Predicting Yield Loss Research Articles

Effect of Barley Yellow Dwarf Virus (BYDV) on Barley: A Precise Assessment of Reductions in Yield Components Under Variable Disease Severities.

Understanding the effects of barley yellow dwarf virus (BYDV) on crop agronomic traits and yield performance helps breeders balance their selection criteria and farmers decide if pesticides should be applied to control aphids that distribute the virus. To precisely assess the deterioration of different agronomic traits and yield components caused by different levels of BYDV infection, seeds of a BYDV-sensitive barley cultivar RGT Planet were space sown in a field plot with 10 cm between seeds and 20 cm between rows under two consecutive years. When BYDV symptoms were shown, plants with different levels (0 to 5) of BYDV infection were tagged. For accurate comparisons, the neighboring non/less-infected plants were also tagged. At maturity, different agronomic traits and yield components were measured on those tagged plants. Results showed a strong linear correlation between BYDV severity and the performance of agronomic traits and yield components. The yield reductions ranged from 30% for the least affected (score of 1) to 90% for the severely affected (score of 5). Our research confirmed previous findings that BYDV seriously affects crop yield and the prediction of yield loss due to BYDV infection should use the percentage of plants with different BYDV symptoms.

Prediction of Severity and Yield Losses in Wheat Caused by Yellow Rust Disease

Prediction of Severity and Yield Losses in Wheat Caused by Yellow Rust Disease

Prediction of Severity and Yield Losses in Wheat Caused by Yellow Rust Disease

Prediction of Severity and Yield Losses in Wheat Caused by Yellow Rust Disease

High-Volume SiC Epitaxial Layer Manufacturing-Maintaining High Materials Quality of Lab Results in Production

Typically, research and development (R&D) results of epitaxial layer growth show superior properties of the grown layers compared to high volume results. Layer uniformities are excellent and achieved defect densities are low compared to typical results. In particular, the conversion of basal plane dislocations (BPD) from the silicon carbide (SiC) substrate is in focus to reduce bipolar degradation of p-n-junctions. It is a great challenge to maintain those excellent results in high-volume manufacturing considering all the factors that impact the properties of the epilayer. Thus, quality of the layers, high throughput and low cost have to be assessed to find a compromise between these key factors. In this paper we present results on the growth of epitaxial layers on 150 mm and 200 mm 4° off-oriented 4H-SiC substrates using warm-wall multi-wafer chemical vapor deposition (CVD) systems. Single wafer data of the key epitaxial layer parameters, thickness, doping and defect densities, are compared to batch and lot results, as well as to statistical data of several hundreds of wafers produced. Improvements in wafer-to-wafer (w-t-w) doping uniformity could be achieved for instance by implementation of an on-wafer temperature measurement. Substrate impact on defect levels is shown comparing X-ray topography (XRT) results of bare substrate wafers and defect analysis of epilayers on sister wafers from the same crystal. Statistical defect data and resulting predicted yield loss also show a dependence on substrate suppliers. For the first time we show w-t-w and run-to-run (r-t-r) results of doping and thickness measurements on 200 mm substrates. Also, defect results of epilayers on 200 mm wafers are compared to results on 150 mm.

Spatially refined salinity hazard analysis in gypsum‐affected irrigated soils

AbstractThe global extent of salt‐affected agricultural land, 20% of which is deemed gypsiferous, results in billions of dollars of annual economic loss, a serious problem deserving of attention. However, the analysis of gypsiferous saline soils, such as in the irrigated Lower Arkansas River Valley (LARV) of Colorado, can result in an inflated estimation of soil salinity when using the traditional soil saturated paste extract electrical conductivity (ECe), leading to inaccurate crop yield loss predictions and misguided decisions for remediation. Sparingly soluble gypsum (CaSO4 2H2O) in these soils dissolves more readily during laboratory preparation of saturated paste extracts because of excess soil water dilution coupled with sample disturbance. We present a pragmatic linear‐regression approach to correct for this phenomenon, calibrated using two adapted methods for correcting ECe on an individual sample basis. The novel approach used electrical conductivity of pore water samples from saline fields to evaluate the accuracy of the correction methods. The approach was applied on soil samples from two surface‐irrigated, saline fields in the LARV, which were mapped using electromagnetic induction data and analysis of covariance linear regression, calibrated for ECe and ECe corrected for excess gypsum dissoultion (ECeg). Average ECeg values are as much as 26% lower than uncorrected ECe in gypsum‐biased portions of the fields. Estimation of corn salinity hazard in these gypsum‐affected areas using ECeg in lieu of ECe in a traditional yield response function generated mean relative yield values that are higher by up to 13 percentage points. We discuss lessons learned and suggest enhancements to the techniques.

