scholarly journals Time-series Multi-spectral Imaging in Soybean for Improving Biomass and Genomic Prediction Accuracy

2021 ◽  
Author(s):  
Kengo Sakurai ◽  
Yusuke Toda ◽  
Hiromi Kajiya-Kanegae ◽  
Yoshihiro Ohmori ◽  
Yuji Yamasaki ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Field Experiments ◽  
Developmental Stages ◽  
Early Stage ◽  
Vegetation Indices ◽  
Optimal Timing ◽  
Growth Stages ◽  
Ms Imaging ◽  
Irrigation Levels

Multi-spectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of above-ground biomass (AGB) and determined which developmental stages should be used for accurate prediction in soybean. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early to late growth stages. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multi-trait genomic prediction. The accuracy of the prediction model increased starting at an early stage of growth (31 days after sowing). To predict phenotypic values of AGB, we employed multi-kernel genomic prediction. Consequently, the prediction accuracy of phenotypic values reached a maximum at a relatively early growth stage (38 days after sowing). Hence, the optimal timing for MS imaging may depend on the irrigation levels.

SENSITIVITY OF CROP PLANTS TO WATER STRESS AT SPECIFIC DEVELOPMENTAL STAGES: REEVALUATION OF EXPERIMENTAL FINDINGS

1995 ◽  
Vol 43 (2) ◽  
pp. 99-111 ◽  
Author(s):  
Zvi Plaut
Keyword(s):  
Water Stress ◽  
Irrigation Water ◽  
Field Experiments ◽  
Developmental Stages ◽  
Growth Period ◽  
Soluble Solids ◽  
Growth Stages ◽  
Mild Stress ◽  
Uniform Reduction ◽  
Different Growth Stages

It has been suggested that in many crops differences in sensitivity to water stress occur at different growth stages. Since identical amounts of water may be applied, irrespective of whether a crop is exposed to relatively severe and short periods of stress or to extended periods of mild stress, the responses to such differing conditions should be compared. Unfortunately, such a comparison has not been conducted in most studies on sensitivity to water stress at different growth stages. In the present study, based on three field experiments conducted for different purposes, such a comparison was made for three crops: corn, sunflower, and tomato. In corn, distinct responses of ear and kernel yields to the timing of water stress were found. Withdrawal of irrigation water during flowering and cob formation resulted in greater yield losses than during other stages, indicating that this is a critical growth stage. However, slight and uniform reduction of water during the entire growth period resulted in significantly less damage to kernel or ear production, although the total amount of water applied was similar to that under staged withdrawal. In sunflowers, the withdrawal of irrigation water even at noncritical growth stages caused a more marked reduction in grain yield than did a uniform reduction throughout the entire season. In tomatoes, on the other hand, the withdrawal of irrigation water during specific growth stages caused minimal damage to fruit and total soluble solids yield as compared with fully irrigated control; reduction of irrigation water throughout the season brought about a significant decrease in yield. The difference between these crops is interpreted on the basis of the determinance of their floral meristems.

Dormancy in peach (Prunus persica) flower buds. VI. Effects of gibberellins and an acylcyclohexanedione (trinexapac-ethyl) on bud morphogenesis in field experiments with orchard trees and on cuttings

2002 ◽  
Vol 80 (6) ◽  
pp. 664-674 ◽  
Author(s):  
Herminda Reinoso ◽  
Virginia Luna ◽  
Carlos Dauría ◽  
Richard P Pharis ◽  
Rubén Bottini
Keyword(s):  
Field Experiments ◽  
Prunus Persica ◽  
Developmental Stages ◽  
Early Stage ◽  
Competitive Inhibitor ◽  
Flower Bud ◽  
Bud Development ◽  
Morphological Alterations ◽  
Floral Bud ◽  
Ga Biosynthesis

