小麦产量

… wheat grain yield; to examine the effects of climate, cultural and management practices and their associated effects on wheat grain yield; and to suggest possible future research needs. …

Four field experiments were conducted to investigate the effects of seed rate on yield and quality of wheat. Despite some small and inconsistent effects of seed rate on radiation-use efficiency and harvest index, the responses of PAR interception, above-ground biomass and grain yield generally followed similar asymptotic increases as seed rate increased. In one experiment, when nitrogen fertilizer was withheld, biomass and grain yields did not respond to increases in seed rate despite increases in PAR interception. In one experiment, grain yield followed a parabolic response to seed rate with apparent reductions in yield at very high seed rates. Plants compensated for low population densities by increased production and survival of tillers and, to a lesser extent, increased grain numbers per ear. Net tiller production continued until the main stems flowered or later. Effects of seed rate on grain specific weight and thousand grain weight were small and inconsistent. Hagberg falling number increased linearly with seed rate in three experiments, associated with quicker maturation of the crop. Grain protein concentration declined with increase in sowing rate according to linear divided by linear or linear plus exponential models depending on whether the grain yield response was asymptotic or parabolic. Discolouration of the grain with blackpoint increased with seed rate in the most susceptible cultivar, namely Hereward. The economic consequences of these effects on yield and quality are discussed.

Abstract Nutrient management can reduce crop yield gaps, but available literature is mostly restricted to studies limited in time, geography, or in the number of nutrients evaluated. Our objective was to synthesize long-term experiments evaluating wheat (Triticum aestivum L.) yield and grain-N concentration (GNC) response to N, P, and K fertilizer rates and their interactions. We used data from three long-term (1966–2016) experiments conducted in Oklahoma (USA) comprising 155 site-years for yield (n = 8035) and 90 site-years for GNC (n = 4580). The last year of the experiments was the baseline to de-trend yield and GNC data. We first explored relationships between grain yield and GNC, grain N removal, apparent recovery of applied N in the grain (N recovery), and N-use efficiency (NUE) as affected by the presence and rate of N, P, and K across the entire dataset. Then, we subdivided the dataset into yield-environments based on the different data quartiles, and analyzed it using descriptive statistics, multi-level modeling, differences from the control, and conditional inference trees. Our main findings were: i) wheat yield was negatively related to GNC, but positively associated with N removal, N recovery, and NUE. ii) The co-application of P and, to a lesser extent, K, increased N removal and NUE but decreased GNC. iii) The proportion of variability in yield and GNC explained by fertilizer management increased with an increase in yield-environment. iv) Wheat yield response to N and to P were typically quadratic, although response to P was restricted to high yielding environments. v) Wheat GNC increased linearly with increases in N rate, but decreased with increases in P and K rate. vi) Conditional inference trees suggested that the co-application of P and K improved yields but decreased GNC. The co-application of P and K can increased wheat yield, N removal, and NUE, but the increases in yield were greater than those in N removal, thus decreasing GNC.

… wheat grain yield under the Mediterranean-like climatic conditions of the Inland PNW. The study had three objectives. These were to: (i) assess available water and wheat grain yield …

… of plant development on wheat grain yield. Two cultivars were … grain yield at maturity. Delaying the final forage harvest resulted in a significant, progressive reduction in grain yield …

… Therefore, it is important for us to increase the grain yield … grain yield for a given crop N content. In this article, I will briefly review photosynthetic performance and yield in rice and wheat …

… Some of this work has been directed at estimating N uptake of winter wheat during early … grain yield. This study focuses on predicting the final yield and/or grain protein of winter wheat at …

… assessed stability of yield and yield components to moisture stress. This study evaluated the stability of spring wheat quality parameters relative to the stability of grain yield and its …

… grain yield of wheat cultivars when placed under dual-purpose (September-sown and grazed) or grain-… Regression analysis showed that grazed wheat yield increased 0.93 kg ha −1 for …

… have lead to decreased yield and deteriorated quality of wheat grains due to salinity in the present study. Phosphorus concentration in shoot, root and grain were decreased under …

… that yield well under drought. The objective of this study was to compare various moisture regimes as evaluation environments for wheat (… bread wheat genotypes selected for high yield …

… of these factors to wheat yield, grain protein content (GPC), … The results revealed a significant improvement in grain yield (… outcomes in terms of yield, GPC, and WP in wheat systems. It …

… region, more than the continuous wheat (CW) rotation system… tillage (CT) on wheat growth and water use efficiency (WUE) … During 1995, similar grain yields were obtained with both NT …

… The grain yield of winter wheat varieties in dry years is generally positively correlated with root … Our evaluation of three generations of winter wheat root size and grain yield is shown in …

… It is now generally believed that wheat grain yield is a function and integration of all these processes, each of which can be altered by the climatic conditions during the growing season …

… This will require a better understanding of how to maximize dry matter partitioning to reproductive structures without under-investing in roots, stems and leaves on which both grain yield …

… , the grain leaf ratio, computed by dividing final grain yield by … measuring the extent to which grain yield was source-limited, … surfaces largely determined grain yield. There were, however…

Accurate predictions of wheat yields are essential to farmers’production plans and to the international trade in wheat. However, only poor approximations of the productivity of wheat crops in China can be obtained using traditional linear regression models based on vegetation indices and observations of the yield. In this study, Sentinel-2 (multispectral data) and ZY-1 02D (hyperspectral data) were used together with 15709 gridded yield data (with a resolution of 5 m × 5 m) to predict the winter wheat yield. These estimates were based on four mainstream data-driven approaches: Long Short-Term Memory (LSTM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Regression (SVR). The method that gave the best estimate of the winter wheat yield was determined, and the accuracy of the estimates based on multispectral and hyperspectral data were compared. The results showed that the LSTM model, for which the RMSE of the estimates was 0.201 t/ha, performed better than the RF (RMSE = 0.260 t/ha), GBDT (RMSE = 0.306 t/ha), and SVR (RMSE = 0.489 t/ha) methods. The estimates based on the ZY-1 02D hyperspectral data were more accurate than those based on the 30-m Sentinel-2 data: RMSE = 0.237 t/ha for the ZY-1 02D data, which is about a 5% improvement on the RSME of 0.307 t/ha for the 30-m Sentinel-2 data. However, the 10-m Sentinel-2 data performed even better, giving an RMSE of 0.219 t/ha. In addition, it was found that the greenness vegetation index SR (simple ratio index) outperformed the traditional vegetation indices. The results highlight the potential of the shortwave infrared bands to replace the visible and near-infrared bands for predicting crop yields Our study demonstrates the advantages of the deep learning method LSTM over machine learning methods in terms of its ability to make accurate estimates of the winter wheat yield.

… This paper shows the application of remote sensing data for estimating winter wheat yield in … winter wheat (WW) yield. A strong correlation was found between winter wheat yield and …

… impact estimated in these studies. In this letter, we compared three largely independent assessment methods used to estimate temperature impacts on wheat yields: … Wheat yields were …

… wheat yield estimation. It underscores the heading stage as a pivotal period influencing yield … (2202.4 nm) spectral bands in yield estimation. Moreover, this model adeptly overcame the …

Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the results of the UAV images, the LAI, LDM, and yield data were obtained by destructive sampling. We extracted the wheat canopy reflectance and selected the best vegetation index for monitoring growth and predicting yield. Simple linear regression (LR), multiple linear regression (MLR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural network (ANN), and random forest (RF) modeling methods were used to construct a model for wheat yield estimation. The results show that the multi-spectral camera mounted on the multi-rotor UAV has a broad application prospect in crop growth index monitoring and yield estimation. The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM. Machine learning methods (i.e., PLSR, ANN, and RF) performed better for predicting wheat yield. The RF model constructed by normalized difference vegetation index (NDVI) at the jointing stage, heading stage, flowering stage, and filling stage was the optimal wheat yield estimation model in this study, with an R2 of 0.78 and relative root mean square error (RRMSE) of 0.1030. The results provide a theoretical basis for monitoring crop growth with a multi-rotor UAV platform and explore a technical method for improving the precision of yield estimation.

… A good predicted yield data of winter wheat could be got about … wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation…

… to estimate and forecast the wheat yield over Europe. The products were used together with official wheat yield … ) and a limited number of wheat yield observations. The model was run in …

… of wheat conditions. Next, the current methodology was applied to estimate wheat yield… The results obtained showed the high accuracy of the method in estimating wheat yield at …

In Mongolia, the monitoring and estimation of spring wheat yield at the regional and national levels are key issues for the agricultural policy and food management as well as for the economy and society as a whole. The remote sensing data and technique have been widely used for the estimation of crop yield and production in the world. For the current research, nine remote sensing indices were tested that include normalized difference drought index (NDDI), normalized difference water index (NDWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), normalized multi-band drought index (NMDI), visible and shortwave infrared drought index (VSDI), and vegetation supply water index (VSWI). These nine indices derived from MODIS/Terra satellite have so far not been used for crop yield prediction in Mongolia. The primary objective of this study was to determine the best remote sensing indices in order to develop an estimation model for spring wheat yield using correlation and regression method. The spring wheat yield data from the ground measurements of eight meteorological stations in Darkhan and Selenge provinces from 2000 to 2017 have been used. The data were collected during the period of the growing season (June–August). Based on the analysis, we constructed six models for spring wheat yield estimation. The results showed that the range of the root-mean-square error (RMSE) values of estimated spring wheat yield was between 4.1 (100 kg ha−1) to 4.8 (100 kg ha−1), respectively. The range of the mean absolute error (MAE) values was between 3.3 to 3.8 and the index of agreement (d) values was between 0.74 to 0.84, respectively. The conclusion was that the best model would be (R2 = 0.55) based on NDWI, VSDI, and NDVI out of the nine indices and could serve as the most effective predictor and reliable remote sensing indices for monitoring the spring wheat yield in the northern part of Mongolia. Our results showed that the best timing of yield prediction for spring wheat was around the end of June and the beginning of July, which is the flowering stage of spring wheat in this study area. This means an accurate yield prediction for spring wheat can be achieved two months before the harvest time using the regression model.

