenolic were increased by 13, 20, 12, 17, 23, and 52%; while reducing the electrolyte leakage and malondialdehyde content by 49 and 32% as compared to control. Among the wheat varieties, Anaaj-2017 showed better resilience towards drought stress and also gave better response towards thiourea application based on morpho-physiological, biochemical, and yield attributes as compared to Punjab-2011, Galaxy-2013, and Ujala-2016. Eta-square values showed that thiourea applications, drought stress, and wheat varieties were key contributors to most of the parameters measured. In conclusion, the sulfhydryl thiourea applications improved the morpho-physiology, biochemical, and yield attributes of wheat varieties, thereby mitigating the adverse effects of drought. Moving forward, detailed studies pertaining to the molecular and genetic mechanisms under sulfhydryl thiourea-induced drought stress tolerance are warranted.

A study of wheat phenology and the factors affecting the wheat yield

A study of wheat phenology and the factors affecting the wheat yield

Thermal indices in relation to crop phenology of wheat (Triticum aestivum L.) crop grown under agro-climatic conditions of Eastern Uttar Pradesh

Thermal indices in relation to crop phenology of wheat (Triticum aestivum L.) crop grown under agro-climatic conditions of Eastern Uttar Pradesh

Case Study of Impact Evaluation of Agrivoltaic Structure Sizing on Water Availability for Wheat

Agrivoltaic (AV) Systems are a new solution for cropping conditions improvement by mitigating extreme weather conditions. Indeed, AV Systems affect microclimate, notably Air Temperature, Irradiance or Evapotranspiration that determines Soil Water Availability. To evaluate crop water stress protection and ensure optimized AV Systems sizing, a methodology was developed using a microclimate simulation tool. This paper presents a case study of Wheat focused on Water Availability, from a project located near Orléans, Center France. The methodology uses Irradiance Simulations at crop level by AGRISOLEO software, which has been parameterized with the structures sizing under study and a panel steering algorithm adapted to wheat phenology. The results are used for evapotranspiration modelling following the FAO-56 Penman-Monteith equation. For this case study, results showed that AV Systems under test reduced irradiance up to 40%. This effect may be reduced up to 17% by controlling the panels rotation angle to maximize irradiance during crop’s key development stages. Furthermore, AV Systems reduced Water Stress up to 48%. Microclimate simulation tool demonstrated possibility to assess AV Systems sizing impact on irradiance received by crop and Water Stress protection. Moreover, controlling the solar panels at key development stages of the crop is the central lever in the synergy of dynamic AV Systems. The methodology presented here applies not only to Wheat but to a wider range of crops and climate conditions, hence opening promising perspectives to optimize AV systems sizing and agronomic benefits.

An improved crop calendar model for winter wheat incorporating vernalization and winter survival to project changes in phenology

Crop phenology and associated crop calendars are necessary for designing breeding goals and for developing effective management practices. Winter wheat is a representative biennial, the cultivation schedule of which is constrained by winter climate conditions, particularly the processes of vernalization and winter survival. Here, we present improvements to a rule-based crop calendar model by incorporating these factors so that it can be used to accurately estimate the phenological events of winter wheat from daily meteorological data. We tested the improved model in Hokkaido, the northernmost Japanese island, which is characterized by seasonal snow cover and a wet summer. The results confirmed that implementing these factors contributed to accurate estimates of peak occurrence dates of winter wheat phenological events. Furthermore, we applied the improved model to simulate wheat phenology under 2 K and 4 K warmer scenarios. The results showed a delayed sowing period up to approximately one month and slight advancements in both flowering and harvesting, leading to a shorter growth period. While this shortened period may be largely compensated by a decrease in the snow-covered period, the shifts in the vegetative and reproductive phases may modify sink-source balance of wheat. We also assessed the risks of pollination failure and preharvest sprouting, both of which are associated with the timing of precipitation, based on the number of rainy days around flowering, and the incidence of precipitation over two consecutive days around the time of harvesting. Our simulations suggested increased risk of pollination failure and reduced risk of preharvest sprouting, leading to an increase in the probability of crop failure. These findings underscore the importance of implementing adaptation measures to mitigate precipitation-related risk under future climates. Further, the findings provide valuable insights for winter wheat breeders and agronomists, thereby facilitating crop production adaptation.