The effects of several gibberellins (GAs), exo-16,17-dihydro GA5, 2,2-dimethyl GA4, and GA3, and trinexapac-ethyl (an acylcyclohexanedione inhibitor of late-stage GA biosynthesis), were assessed for their effects on flower bud development during and after winter dormancy in peach (Prunus persica (L.) Batsch.) in three field trials and one experiment using cuttings. At late developmental stages, GA3 hastened floral bud development and shortened the time to anthesis, whereas early-stage applications of GA3 either had no effect or delayed floral bud development. In contrast, an exceptionally growth-active GA, 2,2-dimethyl GA4, promoted floral bud development (tested only on cuttings) across a range of application dates. However, it also induced a high percentage of bud abscission and remaining buds had a necrotic gynoecium and alterations in the androecium. Surprisingly, trinexapac-ethyl also promoted floral bud development, although it was not as effective as GA1. Trinexapac-ethyl-treated buds also showed morphological alterations and gynoecium necrosis. However, the best and most consistent treatment for enhancing floral bud development and hastening flower anthesis was 16,17-dihydro GA5. It stimulated floral bud development in up to 80% of the treated buds. Further, the promotive effect of 16,17-dihydro GA5 was maintained through to anthesis across three years of field experiments on intact trees, as well as with cuttings. Whether 16,17-dihydro GA5, a competitive inhibitor of the 3β-hydroxylation step in GA biosynthesis, acts per se, acts via a metabolite (such as 16,17-dihydro GA3), or acts by modifying endogenous GA metabolism is not yet known.Key words: gibberellins, trinexapac-ethyl, floral bud morphogenesis, peach.

scholarly journals Assimilation of Remotely-Sensed LAI into WOFOST Model with the SUBPLEX Algorithm for Improving the Field-Scale Jujube Yield Forecasts

2019 ◽  
Vol 11 (16) ◽  
pp. 1945
Author(s):  
Tiecheng Bai ◽  
Shanggui Wang ◽  
Wenbo Meng ◽  
Nannan Zhang ◽  
Tao Wang ◽  
...  
Keyword(s):  
Developmental Stages ◽  
Vegetation Indices ◽  
Remotely Sensed ◽  
High Yield ◽  
Bias Error ◽  
Growth Stages ◽  
Landsat 8 ◽  
Statistical Regression ◽  
Field Scale ◽  
Remotely Sensed Data

In order to enhance the simulated accuracy of jujube yields at the field scale, this study attempted to employ SUBPLEX algorithm to assimilate remotely sensed leaf area indices (LAI) of four key growth stages into a calibrated World Food Studies (WOFOST) model, and compare the accuracy of assimilation with the usual ensemble Kalman filter (EnKF) assimilation. Statistical regression models of LAI and Landsat 8 vegetation indices at different developmental stages were established, showing a validated R2 of 0.770, 0.841, 0.779, and 0.812, and a validated RMSE of 0.061, 0.144, 0.180, and 0.170 m2 m−2 for emergence, fruit filling, white maturity, and red maturity periods. The results showed that both SUBPLEX and EnKF assimilations significantly improved yield estimation performance compared with un-assimilated simulation. The SUBPLEX (R2 = 0.78 and RMSE = 0.64 t ha−1) also showed slightly better yield prediction accuracy compared with EnKF assimilation (R2 = 0.73 and RMSE = 0.71 t ha−1), especially for high-yield and low-yield jujube orchards. SUBPLEX assimilation produced a relative bias error (RBE, %) that was more concentrated near zero, being lower than 10% in 80.1%, and lower than 20% in 96.1% for SUBPLEX, 72.4% and 96.7% for EnKF, respectively. The study provided a new assimilation scheme based on SUBPLEX algorithm to employ remotely sensed data and a crop growth model to improve the field-scale fruit crops yield estimates.

scholarly journals Changes to soil water content and biomass yield under combined maize and maize-weed vegetation with different fertilization treatments in loam soil

2016 ◽  
Vol 64 (2) ◽  
pp. 150-159 ◽  
Author(s):  
Éva Lehoczky ◽  
Mariann Kamuti ◽  
Nikolett Mazsu ◽  
Renáta Sándor
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Profile ◽  
Field Experiments ◽  
Developmental Stages ◽  
Early Stage ◽  
Crop Growth ◽  
Long Term Study ◽  
Loam Soil