Photosynthetic Performance and Yield Losses of Winter Rapeseed (Brassica napus L. var. napus) Caused by Simulated Hail.

Winter oilseed rape (Brassica napus L.), Europe's foremost oilseed crop, is significantly impacted by hailstorms, leading to substantial yield reductions that are difficult to predict and measure using conventional methods. This research aimed to assess the effectiveness of photosynthetic efficiency analysis for predicting yield loss in winter rapeseed subjected to hail exposure. The aim was to pinpoint the chlorophyll fluorescence parameters most affected by hail stress and identify those that could act as non-invasive biomarkers of yield loss. The study was conducted in partially controlled conditions (greenhouse). Stress was induced in the plants by firing plastic balls with a 6 mm diameter at them using a pneumatic device, which launched the projectiles at speeds of several tens of meters per second. Measurements of both continuous-excitation and pulse-modulated-amplitude chlorophyll fluorescence were engaged to highlight the sensitivity of the induction curve and related parameters to hail stress. Our research uncovered that some parameters such as Fs, Fm', ΦPSII, ETR, Fo, Fv/Fm, and Fv/Fo measured eight days after the application of stress had a strong correlation with final yield, thus laying the groundwork for the creation of new practical protocols in agriculture and the insurance industry to accurately forecast damage to rapeseed crops due to hail stress.

Time point of virus yellows infection is crucial for yield losses in sugar beet, and co‐infection with beet mosaic virus is negligible under field conditions

AbstractBeet chlorosis virus (BChV), beet mild yellowing virus (BMYV) and beet yellows virus (BYV) transmitted by Myzus persicae cause virus yellows (VY) disease in sugar beet. M. persicae also transmits beet mosaic virus (BtMV), which is often associated with VY. So far, field trials to determine the effect of infection time point on yield have used 100% inoculation density and little is known about the yield effect of BtMV in mixed infections with VY species under field conditions. Therefore, we conducted sugar beet field trials using a new inoculation protocol with densities of 3%–10% in combination with different infection time points; we also tested the effect of BtMV/VY species mixed infections on white sugar yield (WSY). We observed a wide range of WSY losses for BChV (3.6%–26.8%), BMYV (1.7%–22.0%) and BYV (3.7%–37.0%) depending on infection time point, with no further significant losses after BBCH Stages 18/19, 35 and 39, respectively. Both the time of infection and area under disease progress curve showed excellent correlation with WSY losses for all VY species. BtMV had no significant effect on WSY losses either as a single infection or in mixed infections with BChV or BYV compared to control or single infections of these viruses. However, BMYV/BtMV mixed infection showed significantly increased WSY loss (+13.6%) compared to single BMYV infection. Our results can be used to predict yield losses in practical fields and to develop economic control thresholds for decision support systems.

Weed Biology and Ecology Studies: Diversity, Dominance and Prediction of Yield Loss of Corn (Zea mays L.) Due to Broadleaf Weeds Competition in Dryland

The aim of the research was to determine the diversity, distribution, dominance, and yield loss of corn due to competition from broadleaf weeds. Descriptive research method with direct survey techniques of research objects. Data were collected using 10 sample plots measuring 1 m2, which were placed using random sampling. Observation parameters include the population and dry biomass weight of weeds and corn. The data was analyzed descriptively and comparatively by calculating density, frequency, and dominance to obtain an important value index and summation diminance ratio (SDR). The results of the research showed that 14 species of broad-leaved weeds were found with high species diversity, distribution, dominance, and abundance indices. The competitiveness and relative weighted dominance of six weed species, namely Amaranthus spinusus, Amaranthus gracilis, Synedrella nodiliflora, Acalypha indica, Ageratum conyzoides, and Alternanthera philoxeroides, are higher than those of other weed species, causing corn yield losses of 5.56%–21.90%. Therefore, these six weed species must be controlled.