A Comprehensive Evaluation of Dual-Polarimetric Sentinel-1 SAR Data for Monitoring Key Phenological Stages of Winter Wheat

Large-scale crop phenology monitoring is critical for agronomic planning and yield prediction applications. Synthetic Aperture Radar (SAR) remote sensing is well-suited for crop growth monitoring due to its nearly all-weather observation capability. Yet, the capability of the dual-polarimetric SAR data for wheat phenology estimation has not been thoroughly investigated. Here, we conducted a comprehensive evaluation of Sentinel-1 SAR polarimetric parameters’ sensibilities on winter wheat’s key phenophases while considering the incidence angle. We extracted 12 polarimetric parameters based on the covariance matrix and a dual-pol-version H-α decomposition. All parameters were evaluated by their temporal profile and feature importance score of Gini impurity with a decremental random forest classification process. A final wheat phenology classification model was built using the best indicator combination. The result shows that the Normalized Shannon Entropy (NSE), Degree of Linear Polarization (DoLP), and Stokes Parameter g2 were the three most important indicators, while the Span, Average Alpha (α2¯), and Backscatter Coefficient σVH0 were the three least important features in discriminating wheat phenology for all three incidence angle groups. The smaller-incidence angle (30–35°) SAR images are better suited for estimating wheat phenology. The combination of NSE, DoLP, and two Stokes Parameters (g2 and g0) constitutes the most effective indicator ensemble. For all eight key phenophases, the average Precision and Recall scores were above 0.8. This study highlighted the potential of dual-polarimetric SAR data for wheat phenology estimation. The feature importance evaluation results provide a reference for future phenology estimation studies using dual-polarimetric SAR data in choosing better-informed indicators.

Volatile Organic Compounds Mediate Host Selection of Wheat Midge, Sitodiplosis Mosellana (Géhin) (Diptera: Cecidomyiidae) between Preanthesis and Postanthesis Stages of Wheat.

The orange wheat blossom midge, Sitodiplosis mosellana (Géhin) (Diptera: Cecidomyiidae), is a significant wheat pest in the Prairie Provinces of Canada and northern regions of the USA. Wheat phenology plays a critical role in wheat midge oviposition. We hypothesized that S. mosellana oviposition behaviour is influenced by volatile organic compounds (VOCs) emitted by wheat at two adjacent wheat growth stages: preanthesis and postanthesis. A higher number of S. mosellana eggs laid on preanthesis than postanthesis spikes in an oviposition choice experiment using the susceptible spring wheat cultivar 'Roblin'. In preanthesis, wheat emitted higher amounts of Z-3-hexenyl acetate (Z3-06:OAc) than at the postanthesis stage. Higher amounts of methyl ketones such as 2-tridecanone, 2-pentadecanone, and 2-undecanone were emitted by wheat in the postanthesis stage and these VOCs were sensitive to S. mosellana antennae used in the Gas Chromatography-Electroantennographic Detection. Females were attracted to synthetic Z3-06:OAc but were deterred by 2-tridecanone relative to the solvent control in the vertical Y-tube olfactometer. 2-Undecanone and 2-pentadecanone did not show any attractiveness or deterrence. In a no-choice oviposition experiment, fewer eggs were laid in preanthesis wheat exposed to a synthetic VOC blend of Z3-06:OAc, 2-undecanone, 2-tridecanone, and 2-pentadecanone at the concentrations released by postanthesis spikes. This study shows that the reduction of Z3-06:OAc, in the VOC mix, and possibly the increase in 2-tridecanone, are likely responsible for the reduction in oviposition on postanthesis wheat. These results elucidate for the first time the role of specific VOCs mediating S. mosellana oviposition in preanthesis and postanthesis wheat.

