Integrating Crop Growth Models with Remote Sensing for Predicting Biomass Yield of Sorghum

Abstract Plant phenotypes are often descriptive, rather than predictive of crop performance. As a result, extensive testing is required in plant breeding programs to develop varieties aimed at performance in the target environments. Crop models can improve this testing regime by providing a predictive framework to (1) augment field phenotyping data and derive hard-to-measure phenotypes and (2) estimate performance across geographical regions using historical weather data. The goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote sensing and ground reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids. Genotype parameters for each hybrid were estimated using remote sensing measurements combined with manual phenotyping in West Lafayette, Indiana in 2018. The models were validated in hybrid performance trials in two additional seasons at that site and against yield trials conducted in Bushland, Texas between 2001 and 2018. These trials demonstrated that (1) maximum plant height, final dry biomass, and radiation use efficiency (RUE) of photoperiod sensitive and insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum, (2) photoperiod sensitive sorghum hybrids exhibited greater biomass production in longer growing environments, and (3) the parameterized and validated models perform well in above ground biomass simulations across years and locations. Crop growth models that integrate remote sensing data offer an efficient approach to parameterise larger plant breeding populations.

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Crop modelling and remote sensing for yield prediction

Netherlands Journal of Agricultural Science ◽

10.18174/njas.v43i2.573 ◽

1995 ◽

Vol 43 (2) ◽

pp. 143-161 ◽

Cited By ~ 7

Author(s):

B.A.M. Bouman

Keyword(s):

Remote Sensing ◽

Growth Models ◽

Remote Sensing Data ◽

Ground Cover ◽

Crop Growth ◽

Model Parameters ◽

Optical Remote Sensing ◽

Sensing Data ◽

Forcing Function ◽

Crop Growth Models

Methods for the application of crop growth models, remote sensing and their integrative use for yield forecasting and prediction are presented. First, the general principles of crop growth models are explained. When crop simulation models are used on regional scales, uncertainty and spatial variation in model parameters can result in broad bands of simulated yield. Remote sensing can be used to reduce some of this uncertainty. With optical remote sensing, standard relations between the Weighted Difference Vegetation Index and fraction ground cover and LAI were established for a number of crops. The radar backscatter of agricultural crops was found to be largely affected by canopy structure, and, for most crops, no consistent relationships with crop growth indicators were established. Two approaches are described to integrate remote sensing data with crop growth models. In the first one, measures of light interception (ground cover, LAI) estimated from optical remote sensing are used as forcing function in the models. In the second method, crop growth models are extended with remote sensing sub-models to simulate time-series of optical and radar remote sensing signals. These simulated signals are compared to measured signals, and the crop growth model is re-calibrated to match simulated with measured remote sensing data. The developed methods resulted in increased accuracy in the simulation of crop growth and yield of wheat and sugar beet in a number of case-studies.

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Assimilation of remote sensing into crop growth models: Current status and perspectives

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2019.06.008 ◽

2019 ◽

Vol 276-277 ◽

pp. 107609 ◽

Cited By ~ 33

Author(s):

Jianxi Huang ◽

Jose L. Gómez-Dans ◽

Hai Huang ◽

Hongyuan Ma ◽

Qingling Wu ◽

...

Keyword(s):

Remote Sensing ◽

Growth Models ◽

Crop Growth ◽

Current Status ◽

Crop Growth Models

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Remote sensing-based crop biomass with water or light-driven crop growth models in wheat commercial fields

Field Crops Research ◽

10.1016/j.fcr.2017.11.025 ◽

2018 ◽

Vol 216 ◽

pp. 175-188 ◽

Cited By ~ 21

Author(s):

Isidro Campos ◽

Laura González-Gómez ◽

Julio Villodre ◽

Jose González-Piqueras ◽

Andrew E. Suyker ◽

...

Keyword(s):

Remote Sensing ◽

Growth Models ◽

Crop Growth ◽

Crop Growth Models ◽

Crop Biomass

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Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models

Remote Sensing ◽

10.3390/rs11161928 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1928

Author(s):

Nathaniel Levitan ◽

Yanghui Kang ◽

Mutlu Özdoğan ◽

Vincenzo Magliulo ◽

Paulo Castillo ◽

...

