A review of methods to evaluate crop model performance at multiple and changing spatial scales
Daniel Pasquel,Sébastien Roux,Jonathan Richetti,Davide Cammarano,Bruno Tisseyre,James Taylor +5 more
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In this paper , the authors review the reasons why practitioners decide to spatialize crop models and the main methods they have used to do this, which questions the best place of the spatialization process in the modelling framework.Abstract:
Abstract Crop models are useful tools because they can help understand many complex processes by simulating them. They are mainly designed at a specific spatial scale, the field. But with the new spatial data being made available in modern agriculture, they are being more and more applied at multiple and changing scales. These applications range from typically at broader scales, to perform regional or national studies, or at finer scales to develop modern site-specific management approaches. These new approaches to the application of crop models raise new questions concerning the evaluation of their performance, particularly for downscaled applications. This article first reviews the reasons why practitioners decide to spatialize crop models and the main methods they have used to do this, which questions the best place of the spatialization process in the modelling framework. A strong focus is then given to the evaluation of these spatialized crop models. Evaluation metrics, including the consideration of dedicated sensitivity indices are reviewed from the published studies. Using a simple example of a spatialized crop model being used to define management zones in precision viticulture, it is shown that classical model evaluation involving aspatial indices (e.g. the RMSE) is not sufficient to characterize the model performance in this context. A focus is made at the end of the review on potentialities that a complementary evaluation could bring in a precision agriculture context. read more
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Role of existing and emerging technologies in advancing climate-smart agriculture through modeling: A review
TL;DR: In this article , the authors reviewed, critically assessed, and discussed the present state-of-the-art modeling technologies related to the CSA, and highlighted the current research trends in the different crop simulation models and their CSA applications.
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Coupling Process-Based Models and Machine Learning Algorithms for Predicting Yield and Evapotranspiration of Maize in Arid Environments
Ahmed Attia,Ajit Govind,Asad Sarwar Qureshi,Til Feike,M. S. Rizk,Mahmoud Shabana,Ahmed M. S. Kheir +6 more
TL;DR: In this paper , the combination of CMs and ML algorithms is a powerful tool for predicting yield and water use in arid regions, which are particularly vulnerable to climate change and water scarcity, and the most important features for predicting GY and ET are maximum temperatures, minimum temperature, available water content, soil organic carbon, irrigation, cultivars, soil texture, solar radiation, and planting date.
Journal ArticleDOI
Regional modeling of winter wheat yield and water productivity under water-saving irrigation scenarios
TL;DR: In this article , a parameterized AquaCrop model was employed to simulate the four major elements that affect the crop growth process, such as soil water content, canopy cover, biomass, and grain yield of three irrigated winter wheat cultivars.
Journal ArticleDOI
The urgency for investment on local data for advancing food assessments in Africa: A review case study for APSIM crop modeling
Ana J. P. Carcedo,Nilson Dias Vieira Junior,Lucia Marziotte,Adrian A. Correndo,Alemo Araya,P. V. V. Prasad,Doohong Min,Zachary P. Stewart,A. Faye,Ignacio A. Ciampitti +9 more
TL;DR: In this paper , the authors provide a synthesis analysis of crop modeling efforts in Africa using Agricultural Production Systems Simulator (APSIM) studies as a case-study, highlighting the value of standardized protocols to collect, store and deploy field data, and highlighting the critical issue of limited data accessibility of published manuscripts and unavailability of a data sharing platform.
Proceedings ArticleDOI
Unsupervised Graph Spectral Feature Denoising for Crop Yield Prediction
TL;DR: In this paper , a graph spectral filter is used to denoise relevant features via graph spectral filtering that are inputs to a deep learning prediction model, and then denoise features via a maximum a posteriori (MAP) formulation with a graph Laplacian regularizer (GLR).
References
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Journal ArticleDOI
A Coefficient of agreement for nominal Scales
TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.
Journal ArticleDOI
Data fusion
Jens Bleiholder,Felix Naumann +1 more
TL;DR: This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data Fusion.
Journal ArticleDOI
AquaCrop-The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles
TL;DR: The FAO crop model AquaCrop as mentioned in this paper is a water-driven growth engine, in which transpiration is calculated first and translated into biomass using a conservative, crop-specific parameter: the biomass water productivity, normalized for atmospheric evaporative demand and air CO 2 concentration.
Journal ArticleDOI
CropSyst, a cropping systems simulation model
TL;DR: CropSyst as discussed by the authors is a multi-year, multi-crop, daily time step simulation model developed to serve as an analytical tool to study the effect of climate, soils, and management on cropping systems productivity and the environment.