scholarly journals Dissecting Bread Wheat Heterosis through the Integration of Agronomic and Physiological Traits

Biology ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 907
Author(s):  
Kevin Gimenez ◽  
Pierre Blanc ◽  
Odile Argillier ◽  
Jean-Baptiste Pierre ◽  
Jacques Le Le Gouis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agronomic Traits ◽  
Wheat Yield ◽  
Grain Filling ◽  
Kernel Weight ◽  
Hybrid Vigor ◽  
Delayed Senescence ◽  
Yield Increase ◽  
Wheat Hybrids

To meet the challenge of feeding almost 10 billion people by 2050, wheat yield has to double by 2050. However, over the past 20 years, yield increase has slowed down and even stagnated in the main producing countries. Following the example of maize, hybrids have been suggested as a solution to overcome yield stagnation in wheat. However, wheat heterosis is still limited and poorly understood. Gaining a better understanding of hybrid vigor holds the key to breed for better varieties. To this aim, we have developed and phenotyped for physiological and agronomic traits an incomplete factorial design consisting of 91 hybrids and their nineteen female and sixteen male parents. Monitoring the plant development with normalized difference vegetation index revealed that 89% of the hybrids including the five higher yielding hybrids had a longer grain filling phase with a delayed senescence that results in larger grain size. This average increase of 7.7% in thousand kernel weight translated to a positive mid-parent heterosis for grain yield for 86% of hybrids. In addition, hybrids displayed a positive grain protein deviation leading to a +4.7% heterosis in protein yield. These results shed light on the physiological bases underlying yield heterosis in wheat, paving new ways to breed for better wheat hybrids.

scholarly journals Normalized Difference Vegetation Index as a Tool for Wheat Yield Estimation: A Case Study from Faisalabad, Pakistan

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Syeda Refat Sultana ◽  
Amjed Ali ◽  
Ashfaq Ahmad ◽  
Muhammad Mubeen ◽  
M. Zia-Ul-Haq ◽  
...  
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Experiments ◽  
Wheat Yield ◽  
Grain Filling ◽  
Research Area ◽  
Wheat Crop ◽  
Growth Stages ◽  
Wheat Cultivars

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, andN4= 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 (R2 = 0.90;R2 = 0.90;R2 = 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.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

Ear moisture during kernel development as influenced by field and laboratory selection

1995 ◽  
Vol 75 (3) ◽  
pp. 557-563 ◽  
Author(s):  
H. Z. Cross
Keyword(s):  
Moisture Content ◽  
Field Study ◽  
Agronomic Traits ◽  
Grain Filling ◽  
Kernel Weight ◽  
Test Weight ◽  
Mass Selection ◽  
Dry Matter Accumulation ◽  
Drying Characteristics ◽  
Laboratory Selection

Grain quality, timeliness of harvest, and profitability can be increased by improving field drying characteristics of maize (Zea mays L.) hybrids. To better understand hows genes control ear drying, I compared maize strains developed by divergently selecting three cycles for (1) high HM or low LM moisture content at 45 d post pollination in the field or (2) fast FD vs. slow ear drying SD In laboratory. A field study across five locations compared HM, LM, FD, and SD strains from each of five synthetics for grain yield, ear moisture at harvest, test weight, lodging, and other agronomic traits. I studied ear moisture during grain filling for two subsets of divergently selected strains from one and three synthetics for 2 yr. In a third 2-yr field study, I measured mature kernel weight, lag period duration (LPD), effective grain-filling period (EFPD), and rate of dry matter accumulation (RDMA) for LM and HM strains developed from each of four synthetics. When averaged across the five synthetics, both SD and LM selections produced equivalent yields but lower ear moisture at harvest than the corresponding divergent strains. The LM strains had higher test weights than HM strains. When averaged across three synthetics and 2 yr, the HM strains produced higher moisture than LM strains at 15, 30, 45, and 60 d after silking. However, environments also influenced moisture content of the kernels during grain filling. In three of the four synthetics studied, HM strains had heavier kernels than corresponding LM strains. The heavier kernels seem to be due to increased RDMA. When averaged across four synthetics, LM strains had shorter LPD than HM strains. These correlated selection responses suggest that a genetic association exists among moisture content during grain filling, moisture content at physiological maturity, moisture content at harvest, LPD, and test weight. Breeding for LM or SD should improve field-drying characteristics of maize without increasing stalk breakage or decreasing yields. Key words:Zea mays L., grain filling, dry-down rates, mass selection, breeding methods