AbstractEspecially during early developmental stages, competition with weeds can reduce crop growth and have a serious effect on productivity. Here, the effects of interactions between soil water content (SWC), nutrient availability, and competition from weeds on early stage crop growth were investigated, to better understand this problem. Field experiments were conducted in 2013 and 2014 using long-term study plots on loam soil in Hungary. Plots of maize (Zea maysL.) and a weed-maize combination were exposed to five fertilization treatments. SWC was observed along the 0–80 cm depth soil profile and harvested aboveground biomass (HAB) was measured.Significant differences were found between SWC in maize and maize-weed plots. In all treatments, measured SWC was most variable in soil depths of up to 50 cm, and at the 8–10 leaves (BBCH19) growth stage of the crop. The greatest depletion of SWC was detected within PK treatments across the entire soil profile and under both vegetation types, with depletion also considerable under NPK and NP treatments. Biomass growth was significantly influenced by weeds in treated plots between the BBCH 13 and 19 phenological stages, but water availability did not hamper growth rates in non-fertilized conditions. These findings suggest that, at early stages of crop growth, SWC model simulations need to include better characterisation of depth- and structure-dependent soil water uptake by vegetation.

scholarly journals Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing

2021 ◽  
Vol 13 (17) ◽  
pp. 3390
Author(s):  
Fumin Wang ◽  
Xiaoping Yao ◽  
Lili Xie ◽  
Jueyi Zheng ◽  
Tianyue Xu
Keyword(s):  
Remote Sensing ◽  
Field Experiments ◽  
Rice Yield ◽  
Vegetation Indices ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Spectral Information ◽  
Growth Stages ◽  
Yield Estimation ◽  
First Derivative

Rice floret number per unit area as one of the key yield structure parameters is directly related to the final yield of rice. Previous studies paid little attention to the effect of the variations in vegetation indices (VIs) caused by rice flowering on rice yield estimation. Unmanned aerial vehicles (UAV) equipped with hyperspectral cameras can provide high spatial and temporal resolution remote sensing data about the rice canopy, providing possibilities for flowering monitoring. In this study, two consecutive years of rice field experiments were conducted to explore the performance of florescence spectral information in improving the accuracy of VIs-based models for yield estimates. First, the florescence ratio reflectance and florescence difference reflectance, as well as their first derivative reflectance, were defined and then their correlations with rice yield were evaluated. It was found that the florescence spectral information at the seventh day of rice flowering showed the highest correlation with the yield. The sensitive bands to yield were centered at 590 nm, 690 nm and 736 nm–748 nm, 760 nm–768 nm for the first derivative florescence difference reflectance, and 704 nm–760 nm for the first derivative florescence ratio reflectance. The florescence ratio index (FRI) and florescence difference index (FDI) were developed and their abilities to improve the estimation accuracy of models basing on vegetation indices at single-, two- and three-growth stages were tested. With the introduction of florescence spectral information, the single-growth VI-based model produced the most obvious improvement in estimation accuracy, with the coefficient of determination (R2) increasing from 0.748 to 0.799, and the mean absolute percentage error (MAPE) and the root mean squared error (RMSE) decreasing by 11.8% and 10.7%, respectively. Optimized by flowering information, the two-growth stage VIs-based model gave the best performance (R2 = 0.869, MAPE = 3.98%, RMSE = 396.02 kg/ha). These results showed that introducing florescence spectral information at the flowering stage into conventional VIs-based yield estimation models is helpful in improving rice yield estimation accuracy. The usefulness of florescence spectral information for yield estimation provides a new idea for the further development and improvement of the crop yield estimation method.

scholarly journals Split application of reduced nitrogen rate improves nitrogen uptake and use efficiency in sweetpotato

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Xiangbei Du ◽  
Min Xi ◽  
Lingcong Kong
Keyword(s):  
Field Experiments ◽  
Developmental Stages ◽  
N Uptake ◽  
Partition Ratio ◽  
Growth Stages ◽  
Storage Root ◽  
Split Application ◽  
N Application ◽  
Use Efficiency ◽  
Partial Factor Productivity