… yield estimates as compared to those derived from just agrometeorological data. This study was performed to assess the improvement on wheat yield estimation … main wheat production …

Timely and accurate wheat yield forecasts using Unmanned Aircraft Vehicles (UAV) are crucial for crop management decisions, food security, and ensuring the sustainability of agriculture worldwide. While traditional machine learning algorithms have already been used in crop yield modelling, previous research used machine learning algorithms with default parameters and did not take into account the complex, non-linear relationships between model variables. Especially, the combination of vegetation indices, soil properties, solar radiation, and wheat height at the field estimation has not been deeply analysed in scientific literature. We present a machine learning based wheat yield estimation model using comprehensive UAV datasets with the implementation of hyperparameter tuning to improve model performance. The performance of the models before and after optimisations was measured using the metrics RMSE, MAE and R2, and the results showed that the models improved after tuning. Furthermore, we find that the Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models outperformed other examined models. Furthermore, a non-parametric Friedman test with a Nemenyi post-hoc test indicates that the best-performing algorithms for wheat yield estimation and prediction are RF and XGBoost models. In the final step, we utilised a SHapley Additive exPlanations approach to identify the direct impact of each input variable on the yield estimation model. Among the input variables, only the Red-Edge Chlorophyll Index, the Normalised Difference Red-Edge Index and wheat height were found to be of high explanatory power in predicting wheat yield. The optimised model is 7–12% more accurate in estimating wheat yields than traditional linear models.

… large-scale yield prediction. This study focuses on China, the world's largest wheat producer, to develop an adaptable method for estimating yields in major wheat-growing regions. We …

For estimation of grain yield in wheat, Normalized Difference Vegetation Index (NDVI) is considered as a potential screening tool. Field experiments were conducted to scrutinize the response of NDVI to yield behavior of different wheat cultivars and nitrogen fertilization at agronomic research area, University of Agriculture Faisalabad (UAF) during the two years 2008-09 and 2009-10. For recording the value of NDVI, Green seeker (Handheld-505) was used. Split plot design was used as experimental model in, keeping four nitrogen rates (N1 = 0 kg ha−1, N2 = 55 kg ha−1, N3 = 110 kg ha−1, and N4 = 220 kg ha−1) in main plots and ten wheat cultivars (Bakkhar-2001, Chakwal-50, Chakwal-97, Faisalabad-2008, GA-2002, Inqlab-91, Lasani-2008, Miraj-2008, Sahar-2006, and Shafaq-2006) in subplots with four replications. Impact of nitrogen and difference between cultivars were forecasted through NDVI. The results suggested that nitrogen treatment N4 (220 kg ha−1) and cultivar Faisalabad-2008 gave maximum NDVI value (0.85) at grain filling stage among all treatments. The correlation among NDVI at booting, grain filling, and maturity stages with grain yield was positive (R 2 = 0.90; R 2 = 0.90; R 2 = 0.95), respectively. So, booting, grain filling, and maturity can be good depictive stages during mid and later growth stages of wheat crop under agroclimatic conditions of Faisalabad and under similar other wheat growing environments in the country.

… estimating wheat yield at regional and farm scales in Montana for the years 1989–1997. Both regions and farms showed strong relationships between wheat yields … yield estimates. At …

… the coefficients a, b, and c, and the regression estimates of county yields, Y. The improvement of ~r versus 0 as an estimator of wheat yield indicates the degree of improvement in yield …

… method and prediction stage for wheat yield estimation, (ii) … yield estimation, and (iii) elucidate the contribution of time-series data fusion and 3D spatial information to yield estimation…

Introduction In the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation. Methods Solar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring. However, its effectiveness for yield estimation under various data fusion conditions has not been thoroughly explored. This study developed a deep learning model named BO-CNN-BiLSTM (BCBL), combining the feature extraction capabilities of a convolutional neural network (1DCNN) with the time-series memory advantages of a bidirectional long short-term memory network (BiLSTM). The Bayesian Optimization (BOM) method was employed to determine the optimal hyperparameters for model parameter optimization. Traditional remote sensing variables (TS), such as the Enhanced Vegetation Index (EVI) and Leaf Area Index (LAI), were fused with the SIF and climate data to estimate the winter wheat yields in Henan Province, exploring the SIF’s estimation capabilities using various datasets. Results and Discussion The results demonstrated that the BCBL model, integrating TS, climate, and SIF data, outperformed other models (e.g., LSTM, Transformer, RF, and XGBoost) in the estimation accuracy, with R²=0.81, RMSE=616.99 kg/ha, and MRE=7.14%. Stepwise sensitivity analysis revealed that the BCBL model reliably identified the critical stage of winter wheat yield formation (early March to early May) and achieved high yield estimation accuracy approximately 25 d before harvest. Furthermore, the BCBL model exhibited strong stability and generalization across different climatic conditions. Conclusion Thus, the BCBL model combined with SIF data can offer reliable winter wheat yield estimates, hold significant potential for application, and provide valuable insights for agricultural policymaking and field management.

Abstract Crop yield estimation at regional scale using crop model is generally subjected to large uncertainties from insufficient spatial information on heterogeneous growth environment and agronomic management practices. To solve this problem, we assimilated crop phenology and leaf area index (LAI) derived from remote sensing into a crop model (MCWLA-Wheat) to improve its reliability in estimating winter wheat yields at regional scale. Since the LAI magnitude was obviously underestimated however its spatial pattern was relatively well captured by remote sensing, we developed a novel spatial assimilation scheme that assimilated the spatial differences instead of the absolute values of LAI into crop model. Firstly, we retrieved the information of critical development stages of winter wheat from remote sensing data to adjust the simulation of phenology by MCWLA-Wheat model; then the spatial differences of LAI derived from remote sensing were assimilated into the MCWLA-Wheat model using a kind of constant gain Kalman Filter algorithm to improve the ability of the model in estimating winter wheat LAI and yields at regional scale in the North China Plain. This assimilation scheme extracted effective information from remote sensing LAI and meanwhile abandoned the information with obvious errors, ensuring that the assimilation variables could be close to the reality. It avoids the requirement for correction of the LAI derived from remote sensing using other high-quality ancillary data from field measurements. Using this assimilation scheme, the performance of crop model improved substantially. It successfully produced more accurate yield estimates at regional scale during the period of 2001–2008 (mean R 2  = 0.42, RMSE = 737/ha) than those without assimilation (mean R 2  = 0.26, RMSE = 1012 kg/ha) and those directly assimilating the absolute LAI values derived from remote sensing (mean R 2  = 0.30, RMSE = 1257/ha). Our findings demonstrated a reliable and promising assimilation scheme for improving yield estimation of crop model at regional scale with low data requirement.

Water stress is an important factor to be considered when using crop growth models for crop yields estimation. In this article, we propose a simple algorithm for yield estimate (SAFY) V model to estimate yields of winter wheat by integrating the time-series remotely sensed drought monitoring index, vegetation temperature condition index (VTCI), into SAFY model, and the fixed effective light-use efficiency parameter (elue0) in the SAFY model is modified to a new varied parameter (E) in the SAFY-V model. The parameter E can accurately describe the changes of water stress at the four growth stages of winter wheat and has a high correlation with the field measured yields (R2 values are 0.28, 0.34, 0.31 and 0.31, respectively). The SAFY-V model integrating the time series leaf area index (LAIs) and VTCIs which has the highest accuracy on dry aerial mass estimation compared with the SAFY model and SAFY-WB model (a combination of the SAFY model and water balance model), can better alleviate the yield underestimation and overestimation and greatly improve the estimation accuracy of winter wheat yield especially in rain-fed farmlands (R2 = 0.48, MAE = 1.05 t/ha and NMAE = 15.6%), and the accuracy of winter wheat yields estimates at the county scale was also satisfactory (R2 = 0.49, MAE = 0.73 t/ha and NMAE = 16.1% for five years). The proposed SAFY-V model of this article has few model parameters and low computational cost, which provides a significant reference for crop yield estimation at a regional scale.