scholarly journals Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

2021 ◽  
Vol 13 (6) ◽  
pp. 1144
Author(s):  
Mahendra Bhandari ◽  
Shannon Baker ◽  
Jackie C. Rudd ◽  
Amir M. H. Ibrahim ◽  
Anjin Chang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Grain Filling ◽  
Unmanned Aerial Systems ◽  
High Temporal Resolution ◽  
Wheat Growth ◽  
Spatial Domains ◽  
Temporal And Spatial

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

scholarly journals Are Wheat Hybrids More Affected by Weed Competition than Conventional Cultivars?

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 526
Author(s):  
Marco Milan ◽  
Silvia Fogliatto ◽  
Massimo Blandino ◽  
Francesco Vidotto
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Weather Conditions ◽  
Hybrid Wheat ◽  
Wheat Varieties ◽  
Canopy Development ◽  
Productivity Losses ◽  
Crop Density ◽  
Weed Infestation ◽  
Wheat Hybrids

Seeding rates of hybrid wheat varieties are typically much lower than conventional varieties due to their higher seed costs, which could potentially delay canopy development leading to greater weed pressures. To test whether hybrid wheat crops are more affected by weed pressure than conventional cultivars, a conventional variety (“Illico”) and a hybrid (“Hystar”), were compared in a three-year (2012–2016) field study at two sites in Northern Italy. Weed infestation was mainly characterized by weeds with an early growth pattern, and in only a few seasons did the hybrid crops show a higher weed density than the conventional cultivar. Despite the lower sowing rate, hybrids were able to achieve a similar crop density to the conventional cultivar even in years of delayed sowing or dry weather conditions. Normalized Difference Vegetation Index values were generally similar between cultivars across the years, regardless of the presence of weeds, except during the springtime. Occasionally, the test weight was significantly higher in weeded plots than un-weeded plots. Overall, the two cultivars showed similar yields within the same year. These results indicate that on fields with a low weed burden, and where these weeds emerge early, cultivars may not be significantly affected by productivity losses.

scholarly journals Correlation of the vegetation index (NDVI) and phytometric parameters at different stages of field crops development

2020 ◽  
Author(s):  
Yu.A. Gulyanov ◽  
Keyword(s):  
Spring Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Stem Elongation ◽  
Grain Filling ◽  
Growing Season ◽  
Practical Application ◽  
Field Crops ◽  
The Relationship

The main goal of our research was to identify the relationship between the normalized difference vegetation index (NDVI) and the area of assimilation surface (AS) of spring wheat crops during the growing season, as well as to develop practical application of the findings. Throughout the growing season, the area of assimilation surface of T. aestivum increases much faster than the vegetation index NDVI. The smallest AS (282.7 m2/ha), which corresponded to 0.01 units of the NDVI (calculated factor) was observed during the tillering stage. It reaches its maximum values – 331.7–406.1–383.7 m2/ha (1.20–1.47–1.39 times higher) from stem elongation to the end of flowering. During the grain filling and maturation, these values decrease to 336.2 m2/ha but still are 1.19 times higher than the initial ones.

scholarly journals Identification of the QTL-allele System Underlying Two High-Throughput Physiological Traits in the Chinese Soybean Germplasm Population

2021 ◽  
Vol 12 ◽  
Author(s):  
Lei Wang ◽  
Fangdong Liu ◽  
Xiaoshuai Hao ◽  
Wubin Wang ◽  
Guangnan Xing ◽  
...  
Keyword(s):  
High Throughput ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Genome Wide Association Study ◽  
Agronomic Traits ◽  
Block Design ◽  
Physiological Traits ◽  
Growth And Yield ◽  
Genetic Changes ◽  
Soybean Germplasm