Extraction and spatio-temporal analysis of phenological dates of winter wheat in north Henan Province of China from 2003 to 2018 based on MODIS NDVI time series.

Monitoring of spatio-temporal changes in crop phenology is an important part of the remote sensing of agricultural ecosystems. In this study, the segment turning point method was utilised to determine several phenological dates of winter wheat in northern Henan from 2003 to 2018. The spatio-temporal variation characteristics of these main phenological dates were analyzed, and the effects of temperature and precipitation on phenological changes were investigated. The results showed that: (1) The segment turning point method had strong space-time adaptability, and the RMSE of extracted phenoloical dates of multi-stations in a single year or single station in multi-years was less than 10d. (2) Roughly bounded by 114°E, the trefoil stage, tillering stage, overwintering stage and rising stage of winter wheat in the west were earlier than those in the east of northern Henan in 2018. (3) From 2003 to 2018, the interannual change rates of the trefoil date, tillering date, overwintering date, rising date, booting date, and milky date of winter wheat were 6.92 d/10a, 4.36 d/10a, 0.74 d/10a, -0.1 d/10a, -3.97 d/10a and -2.91 d/10a, indicating the trend of delaying pre-winter phenology and advancing post-winter phenology. (4) The delay of pre-winter phenology and the advance of post-winter phenology of winter wheat were significantly related to the increase in growing season temperature. The results of the study should provide a basis for further understanding of the effects of climate change on winter wheat phenology and to provide a reference for remote sensing monitoring of winter wheat phenology.

Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving a yield prediction model with cumulative growing degree days (CGDD) and days from sowing (DFS) data. The study area was located in Mosonmagyaróvár, Hungary. A small-scale field trial in winter wheat was constructed as a randomized block design including Environmental: N-135.3, P2O5-77.5, K2O-0; Balance: N-135.1, P2O5-91, K2O-0; Genezis: N-135, P2O5-75, K2O-45; and Control: N, P, K 0 kg/ha. The crop growth was monitored every second week between April and June 2022 and 2023, respectively. NDVI measurements recorded by GreenSeeker were taken at three pre-defined GPS points for each plot; NDVI values based on the MicaSense camera Red and NIR bands were calculated for the same points. Results showed a significant difference (p ≤ 0.05) between the Control and treated areas by GreenSeeker measurements and Micasense-based calculated NDVI values throughout the growing season, except for the heading stage. At the heading stage, significant differences could be measured by GreenSeeker. However, remotely sensed images did not show significant differences between the treated and Control parcels. Nevertheless, both sensors were found suitable for yield prediction, and 226 DAS was the most appropriate date for predicting winter wheat’s yield in treated plots based on NDVI values and meteorological data.

Study on the impact of low‐temperature stress on winter wheat based on multi‐model coupling

AbstractCrop growth models, such as the WOrld FOod STudies (WOFOST) model, mimic the mechanistic processes involved in crop development, growth, and yield production. The accuracy of simulation is decreased in unfavorable low‐temperature settings because these models do not accurately represent crop response processes in low‐temperature stress. Enhancing the WOFOST crop growth model's accuracy in simulating crops' responses to cold temperatures is the aim of this work. Given its vulnerability to low temperatures, the inquiry uses winter wheat in Henan Province as a focal point. It integrates the WHEATGROW wheat phenology model with the Frost model of Lethal Temperature 50 (FROSTOL) inside the framework of the crop growth model. This link aims to improve simulation accuracy and supplement the model's mechanisms, particularly when it comes to the impact of low temperatures on crop development. The study uses Long Short‐Term Memory networks to build a yield model that integrates remote sensing data with information from simulated crop models. Under low temperatures, the leaf area index, total above ground biomass, and total weight of storage organs of the model WWF—which combines FROSTOL and WHEATGROW with WOFOST—show a considerable decline. It was discovered that there is a greater improvement in simulation accuracy of the linked model WWF relative to the WOFOST model in frost years than in normal years, based on a comparison analysis between typical frost years and normal years. To be more precise, the improvement is 8.03% in frost years and 1.98% in regular years. When all is said and done, the coupled model advances our knowledge of how winter wheat is impacted by low temperatures.