IMPACT OF SOIL STERILIZATION FROM DIFFERENT LOCATIONS, WHEAT SEEDS CLEANING AND THEIR INTERACTIONS ON WEED CONTROL, AND YIELD AND YIELD COMPONENTS

IMPACT OF SOIL STERILIZATION FROM DIFFERENT LOCATIONS, WHEAT SEEDS CLEANING AND THEIR INTERACTIONS ON WEED CONTROL, AND YIELD AND YIELD COMPONENTS

Nutrient deficiency identification and yield-loss prediction in leaf images of groundnut crop using transfer learning

Nutrient deficiency identification and yield-loss prediction in leaf images of groundnut crop using transfer learning

Adaptability of wheat to future climate change: Effects of sowing date and sowing rate on wheat yield in three wheat production regions in the North China Plain

Adjusting sowing dates and sowing rates is a key adaptation strategy for adapting to future climate change, and maintaining wheat production in the North China Plain (NCP). However, it is still unclear whether the current sowing date and sowing rate can adapt to future climate change, and how to adjust the sowing date and sowing rate to compensate for the adverse effects of climate change on wheat yields. This study predicts the adaptability of agricultural management practices like sowing dates and sowing rates, to future climate change in three wheat regions by referring to four global climate models (GCMs) and AquaCrop model. Population structure and yield were maximized for sowing dates from Oct.11–20 and sowing rates of 10–13 kg/667 m2 (or 13–16 kg/667 m2) in 2016–2021. Three wheat regions were expected to show a warming trend, while the total precipitation has large spatial fluctuations under both representative concentration pathways (rcp) scenarios in the 2022–2100. AquaCrop model could simulate yield with a good precision (RMSE≤1043.7 kg/ha). Compared to the average yield of the baseline period (2016–2021), in the 2022–2100, the average predicted wheat yields of three wheat regions simulated based on the current optimal sowing date and sowing rate decreased by 5.45 % ∼ 11.05 % (9.35 % ∼ 16.84 %) and 2.57 % ∼ 10.95 % (6.97 % ∼ 12.75 %) under the rcp4.5 (rcp8.5), respectively. Average wheat yield losses were effectively compensated when the combinations of Oct.15 and 14 kg/667 m2 for the dryland wheat, Oct.21 and 14 kg/667 m2 for the irrigated wheat, and Oct.21 and 13 kg/667 m2 for the high-yield-rainfed wheat were applied under both rcp scenarios, respectively, with predicted yield losses of −4.17 %, −3.50 %, and − 3.25 %. Thus, adjusting sowing dates and sowing rates are viable options to effectively address the adverse effects of future global climate change, thereby guaranteeing food security in the NCP.

Assessment of yield loss and avoidable yield loss due to southern leaf blight of maize and development of yield loss prediction model

Assessment of yield loss and avoidable yield loss due to southern leaf blight of maize and development of yield loss prediction model

Impact of disease severity on infected bunches upon a yield of grape variety Vranec, caused by Plasmopara viticola (Berk. & M.A. Curtis) Berl and De Toni

Impact of disease severity on infected bunches upon a yield of grape variety Vanec, caused by Plasmopara viticola (Berk. & M.A. Curtis) Berl. & De Toni., every year causes damage to the yield of vines by infection the bunches while they are still unripe. It is essential to assess the severity of Downy Mildew in vineyards to predict yield loss accurately. The software platform 'image J' was used to detect and quantify the disease severity by measuring infected berries relative to healthy tissue. Regression analysis was used as a statistical method to predict yield loss. Plasmopara viticola was monitored in 2022 to find a rational solution to build a Yield Loss Forecast Model. The results of theoretical assumptions compared with actual field situations show that Yield Loss Forecasting Model within the allowed statistical range approximately predicts the yield loss of the control variant.

MAJOR PEST OF MANGO AND MANAGEMENT PRACTICE AT FARMER LEVEL IN SAPTARI, NEPAL

Traditionally used methods to control insect pests are locally accepted, cost-effective, and sustainable to reduce resistance and foster long-term benefits. A household study was carried out in Surunga municipality in three wards of Saptari, Nepal namely Madhupatti, Sripur, and Haripur, Saptari to analyze the principal mango pest and farmer management practices. The moderate climate and edaphic characteristics of the Terai region are favorable for mango production in the district. Grafting was utilized to raise 93.3% of the total plant population in the respondent’s orchards, according to the data. The number of female family members involved in mango cultivation (88) was higher than the number of male family members (81). Gummosis, followed by Sooty Mould and Anthracnose, was the most common and devastating disease in the study area. Mango Hopper was determined to be the most common and harmful insect, followed by Fruit fly and Leaf Webber. With a mean of 15.6533% of the total predicted yield loss. In the study area, local management measures against diseases and pests were used less frequently. Also, 72% of the respondents didn’t use chemical insecticides/fungicides for the control of diseases and pests, and relevant organizations working in the area have failed to reach the majority of farmers in the research area.