Abstract Splitting nitrogen (N) application is beneficial for enhancing sweetpotato growth and promoting optimum yields under reduced N rates; however, studies concerning how split N can affect sweetpotato N dynamics and utilization are limited. Field experiments were conducted from 2015 to 2016 to determine how split N application affects sweetpotato N uptake and N use efficiency (NUE) under a reduced N rate. Two cultivars (Xushu 22 and Shangshu 19) were planted under four N treatments, a conventional basal application of 100 kg N ha−1 (100:0), a basal application of 80 kg N ha−1 (80:0), two equal split applications of 80 kg N ha−1 (basal and 35 days after transplanting, 40:40) and a N omission treatment (N0). Data from two years revealed that sweetpotato yields decreased at a reduced 20% N rate with a basal application (80:0); however, the reduced 20% N rate with a split application (40:40) significantly increased the yield by 16.6–19.0%. Although the 80:0 treatment decreased sweetpotato N uptake, the 40:40 treatment increased the N uptake by increasing the N uptake rate and prolonging the duration of the fast N uptake phase. In comparison to the basal application, the split N application used N more efficiently, showing consistently higher levels of agronomic use efficiency, recovery efficiency, physiological efficiency and partial factor productivity. NUEs under split N improved due to increased N uptake during the middle and late growth stages and a higher N partition ratio to the storage root. The above results indicate that split N application provides better N for crop developmental stages and is recommended as an alternative approach to simultaneously increasing storage root yield and NUE under a reduced N rate in sweetpotato production in China.

scholarly journals Genome-Wide Association Mapping and Genomic Prediction of Anther Extrusion in CIMMYT Hybrid Wheat Breeding Program via Modeling Pedigree, Genomic Relationship, and Interaction With the Environment

2020 ◽  
Vol 11 ◽  
Author(s):  
Anil Adhikari ◽  
Bhoja Raj Basnet ◽  
Jose Crossa ◽  
Susanne Dreisigacker ◽  
Fatima Camarillo ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Field Experiments ◽  
Wheat Breeding ◽  
Genome Wide Association ◽  
Hybrid Wheat ◽  
Test Set ◽  
Genome Wide ◽  
Anther Extrusion

Anther extrusion (AE) is the most important male floral trait for hybrid wheat seed production. AE is a complex quantitative trait that is difficult to phenotype reliably in field experiments not only due to high genotype-by-environment effects but also due to the short expression window in the field condition. In this study, we conducted a genome-wide association scan (GWAS) and explored the possibility of applying genomic prediction (GP) for AE in the CIMMYT hybrid wheat breeding program. An elite set of male lines (n = 603) were phenotype for anther count (AC) and anther visual score (VS) across three field experiments in 2017–2019 and genotyped with the 20K Infinitum is elect SNP array. GWAS produced five marker trait associations with small effects. For GP, the main effects of lines (L), environment (E), genomic (G) and pedigree relationships (A), and their interaction effects with environments were used to develop seven statistical models of incremental complexity. The base model used only L and E, whereas the most complex model included L, E, G, A, and G × E and A × E. These models were evaluated in three cross-validation scenarios (CV0, CV1, and CV2). In cross-validation CV0, data from two environments were used to predict an untested environment; in random cross-validation CV1, the test set was never evaluated in any environment; and in CV2, the genotypes in the test set were evaluated in only a subset of environments. The prediction accuracies ranged from −0.03 to 0.74 for AC and −0.01 to 0.54 for VS across different models and CV schemes. For both traits, the highest prediction accuracies with low variance were observed in CV2, and inclusion of the interaction effects increased prediction accuracy for AC only. In CV0, the prediction accuracy was 0.73 and 0.45 for AC and VS, respectively, indicating the high reliability of across environment prediction. Genomic prediction appears to be a very reliable tool for AE in hybrid wheat breeding. Moreover, high prediction accuracy in CV0 demonstrates the possibility of implementing genomic selection across breeding cycles in related germplasm, aiding the rapid breeding cycle.