… winter wheat yields at 1-km resolution for pixels with wheat … yield estimates were compared with official statistical yields. … more accurate estimates of regional winter wheat yield (R 2 = …

… These contrasting yield formation strategies are probably a consequence of the different … gradual changes in yield components occurred during the movement of durum wheat from east …

… In this study, we used a model of yield formation that included several submodels to calculate the potential value of a yield component from the observed value of the previous …

Experiments in three dry years, 1993/94, 1994/95 and 1995/96, on a medium sand at ADAS Gleadthorpe, England, tested responses of six winter wheat cultivars to irrigation of dry-matter growth, partitioning of dry matter to leaf, stem and ear throughout the season, and to grain at final harvest. Cultivars (Haven, Maris Huntsman, Mercia, Rialto, Riband and Soissons) were selected for contrasts in flowering date and stem soluble carbohydrate. Maximum soil moisture deficit (SMD) exceeded 140 mm in all years, with large deficits (>75 mm) from early June in 1994 and from May in 1995 and 1996. The main effects of drought on partitioning of biomass were for a decrease in the proportion of the crop as lamina in the pre-flowering period, and then earlier retranslocation of stem reserves to grains during the first half of grain filling. Restricted water availability decreased grain yield by 1·83 t/ha in 1994 (P<0·05), and with more prolonged droughts, by 3·06 t/ha in 1995 (P<0·001) and by 4·55 t/ha in 1996 (P<0·001). Averaged over the three years, grain yield responses of the six cultivars differed significantly (P<0·05). Rialto and Mercia lost only 2·8 t/ha compared with Riband and Haven which lost 3·5 t/ha. Losses for Soissons and Maris Huntsman were intermediate. In the two years with prolonged drought, the biomass depression was on average greater for Haven (6·0 t/ha) than for Maris Huntsman (4·2 t/ha) (P<0·05). Thus, the grain yield sensitivity of Haven to drought derived, in part, from a sensitivity of biomass growth to drought. Harvest index (HI; ratio of grain to above-ground dry matter at harvest) responses of the six cultivars to irrigation also differed (P<0·05) and contributed to the yield responses. The smallest decrease in HI of the six cultivars with restricted water availability was shown by Rialto (−0·033); this partially explained the drought resistance for this cultivar. The largest decrease was for Maris Huntsman (−0·072). The cultivars differed in flowering dates by up to 9 days but these were poorly correlated with grain yield responses to irrigation. Stem soluble carbohydrate at flowering varied amongst cultivars from 220 to 300 g/m2 in the unirrigated crop; greater accumulation appeared to be associated with better maintenance of HI under drought. It is concluded that high stem-soluble carbohydrate reserves could be used to improve drought resistance in the UK's temperate climate, but that early flowering seems less likely to be useful.

From a perspective of food security, the agricultural sector worldwide has a responsibility to improve crop yields. Wheat yield in Japan is about half that of high‐yielding countries in Northwest Europe. Explanations offered so far—such as high temperatures and a rainy summer season shortening wheat's growth period, or comparatively underdeveloped breeding and cultivation techniques—remain speculative. This lack of clarity risks misdirecting research efforts on wheat cultivation in Japan and possibly other parts of the world. To address the issue, the present study focused on the efficiency of yield formation, rather than yield itself, across Japan and Northwest Europe. The efficiency of yield formation, derived from the division of actual yield by sunshine hours during the specific growth period from ear emergence to maturity, was compared between two geographical regions while factoring in climate variables. Despite the large yield difference, there was no significant difference in the efficiency of yield formation of wheat between the two regions. This indicates that Japan's low yield is largely due to climatic adversity for wheat, that is, high temperature, high precipitation and short sunshine hours during the critical growth phase for yield formation of the crop. The implication is that improvements in breeding and cultivation techniques alone are not likely to significantly increase wheat yield in Japan. A fruitful direction for future research endeavors in wheat production in monsoon Asia was discussed.

… yield formation besides light use efficiency (P = 0.073) statistically significant differences regarding the cultivars can be observed. Our results suggest that grain yield … the final yield which …

… physiological and biochemical aspects of the wheat crop (Zheng et al.… wheat yield formation process. The effects of enhanced UV-B radiation on yield and yield formation of winter wheat …

Clarifying factors that underpinning the variation in wheat yield components between high and middle soil fertility fields is critical to increase grain production and narrow yield gap for smallholder farming systems in the Huang–Huai–Hai Plain (3HP), which characterized by a large variation in soil fertility. Two-year field experiments were conducted to investigate wheat tillering, leaf photosynthesis, and grain filling characteristics in different soil fertility fields: high soil fertility field (HF) and middle soil fertility field (MF). Results showed that the spike formation rate in HF was 12.7%–13.0% higher than that in MF, leading to an 18.0%–19.8% increase in spike number. In addition, HF improved canopy light interception and leaf photosynthesis characteristics after anthesis and delayed leaf senescence, contributing to the increase in both the active grain filling period and grain filling rate. This resulted in a higher 1,000 grain weight in HF, which was 8.2%–8.3% higher than that in MF. Compared to MF, HF obtained higher yields at 9,840 kg ha−1 in 2017/18 and 11,462 kg ha−1 in 2018/19, respectively. In summary, higher spike number and 1,000-grain weight, which were mediated by spike-formation rate, maximization of light interception and improved leaf photosynthesis. These results would have important implications for narrowing yield gap between MF and HF in the 3HP.

… and PGRs on wheat morphogenesis and yield formation was … may reduce grain yield by inhibiting formation and development … leaves, and the highest grain yield, indicating that more N …

The pattern of grain production of a winter wheat crop and the effect of plant density and time of tiller emergence on grain yield/ear were studied. At harvest, ear size and ear components were ascertained and were discussed in relation to ear growth and ear development during the prefloral and postfloral growing period. Detailed information was obtained on the productivity of ear-bearing tillers and their contribution to final grain yield. Shoot productivity decreased in denser crops; ears were smaller because spikelet differentiation, grain set and grain filling were inadequate. The date that the tiller emerged largely determined its subsequent grain yield. With later tiller initiation and emergence fewer ears were produced. Moreover, these ears were smaller because spikelet initiation, spikelet differentiation, grain set and grain filling were reduced. At low and moderate plant densities, the grain yield of the early-emerged tillers only slightly lagged behind that of main shoots and max. grain yield could be achieved at moderate plant densities. It was concluded that in cereal farming, high and stable grain yields are aims to be achieved. These can be best achieved by having moderate plant densities and applying correct treatments for good crop growth. (Abstract retrieved from CAB Abstracts by CABI’s permission)

… yield formation in cereals, little information exists on the use of this technique in durum wheat, … it (i) provides an overall view on grain yield formation under two temperature and moisture …

Winter wheat cv. Caribo (1979) or Arminda and Okapi (1980) was provided with an additional N dressing at different stages between tillering and ear emergence after a basal N dose applied at the onset of tillering. The effect of N on ear formation depended greatly on the growth stage at the time of N application. Max. effects on tiller formation and spikelet initiation were achieved when additional N was supplied at the beginning of tillering; on ear number when N was supplied at the onset of stem elongation; on the numbers of fertile spikelets, grains/fertile spikelet and grains/ear when N was applied during stem elongation until flag leaf emergence and on single grain wt. when N was applied at ear emergence. Variations in 1000-grain wt. were small, therefore grain yield/ear as well as yield/unit area was largely determined by grain number. Main shoots outyielded ear-bearing tillers because of a higher grain number. In ear-bearing tillers, grain yield largely depended on grain number, being highest in the older tillers. Grain formation of ear-bearing tillers was more strongly affected by the time of additional N application than that of main shoots. Top-dressings of N applied during stem elongation increased the grain number of ear-bearing tillers considerably, because both the number of fertile spikelets and the grain number/fertile spikelet were higher. In the young late-appeared tillers, the opt. time to apply additional N for grain set shifted to later stages of development. (Abstract retrieved from CAB Abstracts by CABI’s permission)

Abstract Although the effect of tillage practices on crops has been well studied, the systematic effect of these practices on the yield formation process of wheat in rainfed regions is often poorly reported. Here, four tillage practices, namely, strip rotary tillage (SR), strip rotary tillage after subsoiling (SRS), rotary tillage (R) and rotary tillage after subsoiling (RS), were performed during 3 wheat–growing seasons to study the effect of tillage practices on the yield formation process and the physiological mechanism in rainfed wheat. SRS and SR reduced the tiller numbers but increased the percentage of earring tillers. SRS and SR produced the spike numbers similar to those produced by RS and R but increased the grain numbers per spike by increasing the number of grains per spikelet. SRS reduced the evapotranspiration (ET) during the early–filling stage and increased the ET and water consumption ratio during the mid– and late–filling stages. Because of increased water consumption, the flag leaf water potential by SRS was improved during the late–filling stage; photosynthetic rate and superoxide dismutase activity also improved. Accompanied by the improvement in physiological characteristics, the post–anthesis dry matter accumulation by SRS increased significantly. The average grain yield by SRS in the three growing seasons was 6.0 %, 13.4 % and 7.0 % higher than those by SR, R and RS, respectively. The yield–increasing effect of subsoiling once could last for 3 years but the growth rate on the third year decreased from 8.6 % to 3.2 % in SRS, and decreased from 6.0 %–4.2 % in RS.