Efficient Prediction of Blast Disease in Paddy Plant using Optimized Support Vector Machine

Diagnostics of plant diseases are essential to predicting yield loss, detecting infections, studying host–pathogen interactions, and detecting host resistance. Early detection of plant leaf disease can increase yields. This is possible if there are automated systems to help farmers diagnose rice diseases from the pictures of plant leaves. Therefore, automatic leaf detection using machine learning is proposed in this study. The proposed approach has three stages, namely, pre-processing, feature extraction, and classification. Initially, the input image is converted into red, green and blue and the noise in the green band is removed using a median filter. Initially, the input image is converted into red, green and blue and the noise in the green band is removed using a median filter. Then, important features of the green band are extracted. An Optimized Support Vector Machine (OSVM) classifier uses the extracted features to classify an image as normal or pathological. To improve SVM performance, the SVM parameters are chosen optimally using the Adaptive Sunflower Optimization (ASFO) algorithm. Then, the infected region is separated using a level-set segmentation algorithm. The efficiency of our work is analyzed based on accuracy, sensitivity, and specificity, the proposed method reached the maximum accuracy of 97.54% for plant disease prediction.

Crop models for assessing impact and adaptation options under climate change

Increased amount of green house gases (GHG) in the atmosphere will cause climate change that will adversely impact crop production especially in the arid and semi-arid regions of the developing countries. Development and implementation of field level adaptations measures to cope up with climate change are necessary to the farmers whose livelihood depends on crop-based income. Crop simulation models that incorporate soil-crop-climate processes of plant growth and that are sensitive to climate change factors can be used to quantify impact of climate change on crop production and evaluating and prioritizing adaptation measures at farm level. This paper analyses the impacts of climate change and plausible agronomic, land and water management and genetic adaptations options for the major crops of the semi-arid tropical region with examples from selected sites in India and other developing countries. The crop models need to be linked to the improved pest, disease and weed models to analyze and predict yield losses, especially those due to climate change. The simulation models also need to incorporate the impact of extreme weather events on crop production that is projected to increase with climate change.

Whether curse or blessing: A counterintuitive perspective on global pest thrips infestation under climatic change with implications to agricultural economics

The improvement and application of pest models to predict yield losses is still a challenge for the scientific community. However, pest models were targeted chiefly towards scheduling scouting or pesticide applications to deal with pest infestation. Thysanoptera (thrips) significantly impact the productivity of many economically important crops worldwide. Until now, no comprehensive study is available on the global distribution of pest thrips, as well as on the extent of cropland vulnerability worldwide. Further, nothing is known about the climate change impacts on these insects. Thus the present study was designed to map the global distribution and quantify the extent of cropland vulnerability in the present and future climate scenarios using data of identified pest thrips within the genus, i.e., Thrips, Frankliniella, and Scirtothrips. Our found significant niche contraction under the climate change scenarios and thrips may reside primarily in their thermal tolerance thresholds. About 3,98,160 km2 of cropland globally was found to be affected in the present scenario. However, it may significantly reduce to 5530 Km2 by 2050 and 1990 km2 by 2070. Further, the thrips distribution mostly getting restricted to Eastern North America, the North-western of the Indian sub-continent, and the north of Europe. Among all realms, thrips may lose ground in the Indo-Malayan realm at the most and get restricted to only 27 out of 825 terrestrial ecoregions. The agrarian communities of the infested regions may get benefit if these pests get wiped out, but on the contrary, we may lose species diversity. Moreover, the vacated niche may attract other invasive species, which may seriously impact the species composition and agricultural productivity. The present study findings can be used in making informed decisions about prioritizing future economic and research investments on the thrips in light of anticipated climate change impacts.

