Effects of fallow tillage on winter wheat yield and predictions under different precipitation types.
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ABSTRACT:
SUBMITTER: Feng Y
PROVIDER: S-EPMC8667742 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In South China, it is common practice for the late rice (Oryza sativa) that is planted during the summer in the paddy fields after harvest to be used for fallowing or to plant winter forage crops. The land is ploughed before early rice planting. Both forage wheat (Triticum aestivum) and Italian ryegrass (Lolium multiflorum) have relatively high nutritional value, and planting them in winter fallow paddy fields could potentially address food shortages and provide quality forage for livestock. In this study, we examined the effects of no-tillage sowing 5 days before rice harvest (NB5), no-tillage sowing 1 day after rice harvest (NA1), and conventional tillage sowing (CK) 1 day after rice harvest on forage wheat and Italian ryegrass soil properties, dry matter (DM), and crude protein (CP) yields. Soil and plant samples were collected after three months of crop growth. The results showed that the NB5 and NA1 soil bulk density (0-20 cm soil layer) tended to increase when compared to that of the CK field. The NA1 treatment increased the total soil nitrogen and organic matter content. The enzyme activities and total soil porosity in the no-tillage forage wheat and Italian ryegrass fields tended to decrease, while the no-tillage water content and soil capillary porosity tended to increase when compared to that of the CK field. Overall, planting year significantly influenced soil chemical properties (except for total nitrogen) and enzyme activity, but crop type had no significant effect on soil physical-chemical properties (except for capillary moisture capacity) and enzyme activity. Sowing methods had no significant effects on the crop DM and CP yields. The DM yield was affected by the interaction between planting year and sowing methods, or between sowing methods and crop type. No-tillage also increased the number of species and aboveground weed biomass. We concluded that the best sowing method for forage wheat and Italian ryegrass in winter fallow paddy fields was no-tillage sowing following rice harvest.
| S-EPMC7847706 | biostudies-literature
Project description:In recent years, the unmanned aerial vehicle (UAV) remote sensing system has been rapidly developed and applied in accurate estimation of crop parameters and yield at farm scale. To develop the major contribution of UAV multispectral images in predicting winter wheat leaf area index (LAI), chlorophyll content (called soil and plant analyzer development [SPAD]), and yield under different water treatments (low water level, medium water level, and high water level), vegetation indices (VIs) originating from UAV multispectral images were used during key winter wheat growth stages. The estimation performances of the models (linear regression, quadratic polynomial regression, and exponential and multiple linear regression models) on the basis of VIs were compared to get the optimal prediction method of crop parameters and yield. Results showed that LAI and SPAD derived from VIs both had high correlations compared with measured data, with determination coefficients of 0.911 and 0.812 (multivariable regression [MLR] model, normalized difference VI [NDVI], soil adjusted VI [SAVI], enhanced VI [EVI], and difference VI [DVI]), 0.899 and 0.87 (quadratic polynomial regression, NDVI), and 0.749 and 0.829 (quadratic polynomial regression, NDVI) under low, medium, and high water levels, respectively. The LAI and SPAD derived from VIs had better potential in estimating winter wheat yield by using multivariable linear regressions, compared to the estimation yield based on VIs directly derived from UAV multispectral images alone by using linear regression, quadratic polynomial regression, and exponential models. When crop parameters (LAI and SPAD) in the flowering period were adopted to estimate yield by using multiple linear regressions, a high correlation of 0.807 was found, while the accuracy was over 87%. Importing LAI and SPAD obtained from UAV multispectral imagery based on VIs into the yield estimation model could significantly enhance the estimation performance. This study indicates that the multivariable linear regression could accurately estimate winter wheat LAI, SPAD, and yield under different water treatments, which has a certain reference value for the popularization and application of UAV remote sensing in precision agriculture.