… intensity and duration on grain yield components during these sensitive … yield losses in response to varying heat intensity and duration during different developmental phases of wheat …

A field experiment with the winter wheat cultivar Donata was carried out on a fine-textured river clay soil in 1978. The rates of nitrogen dressing ranged from 0 to 160 kg N per ha and …

… yield of wheat. The last three years were really different from the viewpoint of wheat production and this was well presented by the yields … varieties gave the highest yields (the average of …

… Peculiarities of forming the yield of durum spring wheat depending on the predecessor and the main tillage. Collection of scientific papers of the Institute of Bioenergy Crops and Sugar …

… In this Review, we examine yield responses to warmer … crops (wheat, maize, millet, sorghum and rice). Elevated CO 2 can have a compensatory effect on crop yield for C3 crops (wheat …

… of crop yield to straw return are inconsistent from individual studies in wheat-maize cropping … the effect of straw return on the yield in the wheat-maize cropping system varied with climate …

From 1948 to the present, wheat (Triticum aestivum L.) yields in the UK have increased by an average of 110 kg ha −1 each year. This rate of increase has been at least maintained in …

… and Lepidium sativum growth and wheat yield losses. Season-long weed-free and crop-free treatments were also established to compare wheat yield and weed growth, respectively. …

SUMMARYThe potential yield of the best varieties of winter wheat currently grown in eastern England is estimated to be in the range 12–14 t/ha (15% moisture, 10% protein, freshweight basis).Increases in the leaf area index at anthesis, in the duration of the grain-filling period and in the light-saturated rate of photosynthesis of the leaves, slower decline in their photosynthetic capacity, and decreases in the respiratory losses of assimilated carbon would increase this potential yield. Such changes might be brought about by breeding or by improved agronomic practices.The grain growth rates predicted by several published simulation models are shown to be in reasonable agreement with each other.

Wheat is considered to be a strategic crop for achieving food security. Wherefore, one of the current objectives of today’s agriculture is to ensure a consistent and sustainable yield of this particular crop while mitigating its environmental footprint. However, along with the genetic potential of varieties, agricultural practices play a key role in ensuring a high and stable yield of wheat. Under changing climatic conditions, new eco-friendly practices were adopted in the wheat farming system in recent decades. In this review, a large number of peer-reviewed articles have been screened during the last 15 years to evaluate the potential of some environmentally friendly agricultural practices such as tillage system, biological crop protection, crop rotation, intercropping systems, and the integration of resistant varieties in achieving a high and stable wheat yield. The present investigation unveiled that embracing eco-friendly agricultural methods in the wheat farming system holds the potential to engender high and sustainable wheat yields, contingent upon a normative strategy that comprehensively addresses multiple factors. These include the intrinsic attributes of the grown wheat cultivars, plant nutritional parameters, soil agrochemical characteristics, and specific climatic conditions. Further in-depth investigations under field conditions are necessary to help in the discernment of appropriate environmentally agricultural techniques that can efficaciously optimize the yield potential of the different cultivated varieties.

… We examined the trends in crop yields for four key global crops: maize, rice, wheat and … We find that the world’s maize, rice, wheat and soybean crops are continuing to experience yield …

Abstract Little is known about the effects of different fertilizers and manure use on the yield stability and sustainability of crop in a rice-wheat cropping system. Therefore, a 34-year field experiment (conducted from 1982 to 2015) was used to evaluate the effect of continuous application of inorganic fertilizers and organic manure, supplied at different combinations, on the stability and sustainability of rice and wheat yields. Eight treatments consisted of unfertilized control (CK), inorganic fertilizers (N, NP, NPK) and organic manure (M) either alone or in combination (MN, MNP, MNPK). There was no significant trend of rice grain yield over time for any of the treatments. Wheat yield had an increasing tendency in all the fertilization treatments, including the unfertilized control. Organic manure in combination with chemical fertilizer supported high rice and wheat yields and sustainable yield index (SYI), with decreased coefficient of variance (CV) of rice and wheat yields. The SYI value clearly indicates that rice yield was more sustainable than wheat yield. In conclusion, the combined use of both organic manure and inorganic fertilizer can improve not only crop grain yield but also yield stability and sustainability.

While an adequate supply of food can be achieved at present for the current global population, sustaining this into the future will be difficult in the face of a steadily increasing population, increased wealth and a diminishing availability of fertile land and water for agriculture. This problem will be compounded by the new uses of agricultural products, for example, as biofuels. Wheat alone provides ≥20% of the calories and the protein for the world's population, and the value and need to increase the production is recognized widely. Currently, the world average wheat yield is around 3 t/ha but there is considerable variation between countries, with region-specific factors limiting yield, each requiring individual solutions. Delivering increased yields in any situation is a complex challenge that is unlikely to be solved by single approaches and a multidisciplinary integrated approach to crop improvement is required. There are three specific major challenges: increasing yield potential, protecting yield potential, and increasing resource use efficiency to ensure sustainability. Since the green revolution, yields at the farm gate have stagnated in many countries, or are increasing at less than half the rate required to meet the projected demand. In some countries, large gains can still be achieved by improvements in agronomy, but in many others the yield gains will only be achieved by further genetic improvement. In this overview, the problems and potential solutions for increased wheat yields are discussed, in the context of specific geographic regions, with a particular emphasis on China. The importance and the prospects for improvement of individual traits are presented. It is concluded that there are opportunities for yield increase but a major challenge will be avoiding a simultaneous increase in resource requirements.

… Wheat is the dominant crop and the largest irrigation water user in Iran; hence, understanding of the crop yield–water relations in wheat … we modeled irrigated and rainfed wheat yield (Y) …

… However, if important C 3 crop plants, such as wheat, possessed a C 4 mechanism of carbon concentration, then very large increases in yield would be expected. The complexities of C …

ABSTRACT Crop yield prediction is an important aspect of agriculture. The timely and accurate crop yield predictions can be of great help for policy makers and farmers in planning and decision making. Generally, statistical models are employed to predict the crop yield which is time consuming and tedious. Emerging trends of deep learning and machine learning has come up as a major breakthrough in the arena. Deep learning models have the inherent ability to perform feature extraction in large dataset thus more suitable for predictions. In this paper, a deep learning-based Recurrent Neural Network (RNN) model is employed to predict wheat crop yield of northern region of India. The present study also employed LSTM to unravel the vanishing gradient problem inherent in RNN model. Experiments were conducted using 43 years benchmark dataset and proposed model results were compared with three machine learning models. Evidently, the results obtained from RNN-LSTM model(RMSE: 147.12,MAE: 60.50), Artificial Neural Network(RMSE: 732.14,MAE: 623.13), Random Forest (RMSE: 540.88, MAE: 449.36) and Multivariate Linear Regression (RMSE: 915.64,MAE: 796.07), proved the efficacy of model. Also, predicted crop yield values were found to be more close to true values for RNN-LSTM model proving efficiency of the proposed work.

… used nowadays to forecast the crop yield at the national/state/… to influence planning, crop planting recommendations and … are considered for the wheat crop yield prediction; these are …

Episodes of water shortage occur in most agricultural regions of the world. Their durations and intensities increase, and their seasonal timing alters with changing climate. During the ontogenic cycle of crop plants, each development stage, such as seed germination, seedling establishment, vegetative root and shoot growth, flowering, pollination and seed and fruit development, is specifically sensitive to dehydration. Desiccation threatens yield and leads to specific patterns, depending on the type of crop plant and the harvested plant parts, e.g. leafy vegetables, tubers, tap roots or fruits. This review summarizes the effects of drought stress on crop plants and relates the dehydration-dependent yield penalty to the harvested organ and tissue. The control of shoot transpiration and the reorganization of root architecture are of core importance for maintaining proper plant water relationships. Upon dehydration, the provision and partitioning of assimilates and the uptake and distribution of nutrients define remaining growth activity. Domestication of crops by selection for high yield under high input has restricted the genetic repertoire for achieving drought stress tolerance. Introgression of suitable alleles from wild relatives into commercial cultivars might improve the ability to grow with less water. Future research activities should focus more on field studies in order to generate more realistic improvements to crops. Robotic field phenotyping should be integrated into genetic mapping for the identification of relevant traits.

… number of interactions between the crop and its environment. … This paper describes a model of the wheat crop developed as a … determinant of final yield since potential crop yield (PY, …

… wheat (Triticum aestivum L.) yield and CWP at a grid resolution of 30′ on the land surface. A comparison between simulated yields and FAO statistical yields … wheat, irrigated wheat has …

This review focuses on recent advances in some key areas of wheat physiology, namely phasic development, determination of potential yield and water-limited potential yield, tolerance to some other abiotic stresses (aluminium, salt, heat shock), and simulation modelling. Applications of the new knowledge to breeding and crop agronomy are emphasized. The linking of relatively simple traits like time to flowering, and aluminium and salt tolerance, in each case to a small number of genes, is being greatly facilitated by the development of molecular gene markers, and there is some progress on the functional basis of these links, and likely application in breeding. However with more complex crop features like potential yield, progress at the gene level is negligible, and even that at the level of the physiology of seemingly important component traits (e.g., grain number, grain weight, soil water extraction, sensitivity to water shortage at meiosis) is patchy and generally slow although a few more heritable traits (e.g. carbon isotope discrimination, coleoptile length) are seeing application. This is despite the advent of smart tools for molecular analysis and for phenotyping, and the move to study genetic variation in soundly-constituted populations. Exploring the functional genomics of traits has a poor record of application; while trait validation in breeding appears underinvested. Simulation modeling is helping to unravel G × E interaction for yield, and is beginning to explore genetic variation in traits in this context, but adequate validation is often lacking. Simulation modelling to project agronomic options over time is, however, more successful, and has become an essential tool, probably because less uncertainty surrounds the influence of variable water and climate on the performance of a given cultivar. It is the ever-increasing complexity we are seeing with genetic variation which remains the greatest challenge for modelling, molecular biology, and indeed physiology, as they all seek to progress yield at a rate greater than empirical breeding is achieving.