The QTL-allele system underlying two spectral reflectance physiological traits, NDVI (normalized difference vegetation index) and CHL (chlorophyll index), related to plant growth and yield was studied in the Chinese soybean germplasm population (CSGP), which consisted of 341 wild accessions (WA), farmer landraces (LR), and released cultivars (RC). Samples were evaluated in the Photosynthetic System II imaging platform at Nanjing Agricultural University. The NDVI and CHL data were obtained from hyperspectral reflectance images in a randomized incomplete block design experiment with two replicates. The NDVI and CHL ranged from 0.05–0.18 and 1.20–4.78, had averages of 0.11 and 3.57, and had heritabilities of 78.3% and 69.2%, respectively; the values of NDVI and CHL were both significantly higher in LR and RC than in WA. Using the RTM-GWAS (restricted two-stage multi-locus genome-wide association study) method, 38 and 32 QTLs with 89 and 82 alleles and 2–4 and 2–6 alleles per locus were identified for NDVI and CHL, respectively, which explained 48.36% and 51.35% of the phenotypic variation for NDVI and CHL, respectively. The QTL-allele matrices were established and separated into WA, LR, and RC submatrices. From WA to LR + RC, 4 alleles and 2 new loci emerged, and 1 allele was excluded for NDVI, whereas 6 alleles emerged, and no alleles were excluded, in LR + RC for CHL. Recombination was the major motivation of evolutionary differences. For NDVI and CHL, 39 and 32 candidate genes were annotated and assigned to GO groups, respectively, indicating a complex gene network. The NDVI and CHL were upstream traits that were relatively conservative in their genetic changes compared with those of downstream agronomic traits. High-throughput phenotyping integrated with RTM-GWAS provides an efficient procedure for studying the population genetics of traits.

scholarly journals Monitoring agricultural drought using geospatial techniques: a case study of Thal region of Punjab, Pakistan

2020 ◽  
Vol 11 (S1) ◽  
pp. 203-216 ◽  
Author(s):  
Muhammad Amin ◽  
Mobushir Riaz Khan ◽  
Sher Shah Hassan ◽  
Aftab Ahmad Khan ◽  
Muhammad Imran ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Wheat Yield ◽  
Standardized Precipitation Index ◽  
Weather Conditions ◽  
Agricultural Drought ◽  
Wheat Crop ◽  
Drought Risk ◽  
Risk Map ◽  
Agricultural Risk

Abstract The Thal region of Punjab often experiences dry weather conditions with extreme variability in rainfall on a spatiotemporal scale during Rabi cropping season. The current study assesses the impacts of agricultural drought on wheat crops for 2000–2015. MOD13Q1 and CHIRPS data were used for identifying and assessing variation in agricultural drought patterns and severity. Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Stress Vegetation Index (STVI) and wheat crop yield anomalies were computed to characterize the gravity of drought across the Thal region. The results indicate that the wheat Rabi cropping seasons of the years 2000–2002 experienced extreme agricultural drought, with a spatial difference in severity level causing low and poor yield, while the years 2011 and 2014 were almost normal among all the years, leaving varied impacts on wheat yield. The combined agricultural risk map was generated by integrating the agricultural and meteorological droughts severity maps. The combined risk map generated using weighted overlay analysis of all the parameters indicate that the total Thal area can be classified into slight, moderate and no drought covering 28.12, 12.76, and 59.12% respectively of the total area. Hence an agricultural risk map would be extremely helpful as a tool to guide the decision-making process for monitoring drought risk on agricultural productivity.

scholarly journals Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale

Agronomy ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 327 ◽  
Author(s):  
Remy Fieuzal ◽  
Vincent Bustillo ◽  
David Collado ◽  
Gerard Dedieu
Keyword(s):  
Relative Error ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Wheat Yield ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Optical Images ◽  
Multi Temporal ◽  
Input Variables ◽  
Sentinel 2

The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat, over a study site located in southwestern France. The methodology is based on the Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the Normalized Difference Vegetation Index (NDVI) is combined with the yield maps collected during the crop rotation, the agricultural season 2014 showing the lower level of performances with a coefficient of determination (R2) of 0.44 and a root mean square error (RMSE) of 8.13 quintals by hectare (q.h−1) (corresponding to a relative error of 12.9%), the three other years being associated with values of R2 close or upper to 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%). Moreover, the proposed approach allows estimating the crop yield throughout the agricultural season, by using the successive images acquired from the sowing to the harvest. In such cases, early and accurate yield estimates are obtained three months before the end of the crop cycle. At this phenological stage, only a slight decrease in performance is observed compared to the statistic obtained just before the harvest.

Sign in / Sign up

Export Citation Format

Share Document