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Project description:The uneven spatial and temporal distribution of light resources and water scarcity during the grain-filling stage pose significant challenges for sustainable crop production, particularly in the arid areas of the Loess Plateau in Northwest China. This study aims to investigate the combined effects of drought and shading stress on winter wheat growth and its physio-biochemical and antioxidative responses. Wheat plants were subjected to different drought levels- full irrigation (I100), 75% of full irrigation (I75), 50% of full irrigation (I50), and 25% of full irrigation (I25), and shading treatments - 12, 9, 6, 3 and 0 days (SD12, SD9, SD6, SD3, and CK, respectively) during the grain-filling stage. The effects of drought and shading treatments reduced yield in descending order, with the most significant reductions observed in the SD12 and I25 treatments. These treatments decreased grain yield, spikes per plant, 1000-grain weight, and spikelets per spike by 160.67%, 248.13%, 28.22%, and 179.55%, respectively, compared to the CK. Furthermore, MDA content and antioxidant enzyme activities exhibited an ascending trend with reduced irrigation and longer shading durations. The highest values were recorded in the I75 and SD12 treatments, which increased MDA, SOD, POD, and CAT activities by 65.22, 66.79, 65.07 and 58.38%, respectively, compared to the CK. The Pn, E, Gs, and iCO2 exhibited a decreasing trend (318.14, 521.09, 908.77, and 90.85%) with increasing shading duration and decreasing irrigation amount. Drought and shading treatments damage leaf chlorophyll fluorescence, decreasing yield and related physiological and biochemical attributes.
| S-EPMC11317437 | biostudies-literature
Project description:BackgroundExploring suitable split nitrogen management is essential for winter wheat production in the Huang-Huai-Hai Plain of China (HPC) under water-saving irrigation conditions, which can increase grain and protein yields by improving nitrogen translocation, metabolic enzyme activity and grain nitrogen accumulation.MethodsTherefore, a 2-year field experiment was conducted to investigate these effects in HPC. Nitrogen fertilizer was applied at a constant total rate (240 kg/ha), split between the sowing and at winter wheat jointing growth stage in varying ratios, N1 (0% basal and 100% dressing fertilizer), N2 (30% basal and 70% dressing fertilizer), N3 (50% basal and 50% dressing fertilizer), N4 (70% basal and 30% dressing fertilizer), and N5 (100% basal and 0% dressing fertilizer).ResultsWe found that the N3 treatment significantly increased nitrogen accumulation post-anthesis and nitrogen translocation to grains. In addition, this treatment significantly increased flag leaf free amino acid levels, and nitrate reductase and glutamine synthetase activities, as well as the accumulation rate, active accumulation period, and accumulation of 1000-grain nitrogen. These factors all contributed to high grain nitrogen accumulation. Finally, grain yield increase due to N3 ranging from 5.3% to 15.4% and protein yield from 13.7% to 31.6%. The grain and protein yields were significantly and positively correlated with nitrogen transport parameters, nitrogen metabolic enzyme activity levels, grain nitrogen filling parameters.ConclusionsTherefore, the use of split nitrogen fertilizer application at a ratio of 50%:50% basal-topdressing is recommended for supporting high grain protein levels and strong nitrogen translocation, in pursuit of high-quality grain yield.