The Convolution Neural Network Combined with the HT Person Fit Statistic to Develop an APP for Detecting Dengue Fever in Children: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
CHIEN WEI ◽  
Chi Chow Julie ◽  
Chou Willy
Keyword(s):  
Neural Network ◽  
Dengue Fever ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Family Members ◽  
Convolution Neural Network ◽  
Public Health Issue ◽  
Person Fit ◽  
Model Accuracy ◽  
Comparison Results

UNSTRUCTURED Backgrounds: Dengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN), a well-established deep learning method, can improve prediction accuracy on account of its usage of a large number of parameters for modeling. Whether the HT person fit statistic can be combined with CNN to increase the prediction accuracy of the model and develop an application (APP) to detect DF in children remains unknown. Objectives: The aim of this study is to build a model for the automatic detection and classification of DF with symptoms to help patients, family members, and clinicians identify the disease at an early stage. Methods: We extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF) using CNN to predict DF risk. The accuracy of two sets of characteristics (19 symptoms and four other variables, including person mean, standard deviation, and two HT-related statistics matched to DF+ and DF−) for predicting DF, were then compared. Data were separated into training and testing sets, and the former was used to predict the latter. We calculated the sensitivity (Sens), specificity (Spec), and area under the receiver operating characteristic curve (AUC) across studies for comparison. Results: We observed that (1) the 23-item model yields a higher accuracy rate (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90) based on the 177-case training set; (2) the Sens values are almost higher than the corresponding Spec values (90% in 10 scenarios) for predicting DF; (3) the Sens and Spec values of the 23-item model are consistently higher than those of the 19-item model. An APP was subsequently designed to detect DF in children. Conclusion: The 23-item model yielded higher accuracy rates (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90). An APP could be developed to help patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

scholarly journals Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Run Yu ◽  
Lili Ren ◽  
Youqing Luo
Keyword(s):  
Early Stage ◽  
Vegetation Indices ◽  
Pine Wilt Disease ◽  
Wilt Disease ◽  
Pinus Tabulaeformis ◽  
Pine Tree ◽  
Preventative Measures ◽  
Pine Wilt ◽  
Pine Trees ◽  
Airborne Vehicle

Abstract Background Pine wilt disease (PWD) is a major ecological concern in China that has caused severe damage to millions of Chinese pines (Pinus tabulaeformis). To control the spread of PWD, it is necessary to develop an effective approach to detect its presence in the early stage of infection. One potential solution is the use of Unmanned Airborne Vehicle (UAV) based hyperspectral images (HIs). UAV-based HIs have high spatial and spectral resolution and can gather data rapidly, potentially enabling the effective monitoring of large forests. Despite this, few studies examine the feasibility of HI data use in assessing the stage and severity of PWD infection in Chinese pine. Method To fill this gap, we used a Random Forest (RF) algorithm to estimate the stage of PWD infection of trees sampled using UAV-based HI data and ground-based data (data directly collected from trees in the field). We compared relative accuracy of each of these data collection methods. We built our RF model using vegetation indices (VIs), red edge parameters (REPs), moisture indices (MIs), and their combination. Results We report several key results. For ground data, the model that combined all parameters (OA: 80.17%, Kappa: 0.73) performed better than VIs (OA: 75.21%, Kappa: 0.66), REPs (OA: 79.34%, Kappa: 0.67), and MIs (OA: 74.38%, Kappa: 0.65) in predicting the PWD stage of individual pine tree infection. REPs had the highest accuracy (OA: 80.33%, Kappa: 0.58) in distinguishing trees at the early stage of PWD from healthy trees. UAV-based HI data yielded similar results: the model combined VIs, REPs and MIs (OA: 74.38%, Kappa: 0.66) exhibited the highest accuracy in estimating the PWD stage of sampled trees, and REPs performed best in distinguishing healthy trees from trees at early stage of PWD (OA: 71.67%, Kappa: 0.40). Conclusion Overall, our results confirm the validity of using HI data to identify pine trees infected with PWD in its early stage, although its accuracy must be improved before widespread use is practical. We also show UAV-based data PWD classifications are less accurate but comparable to those of ground-based data. We believe that these results can be used to improve preventative measures in the control of PWD.

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

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