scholarly journals Operational semi-physical spectral-spatial wheat yield model development

2014 ◽  
Vol XL-8 ◽  
pp. 977-982 ◽  
Author(s):  
R. Tripathy ◽  
K. N. Chaudhary ◽  
R. Nigam ◽  
K. R. Manjunath ◽  
P. Chauhan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Wheat Yield ◽  
State Level ◽  
Radiation Use Efficiency ◽  
Wheat Crop ◽  
Yield Prediction ◽  
Use Efficiency ◽  
Yield Forecasting ◽  
Radiation Use

Spectral yield models based on Vegetation Index (VI) and the mechanistic crop simulation models are being widely used for crop yield prediction. However, past experience has shown that the empirical nature of the VI based models and the intensive data requirement of the complex mechanistic models has limited their use for regional and spatial crop yield prediction especially for operational use. The present study was aimed at development of an intermediate method based on the use of remote sensing and the physiological concepts such as the photo-synthetically active solar radiation (PAR) and the fraction of PAR absorbed by the crop (fAPAR) in Monteith’s radiation use efficiency based equation (Monteith, 1977) for operational wheat yield forecasting by the Department of Agriculture (DoA). Net Primary Product (NPP) has been computed using the Monteith model and stress has been applied to convert the potential NPP to actual NPP. Wheat grain yield has been computed using the actual NPP and Harvest index. Kalpana-VHRR insolation has been used for deriving the PAR. Maximum radiation use efficiency has been collected from literature and wheat crop mask was derived at MNCFC, New Delhi using RS2-AWiFS data. Water stress has been derived from the Land Surface Water Index (LSWI) which has been derived periodically from the MODIS surface reflectance data (NIR and SWIR1). Temperature stress has been derived from the interpolated daily mean temperature. Results indicated that this model underestimated the yield by 3.45 % as compared to the reported yield at state level and hence can be used to predict wheat yield at state level. This study will be able to provide the spatial wheat yield map, as well as the district-wise and state level aggregated wheat yield forecast. It is possible to operationalize this remote sensing based modified Monteith’s efficiency model for future yield forecasting with around 0.15 t ha-1 RMSE at state level.

scholarly journals Evaluating radiation interception pattern and RUE of green gram grown in Lower Gangetic Plains and assessing future yield based on RUE

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
SARATHI SAHA ◽  
SAON BANERJEE ◽  
SOUMEN MONDAL ◽  
ASIS MUKHERJEE ◽  
RAJIB NATH ◽  
...  
Keyword(s):  
West Bengal ◽  
Biomass Conversion ◽  
Average Rate ◽  
Green Gram ◽  
Radiation Use Efficiency ◽  
Yield Prediction ◽  
Gangetic Plains ◽  
Use Efficiency ◽  
Change Impact ◽  
Radiation Use

An experiment was conducted in the Lower Gangetic Plains of West Bengal during 2017 and 2018 with three popular green gram varieties of the region (viz. Samrat, PM05 and Meha). Along with studying the variation of PAR components, a radiation use efficiency (RUE) based equation irrespective of varieties was developed and used to estimate the green gram yield for 2040-2090 period under RCP 4.5 and 8.5 scenarios. Field experimental results showed that almost 33.33 to 52.12% higher yield was recorded in 2017 in comparison to 2018. As observed through pooled experimental data of two years, PM05 produced 3 to 4% higher pod and 4 to 15% more biomass than Samrat and Meha with the highest radiation use efficiency (1.786 g MJ-1). Results also depicted that enhanced thermal condition would cause 9 to 15 days of advancement in maturity. Biomass and yield would also decrease gradually from 2040 to 2090 with an average rate of 7.60-11.70% and 10.19-14.17% respectively. The supporting literature confirms that future yield prediction under projected climate based on “radiation to biomass” conversion efficiency can be used successfully as a method to evaluate climate change impact on crop performance.

scholarly journals Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

2015 ◽  
Vol XL-7/W3 ◽  
pp. 39-44 ◽  
Author(s):  
A. Kolotii ◽  
N. Kussul ◽  
A. Shelestov ◽  
S. Skakun ◽  
B. Yailymov ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Biophysical Model ◽  
Wheat Crop ◽  
Biophysical Parameters ◽  
Model Based ◽  
Winter Wheat Crop ◽  
Yield Forecasting ◽  
Crop Yield Forecasting

Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.

scholarly journals APPLICATION OF CERES-WHEAT MODEL TO SIMULATE GROWTH, DEVELOPMENT AND RADIATION USE EFFICIENCY OF WHEAT CROP

2015 ◽  
Vol 3 (2-3) ◽  
pp. 50-57
Author(s):  
Shabir Hussain ◽  
S.A. Wajid ◽  
Hakoomat Ali ◽  
Abdul Sattar ◽  
Naeem Sarwar ◽  
...  
Keyword(s):  
Radiation Use Efficiency ◽  
Wheat Crop ◽  
Use Efficiency ◽  
Growth Development ◽  
Radiation Use

scholarly journals Field based remote sensing models predict radiation use efficiency in wheat

2021 ◽  
Author(s):  
Carlos A Robles-Zazueta ◽  
Gemma Molero ◽  
Francisco Pinto ◽  
M John Foulkes ◽  
Matthew P Reynolds ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Radiation Use Efficiency ◽  
Primary Metabolism ◽  
Ground Truth Data ◽  
Use Efficiency ◽  
Radiation Use

Abstract Wheat yields are stagnating or declining in many regions, requiring efforts to improve the light conversion efficiency, i.e. radiation use efficiency (RUE). RUE is a key trait in plant physiology because it links light capture and primary metabolism with biomass accumulation and yield, but its measurement is time consuming and this has limited its use in fundamental research and large scale physiological breeding. In this study, high-throughput phenotyping (HTPP) approaches were used among a population of field grown wheat with variation in RUE and photosynthetic traits to build predictive models of RUE, biomass and intercepted photosynthetically active radiation (IPAR). Three approaches were used: best combination of sensors, canopy vegetation indices and partial least square regression. The use of remote sensing models predicted RUE with up to 70% accuracy compared to ground truth data. Water indices and NDVI are the better option to predict RUE, biomass and IPAR, and indices related to NPQ (PRI) and senescence (SIPI) are better predictors for these traits at the vegetative and grain filling stages respectively. These models will be instrumental to explain canopy processes, improve crop growth, yield modelling, and potentially be used to predict RUE in different crops or ecosystems.

FACE-ing the global change: Opportunities for improvement in photosynthetic radiation use efficiency and crop yield

Plant Science ◽  
2009 ◽  
Vol 177 (6) ◽  
pp. 511-522 ◽  
Author(s):  
Jindong Sun ◽  
Lianxin Yang ◽  
Yulong Wang ◽  
Donald R. Ort
Keyword(s):  
Global Change ◽  
Crop Yield ◽  
Radiation Use Efficiency ◽  
Use Efficiency ◽  
Radiation Use

scholarly journals Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

2020 ◽  
Vol 12 (2) ◽  
pp. 236 ◽  
Author(s):  
Jichong Han ◽  
Zhao Zhang ◽  
Juan Cao ◽  
Yuchuan Luo ◽  
Liangliang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Prediction ◽  
Climate Data ◽  
Source Data ◽  
Winter Wheat Yield

Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

scholarly journals Determination of Appropriate Remote Sensing Indices for Spring Wheat Yield Estimation in Mongolia

2019 ◽  
Vol 11 (21) ◽  
pp. 2568 ◽  
Author(s):  
Battsetseg Tuvdendorj ◽  
Bingfang Wu ◽  
Hongwei Zeng ◽  
Gantsetseg Batdelger ◽  
Lkhagvadorj Nanzad
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Spring Wheat ◽  
Drought Index ◽  
Wheat Yield ◽  
Condition Index ◽  
Yield Prediction ◽  
Yield Estimation ◽  
Water Index ◽  
Remote Sensing Indices

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.