| S-EPMC8179224 | biostudies-literature
Project description:Soil rotational tillage is an effective measure to overcome the problems caused by long-term of a single tillage, but the effect of the interval time of rotational tillage practices is not very well understood. Therefore, we conducted a 3-year field study in a wheat-maize cropping system to evaluate the effects of rotary tillage (RT) in rotation with plowing tillage (PT) on soil properties in northern China. Four practices were designed as follows: 3 years of RT to a depth of 10-15 cm (3RT), 3 years of PT to a depth of 30-35 cm (3PT), 1 year of PT followed by 2 years of RT (PT+2RT), and 2 years of PT followed by 1 year of RT (2PT+RT). Within 20 cm of the surface soil, the 3RT treatment significantly increased the soil quality index (SQI) by 6.0%, 8.8% and 13.1%, respectively, relative to the PT+2RT, 2PT+RT and 3PT treatments. The improvement was closely related to the significant increase in the soil organic carbon (SOC) and available nutrients concentrations in the 0-20 cm depths and the improvement of soil invertase, urease, alkaline phosphatase and catalase activities in the topsoil (0-10 cm). However, the opposite effects were observed in the subsoil (20-40 cm). Compared with the 3RT treatment, the 3PT, 2PT+RT and PT+2RT treatments decreased soil bulk density, and significantly enhanced enzyme activities, resulting in an increase in SQI of 32.6%, 24.4% and 0.7%, respectively, especially in the 3PT and 2PT+RT treatments, the difference was significant. When averaged across to all soil depths, the SQI under the 3RT and 2PT+RT treatments was much higher than that under the other treatments. The yields of wheat and maize under the 2PT+RT treatment were 15.0% and 14.3% higher than those under the 3RT treatment, respectively. The 2PT+RT treatment was the most effective tillage practice. These results suggest that RT in rotation with PT could improve soil quality in the soil profile whilst enhancing crop yield after continuous RT, and the benefits were enhanced with an interval time of one year. Therefore, the 2PT+RT treatment could act as an effective method for both soil quality and crop yield improvement in a wheat-maize cropping system under straw incorporation conditions.
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Project description:Stay-green, a key trait of wheat, can not only increase the yield of wheat but also its resistance to heat stress during active photosynthesis. Cytokinins are the most potent general coordinator between the stay-green trait and senescence. The objectives of the present study were to identify and assess the effects of cytokinins on the photosynthetic organ and heat resistance in wheat. Two winter wheat cultivars, Wennong 6 (a stay-green cultivar) and Jimai 20 (a control cultivar), were subjected to heat stress treatment from 1 to 5 days after anthesis (DAA). The two cultivars were sprayed daily with 10 mg L-1 of 6-benzylaminopurine (6-BA) between 1 and 3 DAA under ambient and elevated temperature conditions. We found that the heat stress significantly decreased the number of kernels per spike and the grain yield (P < 0.05). Heat stress also decreased the zeatin riboside (ZR) content, but increased the gibberellin (GA3), indole-3-acetic acid (IAA), and abscisic acid (ABA) contents at 3 to 15 DAA. Application of 6-BA significantly (P < 0.05) increased the grain-filling rate, endosperm cell division rate, endosperm cell number, and 1,000-grain weight under heated condition. 6-BA application increased ZR and IAA contents at 3 to 28 DAA, but decreased GA3 and ABA contents. The contents of ZR, ABA, and IAA in kernels were positively and significantly correlated with the grain-filling rate (P < 0.05), whereas GA3 was counter-productive at 3 to 15 DAA. These results suggest that the decrease in grain yield under heat stress was due to a lower ZR content and a higher GA3 content compared to that at elevated temperature during the early development of the kernels, which resulted in less kernel number and lower grain-filling rate. The results also provide essential information for further utilization of the cytokinin substances in the cultivation of heat-resistant wheat.
| S-EPMC4874672 | biostudies-literature
Project description:Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore-Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.