High-throughput field phenotyping reveals that selection in breeding has affected the phenology and temperature response of wheat in the stem elongation phase.

Crop growth and phenology are driven by seasonal changes in environmental variables, with temperature as one important factor. However, knowledge about genotype-specific temperature response and its influence on phenology is limited. Such information is fundamental to improve crop models and adapt selection strategies. We measured the increase in height of 352 European winter wheat varieties in 4 years to quantify phenology, and fitted an asymptotic temperature response model. The model used hourly fluctuations in temperature to parameterize the base temperature (Tmin), the temperature optimum (rmax), and the steepness (lrc) of growth responses. Our results show that higher Tmin and lrc relate to an earlier start and end of stem elongation. A higher rmax relates to an increased final height. Both final height and rmax decreased for varieties originating from the continental east of Europe towards the maritime west. A genome-wide association study (GWAS) indicated a quantitative inheritance and a large degree of independence among loci. Nevertheless, genomic prediction accuracies (GBLUPs) for Tmin and lrc were low (r≤0.32) compared with other traits (r≥0.59). As well as known, major genes related to vernalization, photoperiod, or dwarfing, the GWAS indicated additional, as yet unknown loci that dominate the temperature response.

A deep learning approach for deriving winter wheat phenology from optical and SAR time series at field level

Information on crop phenology is essential when aiming to better understand the impacts of climate and climate change, management practices, and environmental conditions on agricultural production. Today's novel optical and radar satellite data with increasing spatial and temporal resolution provide great opportunities to derive such information. However, so far, we largely lack methods that leverage this data to provide detailed information on crop phenology at the field level. We here propose a method based on dense time series from Sentinel-1, Sentinel-2, and Landsat 8 to detect the start of seven phenological stages of winter wheat from seeding to harvest. We built different feature sets from these input data and compared their performance for training a one-dimensional temporal U-Net. The model was evaluated using a comprehensive reference data set from a national phenology network covering 16,000 field observations from 2017 to 2020 for winter wheat in Germany and compared against a baseline set by a Random Forest model.Our results show that optical and radar data are differently well suited for the detection of the different stages due to their unique characteristics in signal processing. The combination of both data types showed the best results with 50.1% to 65.5% of phenological stages being predicted with an absolute error of less than six days. Especially late stages can be predicted well with, e.g., a coefficient of determination (R2) between 0.51 and 0.62 for harvest, while earlier stages like stem elongation remain a challenge (R2 between 0.06 and 0.28). Moreover, our results indicate that meteorological data have comparatively low explanatory potential for fine-scale phenological developments of winter wheat.Overall, our results demonstrate the potential of dense satellite image time series from Sentinel and Landsat sensor constellations in combination with the versatility of deep learning models for determining phenological timing.

Improving yield prediction based on spatio-temporal deep learning approaches for winter wheat: A case study in Jiangsu Province, China