… on the wheat yield estimation in the Indian Wheat Belt, where concerns of yield stagnation … We estimated district-level crop yield using as input a set of time series of meteorological and …

… , sensors, and standard features in wheat yield … wheat yield using time series data, with MODIS and Landsat datasets found to be the most widely applied satellite data for wheat yield …

… of nine climate variables, three remote sensing-derived metrics, and three … wheat yield based on data acquired during 2002–2010 from 1582 counties across China’s three wheat …

The availability of big data in agriculture, enhanced by free remote sensing data and on-board sensor-based data, provides an opportunity to understand within-field and year-to-year variability and promote precision farming practices for site-specific management. This paper explores the performance in durum wheat yield estimation using different technologies and data processing methods. A state-of-the-art data cleaning technique has been applied to data from a yield monitoring system, giving a good agreement between yield monitoring data and hand sampled data. The potential use of Sentinel-2 and Landsat-8 images in precision agriculture for within-field production variability is then assessed, and the optimal time for remote sensing to relate to durum wheat yield is also explored. Comparison of the Normalized Difference Vegetation Index(NDVI) with yield monitoring data reveals significant and highly positive linear relationships (r ranging from 0.54 to 0.74) explaining most within-field variability for all the images acquired between March and April. Remote sensing data analyzed with these methods could be used to assess durum wheat yield and above all to depict spatial variability in order to adopt site-specific management and improve productivity, save time and provide a potential alternative to traditional farming practices.

Abstract Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).

Monitoring crop condition and production estimates at the state and county level is of great interest to the U.S. Department of Agriculture. The National Agricultural Statistical Service (NASS) of the U.S. Department of Agriculture conducts field interviews with sampled farm operators and obtains crop cuttings to make crop yield estimates at regional and state levels. NASS needs supplemental spatial data that provides timely information on crop condition and potential yields. In this research, the crop model EPIC (Erosion Productivity Impact Calculator) was adapted for simulations at regional scales. Satellite remotely sensed data provide a real-time assessment of the magnitude and variation of crop condition parameters, and this study investigates the use of these parameters as an input to a crop growth model. This investigation was conducted in the semi-arid region of North Dakota in the southeastern part of the state. The primary objective was to evaluate a method of integrating parameters. retrieved from satellite imagery in a crop growth model to simulate spring wheat yields at the sub-county and county levels. The input parameters derived from remotely sensed data provided spatial integrity, as well as a real-time calibration of model simulated parameters during the season, to ensure that the modeled and observed conditions agree. A radiative transfer model, SAIL (Scattered by Arbitrary Inclined Leaves), provided the link between the satellite data and crop model. The model parameters were simulated in a geographic information system grid, which was the platform for aggregating yields at local and regional scales. A model calibration was performed to initialize the model parameters. This calibration was performed using Landsat data over three southeast counties in North Dakota. The model was then used to simulate crop yields for the state of North Dakota with inputs derived from NOAA AVHRR data. The calibration and the state level simulations are compared with spring wheat yields reported by NASS objective yield surveys.

The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with R2 of 0.56, RMSE of 1.22 t ha−1, and an average relative error of −6.01%. Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.

… to predict crop yield by empirical regression … in remote sensing applied to agricultural studies (21; 4), we studied the effects of N fertilization on the relationships between remote sensing …

… on estimating regional wheat yield by remote sensing from the parametric … wheat crop were achieved by spectral classification of image from AWiFS (Advanced Wide Field Sensor…

Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. Despite significant advancements in crop yield forecasting, existing methods often struggle with integrating diverse sensor data and achieving high prediction accuracy under varying environmental conditions. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms, namely random forest (RF), partial least squares (PLS), ridge regression (RR), k-nearest neighbor (KNN) and extreme gradient boosting decision tree (XGboost), were utilized for multi-sensor data fusion, together with three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average), for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB + MS + Texture + TIR combination demonstrating the highest fusion performance (R2 = 0.660, RMSE = 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the accuracy of wheat yield prediction. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE = 0.668 t ha−1). It significantly outperformed both the second-layer ensemble methods of stacking (R2 = 0.668, RMSE = 0.673 t ha−1) and feature-weighted (R2 = 0.667, RMSE = 0.674 t ha−1), thereby exhibiting superior predictive capabilities. This finding highlighted the third-layer ensemble method’s ability to enhance predictive capabilities and refined the accuracy of wheat yield prediction through simple average ensemble learning, offering a novel perspective for crop yield prediction and breeding selection.

… periods for estimating wheat grain yield and protein content, … obtained enhanced accuracy in wheat yield prediction, while … yield and grain protein content in wheat with remote sensing …

Globally, estimating crop acreage and yield is one of the most critical issues that policy and decision makers need for assessing annual crop productivity and food supply. Nowadays, satellite remote sensing and geographic information system (GIS) can enable the estimation of these crop production parameters over large geographic areas. The present work aims to estimate the wheat (Triticum aestivum) acreage and yield of Maharajganj, Uttar Pradesh, India, using satellite-based data products and the Carnegie-Ames-Stanford Approach (CASA) model. Uttar Pradesh is the largest wheat-producing state in India, and this district is well known for its quality organic wheat. India is the leader in wheat grain export, and, hence, its monitoring of growth and yield is one of the top economic priorities of the country. For the calculation of wheat acreage, we performed supervised classification using the Random Forest (RF) and Support Vector Machine classifiers and compared their classification accuracy based on ground-truthing. We found that RF performed a significantly accurate acreage assessment (kappa coefficient 0.84) compared to SVM (0.68). The CASA model was then used to calculate the winter crop (Rabi, winter-sown, and summer harvested) wheat net primary productivity (NPP) in the study area for the 2020–2021 growth season using the RF-based acreage product. The model used for wheat NPP-yield conversion (CASA) showed 3100.27 to 5000.44 kg/ha over 148,866 ha of the total wheat area. The results showed that in the 2020–2021 growing season, all the districts of Uttar Pradesh had similar wheat growth trends. A total of 30 observational data points were used to verify the CASA model-based estimates of wheat yield. Field-based verification shows that the estimated yield correlates well with the observed yield (R2 = 0.554, RMSE = 3.36 Q/ha, MAE −0.56 t ha−1, and MRE = −4.61%). Such an accuracy for assessing regional wheat yield can prove to be one of the promising methods for calculating the whole region’s agricultural yield. The study concludes that RF classifier-based yield estimation has shown more accurate results and can meet the requirements of a regional-scale wheat grain yield estimation and, thus, can prove highly beneficial in policy and decision making.

Improved understanding of the factors that limit crop yields in farmers' fields will play an important role in increasing regional food production while minimizing environmental impacts. However, causes of spatial variability in crop yields are poorly known in many regions because of limited data availability and analysis methods. In this study, we assessed sources of between-field wheat (Triticum aestivum L.) yield variability for two growing seasons in the Yaqui Valley, Mexico. Field surveys conducted in 2001 and 2003 provided data on management practices for 68 and 80 wheat fields throughout the Valley, respectively, while yields on these fields were estimated using concurrent Landsat satellite imagery. Management-yield relationships were analyzed with t tests, linear regression, and regression trees, all of which revealed significant but year-dependent impacts of management on yields. In 2001, an unusually cool year that favored high yields, N fertilizer was the most important source of between-field variability. In 2003, a warmer year with reduced irrigation water allocations, the timing of the first postplanting irrigation was found to be the most important control. Management explained at least 50% of spatial yield variability in both years. Regression tree models, which were able to capture important nonlinearities and interactions, were more appropriate for analyzing yield controls than traditional linear models. The results of this study indicate that adjustments in management can significantly improve wheat production in the Yaqui Valley but that the relevant controls change from year to year.

Accurate crop yield prediction is crucial for formulating agricultural policies, guiding agricultural management, and optimizing resource allocation. This study proposes a method for predicting yields in China’s major winter wheat-producing regions using MOD13A1 data and a deep learning model which incorporates an Improved Gray Wolf Optimization (IGWO) algorithm. By adjusting the key parameters of the Convolutional Neural Network (CNN) with IGWO, the prediction accuracy is significantly enhanced. Additionally, the study explores the potential of the Green Normalized Difference Vegetation Index (GNDVI) in yield prediction. The research utilizes data collected from March to May between 2001 and 2010, encompassing vegetation indices, environmental variables, and yield statistics. The results indicate that the IGWO-CNN model outperforms traditional machine learning approaches and standalone CNN models in terms of prediction accuracy, achieving the highest performance with an R2 of 0.7587, an RMSE of 593.6 kg/ha, an MAE of 486.5577 kg/ha, and an MAPE of 11.39%. The study finds that April is the optimal period for early yield prediction of winter wheat. This research validates the effectiveness of combining deep learning with remote sensing data in crop yield prediction, providing technical support for precision agriculture and contributing to global food security and sustainable agricultural development.