| S-EPMC7073225 | biostudies-literature
Project description:BackgroundOptimal nitrogen (N) application and plant growth regulators can improve wheat productivity. This can help to improve yield level and ensure food security with limited resources in the Huang-Huai-Hai Plain of China (HPC).MethodsA 2-year field experiment was conducted using a randomized block design with four treatments (TS-N topdressing at pseudostem erection stage ; TPS-N topdressing combined with paclobutrazol application at pseudostem erection stage; TJ-N topdressing at jointing stage; TPJ-N topdressing at combined with paclobutrazol application at jointing stage) in 2011-2013.ResultsThe grain number per ear, thousand kernel weight and yield for the TJ and TPJ treatments were higher than those of the TS and TPS treatments. Grain number per ear, yield, and thousands kernel weigh for the TPJ treatment were significantly higher than for the TS and TPS in 2011-2012 (9.82% and 7.27%, 10.23% and 8.99%, 6.12% and 5.58%) and in 2012-2013 (10.21% and 11.55%, 8.00% and 6.58%, 0.00 and 0.00), respectively. Thousands kernel weight under TJ were significantly higher than those under TS and TPS by 13.21% and 14.03%, respectively in 2012-2013. The floret number, significantly correlated with cytokinin content, was also significantly increased under TJ and TPJ at connectivum differentiation stage. For TPJ treatment, the floret number was significantly higher than for the TS, TPS, and TJ by 19.92%, 10.21%, 6.10% in 2011-2012; it was higher than for the TS and TPS by 28.06% and 29.61% in 2012-2013, respectively. The relative expression level of cytokinin oxidase/dehydrogenase gene (TaCKX2.2) was improved during flowering, when cytokinin content was at high level and was also inhibited by paclobutrazol with different degrees.ConclusionsTherefore, nitrogen topdressing at jointing stage had increased grain number per ear, thousand kernel weight, and grain yield of wheat. Paclobutrazol could delay spike differentiation and promote cytokinin accumulation that induced expression of TaCKX2.2, maintaining hormonal balance and affecting wheat spike morphogenesis.
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Project description:Genomic prediction (GP) is now routinely performed in crop plants to predict unobserved phenotypes. The use of predicted phenotypes to make selections is an active area of research. Here, we evaluate GP for predicting grain yield and compare genomic and phenotypic selection by tracking lines advanced. We examined four independent nurseries of F3:6 and F3:7 lines trialed at 6 to 10 locations each year. Yield was analyzed using mixed models that accounted for experimental design and spatial variations. Genotype-by-sequencing provided nearly 27,000 high-quality SNPs. Average genomic predictive ability, estimated for each year by randomly masking lines as missing in steps of 10% from 10 to 90%, and using the remaining lines from the same year as well as lines from other years in a training set, ranged from 0.23 to 0.55. The predictive ability estimated for a new year using the other years ranged from 0.17 to 0.28. Further, we tracked lines advanced based on phenotype from each of the four F3:6 nurseries. Lines with both above average genomic estimated breeding value (GEBV) and phenotypic value (BLUP) were retained for more years compared to lines with either above average GEBV or BLUP alone. The number of lines selected for advancement was substantially greater when predictions were made with 50% of the lines from the testing year added to the training set. Hence, evaluation of only 50% of the lines yearly seems possible. This study provides insights to assess and integrate genomic selection in breeding programs of autogamous crops.
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Project description:Rapid and non-destructive estimation of leaf nitrogen accumulation (LNA) is essential to field nitrogen management. Currently, many vegetation indices have been used for indicating nitrogen status. Few studies systematically analyzed the performance of vegetation indices of winter wheat in estimating LNA under different irrigation regimes. This study aimed to develop a new spectral index for LNA estimation. In this study, 2 years of field experiments with different irrigation regimes were conducted from 2015 to 2017. The original reflectance (OR) and three transformed spectra [e.g., the first derivative reflectance (FDR), logarithm of the reciprocal of the spectra (Log(1/R)), and continuum removal (CR)] were used to calculate two- and three-band spectral indices. Correlation analyses and univariate linear and non-linear regression between transformed-based spectral indices and LNA were performed. The performance of the optimal spectral index was evaluated with classical vegetation index. The results showed that FDR was the most stable transformation method, which can effectively enhance the relationships to LNA and improve prediction performance. With a linear relationship with LNA, FDR-based three-band spectral index 1 (FDR-TBI1) (451, 706, 688) generated the best performance with coefficient of determination (R 2) of 0.73 and 0.79, the root mean square error (RMSE) of 1.267 and 1.266 g/m2, and the ratio of performance to interquartile distance (RPIQ) of 2.84 and 2.71 in calibration and validation datasets, respectively. The optimized spectral index [FDR-TBI1 (451, 706, 688)] is more effective and might be recommended as an indicator for estimating winter wheat LNA under different irrigation regimes.
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