Accurately and timely predicting yield and identifying its drivers are crucial for adjusting agricultural interventions, thereby responding to climate change and ensuring food security. Winter wheat phenology exhibited pronounced temporal and spatial variations at the county level in Jiangsu province. This study constructed a robust long short-term memory (LSTM) model that incorporates multiple data sources, including meteorological parameters, soil attributes, terrain characteristics, remote sensing data, and heterogeneous wheat phenology, aiming at forecasting the county-level wheat yield from year 2005 to 2020, with an emphasis on different precipitation year-types. The results demonstrated that the LSTM model could explain 71% of yield variations because of its ability to deal with time cumulative effects and high-dimensional data. Its performance outperformed that of machine learning algorithms, even in a normal year (RMSE = 0.26 t/ha) and an extreme year (RMSE = 0.29–0.33 t/ha). Time-series data (meteorological factors and vegetation indices), soil attributes, as well as topographic data that capture spatial and temporal heterogeneity, can achieve accurate wheat prediction approximately three months before harvest. Feature importance analysis showed meteorological factors, especially precipitation, are the most important drivers for yield prediction. This study developed a promising and scalable framework for spatio-temporal yield prediction by integrating available multi-source data to respond to climatic variations and provided insights for policymakers and farmers to adjust agricultural interventions.

Unlocking the secrets of wheat production: Evaluating phosphorus levels and seeding rates in alluvial soil conditions

A research study was conducted to determine the effect of different P levels and seeding rates on the growth, phenology, and yield of wheat. The experiment used a randomized complete block design with split plot arrangement and four replications. The main plots consisted of six seeding rates, ranging from 50 to 175 kg/ha, while the sub-plots received six different P levels, ranging from 50 to 200 kg/ha. In consecutive years, the study revealed significant effects of phosphorus levels, seeding rates, and their interaction on various growth characteristics and yield parameters of wheat. These included days to 50% heading and 50% maturity, leaf area index (LAI), leaf area duration (LAD), crop growth rate (CGR), net assimilation rate (NAR), and grain yield (GY). Phosphorus (P) level of 140 kg/ha resulted in the highest CGR, NAR, and GY in both years of the study. At a seeding density of 125 kg/ha, similar findings were observed, with the maximum CGR, NAR, and GY recorded. The highest interaction values for CGR, NAR, and GY were achieved with the combination of 140 kg P/ha and a seeding rate of 125 kg/ha. By utilizing a seed rate of 175 kg/ha and a P level of 200 kg/ha, the study revealed the highest values for LAI and LAD. Additionally, the maximum protein content was observed in treatments where seeds and P were applied at 50 and 200 kg/ha, respectively. In summary, the study provided conclusive evidence that the application of suitable seeding rates and P levels can yield significant improvements in the growth, phenology, and yield of wheat. The findings could be beneficial for farmers and researchers in improving wheat production.

Early-season and refined mapping of winter wheat based on phenology algorithms -a case of Shandong, China.

Winter wheat is one of the major food crops in China, and timely and effective early-season identification of winter wheat is crucial for crop yield estimation and food security. However, traditional winter wheat mapping is based on post-season identification, which has a lag and relies heavily on sample data. Early-season identification of winter wheat faces the main difficulties of weak remote sensing response of the vegetation signal at the early growth stage, difficulty of acquiring sample data on winter wheat in the current season in real time, interference of crops in the same period, and limited image resolution. In this study, an early-season refined mapping method with winter wheat phenology information as priori knowledge is developed based on the Google Earth Engine cloud platform by using Sentinel-2 time series data as the main data source; these data are automated and highly interpretable. The normalized differential phenology index (NDPI) is adopted to enhance the weak vegetation signal at the early growth stage of winter wheat, and two winter wheat phenology feature enhancement indices based on NDPI, namely, wheat phenology differential index (WPDI) and normalized differential wheat phenology index (NDWPI) are developed. To address the issue of " different objects with the same spectra characteristics" between winter wheat and garlic, a plastic mulched index (PMI) is established through quantitative spectral analysis based on the differences in early planting patterns between winter wheat and garlic. The identification accuracy of the method is 82.64% and 88.76% in the early overwintering and regreening periods, respectively, These results were consistent with official statistics (R2 = 0.96 and 0.98, respectively). Generalization analysis demonstrated the spatiotemporal transferability of the method across different years and regions. In conclusion, the proposed methodology can obtain highly precise spatial distribution and planting area information of winter wheat 4_6 months before harvest. It provides theoretical and methodological guidance for early crop identification and has good scientific research and application value.