Wheat (Triticum aestivum L.) is one of the world’s primary food crops, and timely and accurate yield prediction is essential for ensuring food security. There has been a growing use of remote sensing, climate data, and their combination to estimate yields, but the optimal indices and time window for wheat yield prediction in arid regions remain unclear. This study was conducted to (1) assess the performance of widely recognized remote sensing indices to predict wheat yield at different growth stages, (2) evaluate the predictive accuracy of different yield predictive machine learning models, (3) determine the appropriate growth period for wheat yield prediction in arid regions, and (4) evaluate the impact of climate parameters on model accuracy. The vegetation indices, widely recognized due to their proven effectiveness, used in this study include the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Atmospheric Resistance Vegetation Index (ARVI). Moreover, four machine learning models, viz. Decision Trees (DTs), Random Forest (RF), Gradient Boosting (GB), and Bagging Trees (BTs), were evaluated to assess their predictive accuracy for wheat yield in the arid region. The whole wheat growth period was divided into three time windows: tillering to grain filling (December 15–March), stem elongation to grain filling (January 15–March), and heading to grain filling (February–March 15). The model was evaluated and developed in the Google Earth Engine (GEE), combining climate and remote sensing data. The results showed that the RF model with ARVI could accurately predict wheat yield at the grain filling and the maturity stages in arid regions with an R2 > 0.75 and yield error of less than 10%. The grain filling stage was identified as the optimal prediction window for wheat yield in arid regions. While RF with ARVI delivered the best results, GB with EVI showed slightly lower precision but still outperformed other models. It is concluded that combining multisource data and machine learning models is a promising approach for wheat yield prediction in arid regions.

The development of accurate grain yield (GY) multivariate models using normalized difference vegetation index (NDVI) assessments obtained from aerial vehicles and additional agronomic traits is a promising option to assist, or even substitute, laborious agronomic in-field evaluations for wheat variety trials. This study proposed improved GY prediction models for wheat experimental trials. Calibration models were developed using all possible combinations of aerial NDVI, plant height, phenology, and ear density from experimental trials of three crop seasons. First, models were developed using 20, 50 and 100 plots in training sets and GY predictions were only moderately improved by increasing the size of the training set. Then, the best models predicting GY were defined in terms of the lowest Bayesian information criterion (BIC) and the inclusion of days to heading, ear density or plant height together with NDVI in most cases were better (lower BIC) than NDVI alone. This was particularly evident when NDVI saturates (with yields above 8 t ha-1) with models including NDVI and days to heading providing a 50% increase in the prediction accuracy and a 10% decrease in the root mean square error. These results showed an improvement of NDVI prediction models by the addition of other agronomic traits. Moreover, NDVI and additional agronomic traits were unreliable predictors of grain yield in wheat landraces and conventional yield quantification methods must be used in this case. Saturation and underestimation of productivity may be explained by differences in other yield components that NDVI alone cannot detect (e.g. differences in grain size and number).

… remote sensing (RS) and meteorological data to map the variability of yield/biomass in cultivated wheat … of the differences in crop growth and yield assuming that in operative scenarios, …

… for optimizing its yield and water-fertilizer use efficiency. Unmanned aerial vehicle remote sensing provides a reliable tool for accurately monitoring winter wheat growth and dynamically …

Rainfall, temperature, and solar radiation are important climate factors, which determine crop growth, development and yield from instantaneous to decadal scales. We propose to identify year patterns of climate impact on yield on the basis of rain and non‐rain weather. There are inter‐related impacts of climatic factors on crop production within a specific pattern. Historical wheat yield data in Queensland during 1889–2004 were used. The influence of meteorological conditions on wheat yields was derived from statistical yield data which were detrended by 9‐year‐smoothing averages to remove the effects of technological improvements on wheat yields over time. Climate affects crop growth and development differently over different growth stages. Therefore, we considered the climate effects at both vegetative and reproductive stages (before and after flowering date, respectively) on yield. Cluster analysis was employed to identify the year patterns of climate impact. Five patterns were significantly classified. Precipitation during the vegetative stage was the dominant and beneficial factor for wheat yields while increasing maximum temperature had a negative influence. Crop yields were strongly dependent on solar radiation under normal rainfall conditions. As the effect of rainfall on soil water is relatively long‐lasting, its beneficial effect in vegetative stage was higher than its effect during the reproductive stage. The Agricultural Production Systems sIMulator (APSIM) was evaluated using long‐term historical data to determine whether the model could reasonably simulate effects of climate factors for each year pattern. The model provided good estimates of wheat yield when conditions resulted in medium yield levels, however, in extremely low or high yield years, corresponding to extremely low or high precipitation in the vegetative stage, the model tended to underestimate or overestimate. Under high growing season precipitation, simulations responded more favourably to reproductive stage rainfall than measured yields.

… climate models (RCMs) of the Coordinated Regional Climate … the impact of future climate change on winter wheat yield and … Three RCMs were selected, and climate variables were …

… Wheat (Triticum aestivum) is the most widely grown food crop in the world … future climate change. In this study, we simulated climate change impacts and adaptation strategies for wheat …

… However, most climate change agricultural impact assessments have used a single crop … with studies of climate impacts on crop yields, we used 27 different wheat crop models (…

… , the aspects of climate that winter wheat yields have been most … and the impact of changes in these variables on winter wheat yield trends was quantified. A number of aspects of climate …

… climate impacts on wheat yield. Our findings suggest the response of wheat growth and yield to climate … to improve the prediction of climate impacts and to plan adaptations for future. …

Climate change is one of the major challenges facing humanity in the future and effect of climate change has been detrimental to agricultural industry. The aim of this study was to simulate the effects of climate change on the maturity period, leaf area index (LAI), biomass and grain yield of wheat under future climate change for the Sistan and Baluchestan region in Iran. For this purpose, two general circulation models HadCM3 and IPCM4 under three scenarios A1B, B1 and A2 in three time periods 2020, 2050 and 2080 were used. LARS-WG model was used for simulating climatic parameters for each period and CERES-Wheat model was used to simulate wheat growth. The results of model evaluation showed that LARS-WG had appropriate prediction for climatic parameters and simulation of stochastic growing season in future climate change conditions for the studied region. Wheat growing season period in all scenarios of climate change was reduced compared to the current situation. Possible reasons were the increase in temperature rate and the accelerated growth stages of wheat. This reduction in B1 scenario was less than A1B and A2 scenarios. Maximum wheat LAI in all scenarios, except scenario A1B in 2050, is decreased compared to the current situation. Yield and biological yield of wheat in both general circulation models under all scenarios and all times were reduced in comparison with current conditions and the lowest reduction was related to B1 scenario. In general, the results showed that wheat production in the future will be affected by climate change and will decrease in the studied region. To reduce these risks, the impact of climate change mitigation strategies and management systems for crop adaptation to climate change conditions should be considered.

… for global scale applications, and explore how combining methods can improve our understanding of weather and climate impacts on yields in the diverse regions of the world. …

… the impact of climate change on drought indicators and yield of winter wheat in England and Wales. We used the crop simulation model Sirius to assess the effect of changing climate on …

… the timing of each climatic factor at each stage and this is critical for any impact analysis. This … effect of temperature variability on wheat yields by separating natural temperature effects …

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

The frequency and magnitude of extreme weather events are likely to increase with global warming. However, it is not clear how these events might affect agricultural crops and whether yield losses resulting from severe droughts or heat stress will increase in the future. The aim of this paper is to analyse changes in the magnitude and spatial patterns of two impact indices for wheat: the probability of heat stress around flowering and the severity of drought stress. To compute these indices, we used a wheat simulation model combined with high-resolution climate scenarios based on the output from the Hadley Centre regional climate model at 18 sites in England and Wales. Despite higher temperature and lower summer precipitation predicted in the UK for the 2050s, the impact of drought stress on simulated wheat yield is predicted to be smaller than that at present, because wheat will mature earlier in a warmer climate and avoid severe summer drought. However, the probability of heat stress around flowering that might result in considerable yield losses is predicted to increase significantly. Breeding strategies for the future climate might need to focus on wheat varieties tolerant to high temperature rather than to drought.

… it is important to know how different aspects of climate change affect agricultural production … rainfall scenarios affected wheat yield and grain protein. Effects of climate change were …

The Indo-Gangetic Plain (IGP) is one of the main wheat-production regions in India and the world. With climate change, wheat yields in this region will be affected through changes in temperature and precipitation and decreased water availability for irrigation, raising major concerns for national and international food security. Here we use a regional climate model and a crop model to better understand the direct (via changes in temperature and precipitation) and indirect (via a decrease in irrigation availability) impacts of climate change on wheat yields at four sites spread across different states of the IGP: Punjab, Haryana, Uttar Pradesh and Bihar. The results show an increase in mean temperature and precipitation as well as maximum temperature during the growing season or Rabi season (November–April). The direct impact of climate change, via changes in temperature and precipitation, leads to wheat yield losses between −1% and −8% depending on the site examined. Then, the indirect impact of climate change is examined, considering the impact of climate change on water availability leading to a decrease in irrigation. In this case, the yield losses become significant and much higher, reaching −4% to −36% depending on the site examined and the irrigation regime chosen (6, 5, 3 or 1 irrigations). This work shows that the indirect impacts of climate change may be more detrimental than the direct climatic effects for the future wheat yields in the IGP. It also emphasizes the complexity of climatic risk and the necessity of integrating indirect impacts of climate change to fully assess how it affects agriculture and choose the adequate adaptation response.