Keyword(s):

Remote Sensing ◽

Random Error ◽

Growth Stage ◽

Growth Models ◽

Crop Growth ◽

Regression Coefficients ◽

Satellite Measurement ◽

Area Index ◽

Specific Factors ◽

Crop Growth Models

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUECanopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUECanopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUECanopy. The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.

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An Agent-Based Crop Model Framework for Heterogeneous Soils

Agronomy ◽

10.3390/agronomy11010085 ◽

2021 ◽

Vol 11 (1) ◽

pp. 85

Author(s):

Jorge Lopez-Jimenez ◽

Nicanor Quijano ◽

Alain Vande Wouwer

Keyword(s):

Growth Models ◽

Control Strategies ◽

Low Cost ◽

Soil Heterogeneity ◽

Crop Growth ◽

Negative Effects ◽

Model Framework ◽

Agent Based ◽

Crop Growth Models ◽

Definition Of

Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these models is that they do not consider the variability of the soil throughout the cultivation area. In addition, with the availability of new information sources (i.e., aerial or satellite images) and low-cost meteorological stations, it is convenient that the models incorporate prediction capabilities to enhance the representation of production scenarios. In this work, an agent-based model (ABM) that considers the soil heterogeneity and water exchanges is proposed. Soil heterogeneity is associated to the combination of individual behaviours of uniform portions of land (agents), while water fluxes are related to the topography. Each agent is characterized by an individual dynamic model, which describes the local crop growth. Moreover, this model considers positive and negative effects of water level, i.e., drought and waterlogging, on the biomass production. The development of the global ABM is oriented to the future use of control strategies and optimal irrigation policies. The model is built bottom-up starting with the definition of agents, and the Python environment Mesa is chosen for the implementation. The validation is carried out using three topographic scenarios in Colombia. Results of potential production cases are discussed, and some practical recommendations on the implementation are presented.

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Water uptake in crop growth models for land use systems analysis

Agriculture Ecosystems & Environment ◽

10.1016/s0167-8809(01)00285-7 ◽

2002 ◽

Vol 92 (1) ◽

pp. 21-36 ◽

Cited By ~ 19

Author(s):

M. van den Berg ◽

P.M. Driessen

Keyword(s):

Land Use ◽

Water Uptake ◽

Systems Analysis ◽

Growth Models ◽

Crop Growth ◽

Land Use Systems ◽

Crop Growth Models

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An efficient and accurate method for obtaining regional scale rice growth conditions based on WOFOST model and satellite images

10.5194/egusphere-egu21-10688 ◽

2021 ◽

Author(s):

Bingyu Zhao ◽

Meiling Liu ◽

Jiianjun Wu ◽

Xiangnan Liu ◽

Mengxue Liu ◽

...

Keyword(s):

Remote Sensing ◽

Data Assimilation ◽

Growth Model ◽

Growth Parameters ◽

Remote Sensing Data ◽

Growth Conditions ◽

Crop Growth ◽

Crop Growth Model ◽

Parallel Method ◽

Rice Growth

<p>It is very important to obtain regional crop growth conditions efficiently and accurately in the agricultural field. The data assimilation between crop growth model and remote sensing data is a widely used method for obtaining vegetation growth information. This study aims to present a parallel method based on graphic processing unit (GPU) to improve the efficiency of the assimilation between RS data and crop growth model to estimate rice growth parameters. Remote sensing data, Landsat and HJ-1 images were collected and the World Food Studies (WOFOST) crop growth model which has a strong flexibility was employed. To acquire continuous regional crop parameters in temporal-spatial scale, particle swarm optimization (PSO) data assimilation method was used to combine remote sensing images and WOFOST and this process is accompanied by a parallel method based on the Compute Unified Device Architecture (CUDA) platform of NVIDIA GPU. With these methods, we obtained daily rice growth parameters of Zhuzhou City, Hunan, China and compared the efficiency and precision of parallel method and non-parallel method. Results showed that the parallel program has a remarkable speedup (reaching 240 times) compared with the non-parallel program with a similar accuracy. This study indicated that the parallel implementation based on GPU was successful in improving the efficiency of the assimilation between RS data and the WOFOST model and was conducive to obtaining regional crop growth conditions efficiently and accurately.</p>

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