scholarly journals RADIATION USE EFFICIENCY, ABOVE GROUND BIOMASS ACCUMULATION, CANOPY DEVELOPMENT, LEAF AREA-LIGHT INTERCEPTION PROFILES AND RADIATION INTERCEPTION OF SUNFLOWER (Helianthus annuus L.) AS INFLUENCED BY IRRIGATION REGIMEN / INFLUENCIA DEL RÉGIMEN DE IRRIGACIÓN SOBRE LA EFICACIA DEL USO DE LA RADIACIÓN, LA ACUMULACIÓN DE BIOMASA, EL DESAROROLLO DEL CANOPEO, EL ÁREA DE LA HOJA Y LOS PERFILES DE INTERCEPCIÓN DE LUZ Y DE RADIACIÓN EN GIRASOL (Helianthus annuus L.) / EFFICACITÉ DE L’UTILISATION DES RADIATIONS SOLAIRES, ACCUMULATION DE LA BIOMASSE DANS LA PARTIE AÉRIENNE, DÉVELOPPEMENT DE L’OMBRELLE, PROFILS D’INTERCEPTION DE LA LUMIÈRE PAR LA FEUILLE ET INTERCEPTION DES RADIATIONS SOLAIRES CHEZ LE TOURNESOL (Helianthus annuus L.) SOUS L’INFLUENCE D’UN RÉGIME D’IRRIGATION

Helia ◽  
2001 ◽  
Vol 24 (35) ◽  
pp. 101-110 ◽  
Author(s):  
S. Sridhara ◽  
T.G. Prasad
Keyword(s):  
Helianthus Annuus ◽  
Biomass Accumulation ◽  
Light Interception ◽  
Radiation Use Efficiency ◽  
Leaf Nitrogen Content ◽  
Radiation Interception ◽  
Canopy Development ◽  
Helianthus Annuus L ◽  
Use Efficiency ◽  
Radiation Use

SUMMARYA field experiment was conducted at Gandhi Krishi Vignana Kendra, University of Agricultural Sciences, Bangalore to study the effect of irrigation regimens on the biomass accumulation, canopy development, light interception and radiation use efficiency of sunflower. The treatments includes irrigating the plants at 0.4, 0.6, 0.8 and 1.0 cumulative pan evaporation. The results indicated that the aboveground biomass, canopy development, radiation interception and radiation use efficiency were influenced favorably by the irrigation regimens. Irrespective of the irrigation regimen, the radiation use efficiency of sunflower increased from 15 DAS to 75 DAS and then tended to decline. The decrease in RUE after anthesis is coupled with decrease in leaf nitrogen content. In general the RUE of sunflower ranged from 0.49 g MJ-1 to 1.84 g MJ-1 at different growth stages. The light transmission within the canopy increased exponentially with plant height and the canopy extension coefficient is found to be 0.8.

scholarly journals Intercepted Photosynthetically Active Radiation (PAR) and Spatial and Temporal Distribution of Transmitted PAR under High-Density and Super High-Density Olive Orchards

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 351
Author(s):  
Adolfo Rosati ◽  
Damiano Marchionni ◽  
Dario Mantovani ◽  
Luigi Ponti ◽  
Franco Famiani
Keyword(s):  
Spatial Variability ◽  
Photosynthetically Active Radiation ◽  
Temporal Distribution ◽  
High Density ◽  
Radiation Use Efficiency ◽  
Spatial And Temporal Distribution ◽  
Active Radiation ◽  
Use Efficiency ◽  
And Performance ◽  
Radiation Use

We quantified the photosynthetically active radiation (PAR) interception in a high-density (HD) and a super high-density (SHD) or hedgerow olive system, by measuring the PAR transmitted under the canopy along transects at increasing distance from the tree rows. Transmitted PAR was measured every minute, then cumulated over the day and the season. The frequencies of the different PAR levels occurring during the day were calculated. SHD intercepted significantly but slightly less overall PAR than HD (0.57 ± 0.002 vs. 0.62 ± 0.03 of the PAR incident above the canopy) but had a much greater spatial variability of transmitted PAR (0.21 under the tree row, up to 0.59 in the alley center), compared to HD (range: 0.34–0.43). This corresponded to greater variability in the frequencies of daily PAR values, with the more shaded positions receiving greater frequencies of low PAR values. The much lower PAR level under the tree row in SHD, compared to any position in HD, implies greater self-shading in lower-canopy layers, despite similar overall interception. Therefore, knowing overall PAR interception does not allow an understanding of differences in PAR distribution on the ground and within the canopy and their possible effects on canopy radiation use efficiency (RUE) and performance, between different architectural systems.

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