Critical-point yield loss models based on incidence and severity of wheat head blast epidemics in the Brazilian Cerrado

Wheat head blast (WHB), caused by the fungus Pyricularia oryzae Triticum pathotype, is one of the most damaging diseases of wheat. In Brazil, the disease has limited the expansion of wheat in the tropical regions of the Cerrado biome. In this study, wheat yield (Y) and visual estimates of WHB incidence (I) and severity (S) (assessed 25 to 30 days after flowering) were measured in a single location in the Cerrado (Minas Gerais state) where field experiments were conducted during seven sequential years (2013 to 2019) using three cultivars and 12 planting dates each year. Pearson correlation (r) analysis, used to study the association between each of the three disease variables (I, DHS = diseased-head severity and S) and Y, under a meta-analytic framework, showed the strongest association between incidence and yield (rI-Y = −0.92) and severity and yield (rS-Y = −0.90). The functional relationship between WHB intensity (I or S) and Y was studied based on the fit of a random coefficients model to I or S and the log-transformed Y data. The population average estimated intercept and slope for the I or S and log-Y relationships were 7.798 log units (2436.6 kg/ha) and − 0.020 for the I-Y relationship and 7.721 log units (2255.5. kg/ha) and − 0.030 for the S-Y relationship. The relative (percent) yield loss when I or S reaches 10% was predicted at 18.4% and 26.1%, respectively. This study provides a basis for understanding and predicting yield losses due to wheat blast based on the visual assessment of two disease variables assessed at a single time during the epidemics.

Stepwise Regression Models-Based Prediction for Leaf Rust Severity and Yield Loss in Wheat

Leaf rust is a devastating disease in wheat crop. The disease forecasting models can facilitate the economic and effective use of fungicides and assist in limiting crop yield losses. In this study, six wheat cultivars were screened against leaf rust at two locations, during three consecutive growing seasons. Subsequently, the stepwise regression analysis was employed to analyze the correlation of six epidemiological variables (minimum temperature, maximum temperature, minimum relative humidity, maximum relative humidity, rainfall and wind speed) with disease severity and yield loss (%). Disease predictive models were developed for each cultivar for final leaf rust severity and yield loss prediction. Principally, all epidemiological variables indicated a positive association with leaf rust severity and yield loss (%) except minimum relative humidity. The effectiveness of disease predictive models was estimated using coefficient of determination (R2) values for all models. Then, these predictive models were validated to forecast disease severity and yield loss at another location in Faisalabad. The R2 values of all disease predictive models for each of the tested cultivars were high, evincing that our regression models could be effectively employed to predict leaf rust disease severity and anticipated yield loss. The validation results explained 99% variability, suggesting a highly accurate prediction of the two variables (leaf rust severity and yield loss). The models developed in this research can be used by wheat farmers to forecast disease epidemics and to make disease management decisions accordingly.

Attention-optimized DeepLab V3 + for automatic estimation of cucumber disease severity

BackgroundAutomatic and accurate estimation of disease severity is critical for disease management and yield loss prediction. Conventional disease severity estimation is performed using images with simple backgrounds, which is limited in practical applications. Thus, there is an urgent need to develop a method for estimating the disease severity of plants based on leaf images captured in field conditions, which is very challenging since the intensity of sunlight is constantly changing, and the image background is complicated.ResultsThis study developed a simple and accurate image-based disease severity estimation method using an optimized neural network. A hybrid attention and transfer learning optimized semantic segmentation model was proposed to obtain the disease segmentation map. The severity was calculated by the ratio of lesion pixels to leaf pixels. The proposed method was validated using cucumber downy mildew, and powdery mildew leaves collected under natural conditions. The results showed that hybrid attention with the interaction of spatial attention and channel attention can extract fine lesion and leaf features, and transfer learning can further improve the segmentation accuracy of the model. The proposed method can accurately segment healthy leaves and lesions (MIoU = 81.23%, FWIoU = 91.89%). In addition, the severity of cucumber leaf disease was accurately estimated (R2 = 0.9578, RMSE = 1.1385). Moreover, the proposed model was compared with six different backbones and four semantic segmentation models. The results show that the proposed model outperforms the compared models under complex conditions, and can refine lesion segmentation and accurately estimate the disease severity.ConclusionsThe proposed method was an efficient tool for disease severity estimation in field conditions. This study can facilitate the implementation of artificial intelligence for rapid disease severity estimation and control in agriculture.