Assessing the potential of an algorithm based on mean climatic data to predict wheat yield

The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3–4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days.

[1]  T. Palmer,et al.  Development of a European Multi-Model Ensemble System for Seasonal to Inter-Annual Prediction (DEMETER) , 2004 .

[2]  D. Higdon,et al.  Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling , 2009 .

[3]  M. Rivington,et al.  Validation of Biophysical Models: Issues and Methodologies , 2011 .

[4]  Anil Kumar Singh,et al.  Evaluation of CERES-Wheat and CropSyst models for water-nitrogen interactions in wheat crop , 2008 .

[5]  F.J Doblas-Reyes,et al.  Probabilistic prediction of climate using multi-model ensembles: from basics to applications , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[6]  Chris Hewitt,et al.  Ensembles-based predictions of climate changes and their impacts , 2004 .

[7]  S. Nonhebel.  Use of weather data in crop growth simulation models. , 1992 .

[8]  Mikhail A. Semenov,et al.  A serial approach to local stochastic weather models , 1991 .

[9]  Francisco J. Doblas-Reyes,et al.  Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles , 2005 .

[10]  J. V. Stafford,et al.  Precision agriculture ’13 , 2013, Wageningen Academic Publishers.

[11]  J. M. Norman,et al.  Plant Canopies: Their Growth, Form and Function: The description and measurement of plant canopy structure , 1989 .

[12]  Majdi Mansouri,et al.  Modeling and prediction of nonlinear environmental system using Bayesian methods , 2013 .

[13]  M. Trnka,et al.  Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models , 2011 .

[14]  Munir S. Alam,et al.  Factors Influencing the , 2016 .

[15]  Mikhail A. Semenov,et al.  Assessing lead-time for predicting wheat growth using a crop simulation model , 2005 .

[16]  M. Semenov,et al.  USE OF A STOCHASTIC WEATHER GENERATOR IN THE DEVELOPMENT OF CLIMATE CHANGE SCENARIOS , 1997 .

[17]  John R. Williams,et al.  The EPIC crop growth model , 1989 .

[18]  Martin K. van Ittersum,et al.  Scale changes and model linking methods for integrated assessment of agri-environmental systems , 2011 .

[19]  Bruno Basso,et al.  Analyzing the effects of climate variability on spatial pattern of yield in a maize-wheat-soybean rotation , 2007 .

[20]  Bruno Basso,et al.  Impact of compost, manure and inorganic fertilizer on nitrate leaching and yield for a 6-year maize-alfalfa rotation in Michigan , 2005 .

[21]  I. R. Cowan.  IN THE SOIL-PLANT-ATMOSPHERE SYSTEM , 2016 .

[22]  Bruno Mary,et al.  Conceptual basis, formalisations and parameterization of the STICS crop model , 2009 .

[23]  J. Wolf,et al.  WOFOST: a simulation model of crop production. , 1989 .

[24]  Mike Rivington,et al.  Validation of biophysical models: issues and methodologies. A review , 2011, Agronomy for Sustainable Development.

[25]  A. Titi,et al.  Integrated Farming: an Ecological Farming Approach in European Agriculture: , 1992 .

[26]  H. Sinoquet,et al.  An overview of the crop model STICS , 2003 .

[27]  D. Makowski,et al.  Is it useful to combine measurements taken during the growing season with a dynamic model to predict the nitrogen status of winter wheat , 2008 .

[28]  Graham Russell,et al.  Plant Canopies: Their Growth, Form and Function: Contents , 1989 .

[29]  Bernard Bodson,et al.  Parameter identification of the STICS crop model, using an accelerated formal MCMC approach , 2014, Environ. Model. Softw..

[30]  D. Cammarano,et al.  Analysis of rainfall distribution on spatial and temporal patterns of wheat yield in Mediterranean environment , 2012 .

[31]  Sanderine Nonhebel.  Inaccuracies in weather data and their effects on crop growth simulation results. I. Potential production , 1994 .

[32]  S. Recous,et al.  STICS : a generic model for the simulation of crops and their water and nitrogen balances. I. Theory, and parameterization applied to wheat and corn , 1998 .

[33]  M. Semenov.  Simulation of extreme weather events by a stochastic weather generator , 2008 .

[34]  P. Jamieson,et al.  Sirius: a mechanistic model of wheat response to environmental variation , 1998 .

[35]  Andrew P. Morse,et al.  DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER) , 2004 .

[36]  Mikhail A. Semenov,et al.  Utility of dynamical seasonal forecasts in predicting crop yield , 2007 .

[37]  J. Porter,et al.  Climatic variability and the modelling of crop yields , 1995 .

[38]  P. Cantelaube,et al.  Seasonal weather forecasts for crop yield modelling in Europe , 2005 .

[39]  J. Ritchie,et al.  Long-term wheat response to nitrogen in a rainfed Mediterranean environment: field data and simulation analysis. , 2010 .

[40]  J. R. Ritchie,et al.  Description and performance of CERES-Wheat: a user-oriented wheat yield model , 1985 .

[41]  J. Bouma,et al.  Future Directions of Precision Agriculture , 2005, Precision Agriculture.

[42]  J. Porter,et al.  Crop responses to climatic variation , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[43]  J. Ritchie,et al.  A strategic and tactical management approach to select optimal N fertilizer rates for wheat in a spatially variable field , 2011 .

[44]  J. Hansen,et al.  Interannual variability characteristics and simulated crop response of four stochastic weather generators , 2001 .

[45]  T. Mavromatis,et al.  Comparison of climate change scenario construction methodologies for impact assessment studies , 1998 .

[46]  Yeong Sheng Tey,et al.  Factors influencing the adoption of precision agricultural technologies: a review for policy implications , 2012, Precision Agriculture.

[47]  Susan J. Riha,et al.  Impact of temperature and precipitation variability on crop model predictions , 1996 .

[48]  Mikhail A. Semenov,et al.  Climate variability and crop yields in Europe , 1999, Nature.

[49]  P. Thornton,et al.  Using Crop Models for Sustainability and Environmental Quality Assessment , 1992 .