… Refined and improved climate change scenarios have been applied in this study to quantify the possible impacts of future climate change on South Australian wheat yield with …

… the effect of two different climate scenarios on crop yields in England and Wales … yield for both warm-world scenarios, with largest decreases for hay yield and least effect on wheat yield. …

The world's population is predicted to exceed nine billion by 2050 and there is increasing concern about the capability of agriculture to feed such a large population. Foresight studies on food security are frequently based on crop yield trends estimated from yield time series provided by national and regional statistical agencies. Various types of statistical models have been proposed for the analysis of yield time series, but the predictive performances of these models have not yet been evaluated in detail. In this study, we present eight statistical models for analyzing yield time series and compare their ability to predict wheat yield at the national and regional scales, using data provided by the Food and Agriculture Organization of the United Nations and by the French Ministry of Agriculture. The Holt-Winters and dynamic linear models performed equally well, giving the most accurate predictions of wheat yield. However, dynamic linear models have two advantages over Holt-Winters models: they can be used to reconstruct past yield trends retrospectively and to analyze uncertainty. The results obtained with dynamic linear models indicated a stagnation of wheat yields in many countries, but the estimated rate of increase of wheat yield remained above 0.06 t ha−1 year−1 in several countries in Europe, Asia, Africa and America, and the estimated values were highly uncertain for several major wheat producing countries. The rate of yield increase differed considerably between French regions, suggesting that efforts to identify the main causes of yield stagnation should focus on a subnational scale.

… yield condition monitoring and crop yield monitoring and forecasting. This paper reports the development of operational spectrometeorological yield models of wheat … and crop yield data …

… estimates of the total wheat yield at national level. In this study, four simple prediction models were tested to assess their operational usefulness. These models consist of a trend …

… the GEPIC model to study the crop-water relationship for winter wheat in China. Wheat is selected because of its importance for China and its high dependence on irrigation. Wheat in …

Abstract We compared the performance of eight widely used, easily accessible and well-documented crop growth simulation models (APES, CROPSYST, DAISY, DSSAT, FASSET, HERMES, STICS and WOFOST) for winter wheat ( Triticum aestivum L.) during 49 growing seasons at eight sites in northwestern, Central and southeastern Europe. The aim was to examine how different process-based crop models perform at the field scale when provided with a limited set of information for model calibration and simulation, reflecting the typical use of models for large-scale applications, and to present the uncertainties related to this type of model application. Data used in the simulations consisted of daily weather statistics, information on soil properties, information on crop phenology for each cultivar, and basic crop and soil management information. Our results showed that none of the models perfectly reproduced recorded observations at all sites and in all years, and none could unequivocally be labelled robust and accurate in terms of yield prediction across different environments and crop cultivars with only minimum calibration. The best performance regarding yield estimation was for DAISY and DSSAT, for which the RMSE values were lowest (1428 and 1603 kg ha −1 ) and the index of agreement (0.71 and 0.74) highest. CROPSYST systematically underestimated yields (MBE – 1186 kg ha −1 ), whereas HERMES, STICS and WOFOST clearly overestimated them (MBE 1174, 1272 and 1213 kg ha −1 , respectively). APES, DAISY, HERMES, STICS and WOFOST furnished high total above-ground biomass estimates, whereas CROPSYST, DSSAT and FASSET provided low total above-ground estimates. Consequently, DSSAT and FASSET produced very high harvest index values, followed by HERMES and WOFOST. APES and DAISY, on the other hand, returned low harvest index values. In spite of phenological observations being provided, the calibration results for wheat phenology, i.e. estimated dates of anthesis and maturity, were surprisingly variable, with the largest RMSE for anthesis being generated by APES (20.2 days) and for maturity by HERMES (12.6). The wide range of grain yield estimates provided by the models for all sites and years reflects substantial uncertainties in model estimates achieved with only minimum calibration. Mean predictions from the eight models, on the other hand, were in good agreement with measured data. This applies to both results across all sites and seasons as well as to prediction of observed yield variability at single sites – a very important finding that supports the use of multi-model estimates rather than reliance on single models.

… the dynamic simulation model CERES-Wheat could be used to forecast final grain yield and crop … Experimental data for three seasons and four sites were used for model calibration and …

… Simulated yields obtained from the crop growth model using … the model reliability, comparing winter wheat yield simulated by means of a formerly and independently calibrated model (…

… In an attempt to allow for yearly weather variation, two yield models (one simulation-based … the weather-based potential yield of each year. These predicted yields showed a significant …

… models of agricultural crops is compared. As empirical yields models the linear regression models of yield … as well as nonlinear regression model, in which daily meteorological data of …

… Wheat (Triticum spp.) crop models have been evolving since … incorporated advances in the modeling of environmental footprints… and limitations of modern wheat crop models in assisting …

… programs is possible with a wheat growth simulation model calibrated for the study area. In this study, a model was developed for simulation of wheat growth and yield. The …

… in the model. For wheat, over 70 models have been developed over the past 20 yr (McMaster 193). Not all of these models have been based on the physiological pKrcesses of the crop, …

… Our research was directed toward developing models to predict large-area wheat yields … Such models would need to respond to both abrupt year-to-year yield changes due to …

An increase in world population requires growth in food production. Wheat is one of the major food crops, covering 21% of global food needs. The food supply issue necessitates reliable mathematical methods for predicting wheat yields. Crop yield information is necessary for agricultural management and strategic planning. Our mathematical model was developed based on a three-year field experiment in a semi-arid climate zone. Wheat yields ranged from 4310 to 6020 kg/ha. The novelty of this model is the inclusion of some stochastic data (weather and technological). The proposed method for wheat yield modeling is based on the theory of random sequence analysis. The model does not impose any restrictions on the number of production parameters and environmental indicators. A significant advantage of the proposed model is the absence of limits on the yield function. Consideration of the stochastic features of wheat production (technological and weather parameters) allows researchers to achieve the best accuracy. The numerical experiment confirmed the high accuracy of the proposed mathematical model for the prediction of wheat yield. The mean relative error (for the third-order polynomial model) varied from 1.79% to 2.75% depending on the preceding crop.

Enhanced grain yield has been achieved in bread wheat (Triticum aestivum L.) through development and cultivation of superior genotypes incorporating yield-related agronomic and physiological traits derived from genetically diverse and complementary genetic pool. Despite significant breeding progress, yield levels in wheat have remained relatively low and stagnant under marginal growing environments. There is a need for genetic improvement of wheat using yield-promoting morpho-physiological attributes and desired genotypes under the target production environments to meet the demand for food and feed. This review presents breeding progress in wheat for yield gains using agronomic and physiological traits. Further, the paper discusses globally available wheat genetic resources to identify and select promising genotypes possessing useful agronomic and physiological traits to enhance water, nutrient-, and radiation-use efficiency to improve grain yield potential and tolerance to abiotic stresses (i.e. elevated CO2, high temperature, and drought stresses). Finally, the paper highlights quantitative trait loci (QTL) linked to agronomic and physiological traits to aid breeding of high-performing wheat genotypes.

… Genetic improvements have contributed much to wheat … of agronomic traits and the physiological basis of grain yield will facilitate breeders and agronomists in developing new wheat …

Drought has been one of the most important limiting factors for crop production, which deleteriously affects food security worldwide. The main objective of the present study was to quantitatively assess the effect of drought on the agronomic traits (e.g., plant height, biomass, yield, and yield components) of rice and wheat in combination with several moderators (e.g., drought stress intensity, rooting environment, and growth stage) using a meta-analysis study. The database was created from 55 published studies on rice and 60 published studies on wheat. The results demonstrated that drought decreased the agronomic traits differently between rice and wheat among varying growth stages. Wheat and rice yields decreased by 27.5% and 25.4%, respectively. Wheat grown in pots showed greater decreases in agronomic traits than those grown in the field. Rice showed opposite growing patterns when compared to wheat in rooting environments. The effect of drought on rice increased with plant growth and drought had larger detrimental influences during the reproductive phase (e.g., blooming stage, filling stage, and maturity). However, an exception was found in wheat, which had similar decreased performance during the complete growth cycle. Based on these results, future droughts could produce lower yields of rice and wheat when compared to the current drought.

Drought stress is one of the major abiotic stresses to wheat worldwide, with negative effects on wheat growth and yield. Assessing genetic variation and drought stress tolerance of key agronomic and physiological traits of spring wheat and screening germplasm resources for higher drought tolerance and yield stability are a prerequisite for developing new, better-adapted spring wheat varieties. This study evaluated nine important agronomic and physiological traits in 152 spring wheat cultivars under non-stress (NS) and drought-stress (DS) conditions. Under DS conditions, grain yield per plot (GYP) and grain weight per spike (GWE) were significantly reduced by 33.8% and 31.7%, and their drought-tolerance indexes (DIs) were only 0.66 and 0.69, respectively, indicating that GYP and GWE are the most susceptible traits to drought stress. The SPAD value of flag leave at flowering stage decreased by 13.9% under DS conditions, and the DI of SPAD was 0.86. In addition, DI-SPAD was significantly positively correlated with DIs of plant height (PH), grain number per spikelet (GPS), grain number per spike (GNS), GWE and GYP, indicating that the drought tolerance and yield of wheat are closely related to chlorophyll retention. Six wheat germplasm accessions were identified for their ability to sustain grain yield and improve drought tolerance simultaneously. These results provide insights into the genetic co-variation between grain yield and drought stress tolerance and provide a theoretical basis for the development of new wheat cultivars with excellent drought tolerance and high yields in the presence and absence of drought.

… Although recognizing that exceptions can doubtlessly be found, we believe that there is some agreement in literature on wheat and barley on: a. Selecting for high yield potential does …

… agronomic traits of winter wheat cultivars cultivated in France during the second half of the 20th century at four agronomic … major role in the increase in winter wheat yield after 1946. The …

Wheat is a cool seasoned crop requiring low temperature during grain filling duration and therefore increased temperature causes significant yield reduction. A set of 125 spring wheat genotypes from International Maize and Wheat Improvement Centre (CIMMYT-Mexico) was evaluated for phenological and yield related traits at three locations in Pakistan under normal sowing time and late sowing time for expose to prolonged high temperature. With the help of genome-wide association study using genotyping-by-sequencing, marker trait associations (MTAs) were observed separately for the traits under normal and late sown conditions. Significant reduction ranging from 9 to 74% was observed in all traits under high temperature. Especially 30, 25, 41 and 66% reduction was observed for days to heading (DH), plant height (PH), spikes per plant (SPP) and yield respectively. We identified 55,954 single nucleotide polymorphisms (SNPs) using genotyping by sequencing of these 125 hexaploid spring wheat genotypes and conducted genome-wide association studies (GWAS) for days to heading (DH), grain filled duration (GFD), plant height (PH), spikes per plant (SPP), grain number per spike (GNS), thousand kernel weight (TKW) and grain yield per plot (GY). Genomic regions identified through GWAS explained up to 13% of the phenotypic variance, on average. A total of 139 marker-trait associations (MTAs) across three wheat genomes (56 on genome A, 55 on B and 28 on D) were identified for all the seven traits studied. For days to heading, 20; grain filled duration, 21; plant height, 23; spikes per plant, 13; grain numbers per spike, 8; thousand kernel weight, 21 and for grain yield, 33 MTAs were detected under normal and late sown conditions. This study identifies the essential resource of genetics research and underpins the chromosomal regions of seven agronomic traits under normal and high temperature. Significant relationship was observed between the number of favored alleles and trait observations. Fourteen protein coding genes with their respective annotations have been searched with the sequence of seven MTAs which were identified in this study. These findings will be helpful in the development of a breeder friendly platform for the selection of high yielding wheat lines at high temperature areas.

Genetic analyses and association mapping were performed on a winter wheat core collection of 96 accessions sampled from a variety of geographic origins. Twenty-four agronomic traits were evaluated over 3 years under fully irrigated, rainfed and drought treatments. Grain yield was the most sensitive trait to water deficit and was highly correlated with above-ground biomass per plant and number of kernels per m2. The germplasm was structured into four subpopulations. The association of 46 SSR loci distributed throughout the wheat genome with yield and agronomic traits was analyzed using a general linear model, where subpopulation information was used to control false-positive or spurious marker-trait associations (MTAs). A total of 26, 21 and 29 significant (P < 0.001) MTAs were identified in irrigated, rainfed and drought treatments, respectively. The marker effects ranged from 14.0 to 50.8%. Combined across all treatments, 34 significant (P < 0.001) MTAs were identified with nine markers, and R2 ranged from 14.5 to 50.2%. Marker psp3200 (6DS) and particularly gwm484 (2DS) were associated with many significant MTAs in each treatment and explained the greatest proportion of phenotypic variation. Although we were not able to recognize any marker related to grain yield under drought stress, a number of MTAs associated with developmental and agronomic traits highly correlated with grain yield under drought were identified.

Knowledge about the yield gain over the years due to associated changes in the yield component traits is essential for a critical understanding of yield-limiting factors. To estimate genetic gain in grain yield (GY) and component agronomic traits of wheat varieties released between 1900 and 2016 for northwestern plain zone (NWPZ) of India and to identify agronomic and/or genetic basis of the realized gains, two sets of wheat varieties comprising mega varieties and two recently developed varieties were evaluated under timely sown, tilled, and early sown conservation agriculture (CA) conditions for four consecutive years under irrigated conditions. The average annual genetic gain in GY since 1,905 under timely sown irrigated conditions was found to be 0.544% yr−1 over the average of all varieties and 0.822% yr−1 (24.27 kg ha−1 yr−1) over the first released variety, NP4. The realized mean yield increased from 2,950 kg ha−1 of the variety NP4 released in 1,905–5,649 kg ha−1 of HD3086 released in 2014. Regression analysis revealed a linear reduction in height and peduncle length (PL) over the years with a simultaneous and linear increase in biomass at the rate of 43.9 kg ha−1 yr−1 or relatively at 0.368% yr−1 mainly because of delayed heading and increased crop duration. Regression analysis showed no linear trend for tiller number and thousand-grain weight (TGW). Though harvest index (HI) was found to linearly increase relatively at the rate of 0.198% per annum, polynomial regression improved the fitness of data with the indication of no increase in HI since 1982. Interestingly, genetic gain evaluation under early sown CA conditions for 4 years showed similar relative gain (RG) [a relative improvement in varieties across breeding periods (BP)] (0.544% yr−1) but with a higher absolute value (29.28 kg ha−1 yr−1). Major mega varieties like Kalyan Sona, HD2009, PBW 343, HD2967, and HD3086, which occupied a comparatively larger area, were found highly plastic to the improvements in the production environment under timely sown conditions.

Several environmental factors including drought and disease can reduce leaf area and photosynthesis during grain-filling to decrease grain yield and kernel weight of cereal crops. Water-soluble carbohydrates (WSC) accumulated around anthesis can be mobilised to assist in filling of developing grains when post-anthesis assimilation is low. Cultivar differences support opportunities to select for high WSC but little is known of the extent or nature of genetic control for this trait in wheat. Three wheat mapping populations (Cranbrook/Halberd, Sunco/Tasman, and CD87/Katepwa) were phenotyped for WSC and other agronomic traits across multiple environments. The range for WSC concentration (WSC-C) was large among progeny contributing to moderate-to-high narrow-sense heritabilities within environments (h2 = 0.51–0.77). Modest genotype × environment interaction reduced the correlation of genotype means across environments (rp = 0.37–0.78, P &amp;lt; 0.01) to reduce heritability on a line-mean (h2 = 0.55–0.87) basis. Transgressive segregation was large and genetic control complex, with 7–16 QTLs being identified for WSC-C in each population. Heritability was smaller (h2 = 0.32–0.54) for WSC mass per unit area (WSC-A), reflecting large genotype × environment interaction and residual variance with estimating anthesis biomass. Fewer significant QTLs (4–8) were identified for this trait in each population, while sizes of individual genetic effects varied between populations but were repeatable across environments. Several genomic regions were common across populations including those associated with plant height (e.g. Rht-B1) and/or anthesis date (e.g. Ppd1). Genotypes with high WSC-C were commonly shorter, flowered earlier, and produced significantly (P &amp;lt; 0.01) fewer tillers than those of low WSC-C. This resulted in similar yields, lower final biomass, and fewer grains per m2, but greater dry weight partitioning to grain, kernel weight, and less grain screenings in high compared with low WSC-C genotypes. By contrast, lines high for WSC-A produced more fertile tillers associated with similar or greater anthesis and maturity biomass, grain number, and yield, yet similar kernel weight or size compared with genotypes with low WSC-A. The data support an important role for WSC-A in assuring stable yield and grain size. However, the small effects of many independent WSC QTLs may limit their direct use for marker-aided selection in breeding programs. We suggest using molecular markers to enrich populations for favourable height and anthesis date alleles before the more costly phenotypic selection among partially inbred families for greater WSC-A.

Understanding the basis of genetic gains in grain yield and yield-related traits is essential for designing future breeding strategies that lead to the development of higher-yielding wheat cultivars. The objectives of this study were to assess the changes in grain yield achieved by durum wheat breeding in Argentina and to identify the agronomic traits associated with these changes. To this end, a wide set of Argentinian cultivars was analyzed in three field trials. A significant linear trend (R2 = 0.55) was observed between the grain yield and the cultivar’s release year, with an increase of 26.94 kg ha−1 yr−1 from 1934 to 2015. The harvest index and grain number were key traits that explained the increases in grain yield. The number of grains per plant increased with the cultivar’s release year, while the thousand kernel weight remained unchanged. The grain yield showed an increase of 51% when comparing old cultivars (<1980) with intermediate ones (1980–1999), whereas the increase between intermediate and modern cultivars (2000+) was only 16%. Thus, the genetic gains were mostly associated with the incorporation of semi-dwarfism into the germplasm in the 1980s, with low genetic gains after that.

Wheat cultivars grown in south-eastern Europe are exposed to variable rainfed environments. Climate change predictions indicate that the frequency of dry years will likely increase in the future. This study examined relationships among agronomic traits and some drought indices with grain yield as influenced by genotype and environment. In a 4-year experiment, 100 cultivars and landraces of bread wheat (Triticum aestivum L.) from different countries were tested under 3 watering regimes: fully irrigated, rainfed, and in a rain-out plot shelter. Three selection indices, mean productivity (MP), tolerance (TOL), and stress susceptibility index (SSI), were calculated based on grain yield in irrigated and drought-stressed conditions. The additive main effects and multiplicative interaction (AMMI) models were used to study the genotype × environment effects. Average yield reduction due to drought in the sheltered plots was 37.5%. High-yielding genotypes in each treatment showed high values of MP and high rank for SSI and, particularly, TOL. Conversely, low-yielding genotypes in each treatment had low values of MP and high drought tolerance according to SSI and TOL (i.e. low ranks). MP values were noted as being particularly well suited for predicting performance in this experiment. Total biomass and early vigour were found to be the most important agronomic traits for selecting high-yielding genotypes in a range of stress and non-stress conditions.