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T Palosuo, KC Kersebaum, C Angulo, P Hlavinka, M Moriondo, JE Olesen (2011)
Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth modelsEuropean Journal of Agronomy, 35
JA Vrugt, CJFT Braak, CGH Diks, BA Robinson, JM Hyman, D Higdon (2009)
Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace samplingInternational Journal of Nonlinear Sciences and Numerical Simulation, 10
GP Robertson, EA Paul, RR Harwood (2000)
Greenhouse gases in intensive agriculture: Contributions of individual gases to the radiative forcing of the atmosphereScience, 289
GP Robertson, PM Vitousek (2009)
Nitrogen in agriculture: balancing the cost of an essential resourceAnnual Review of Environment and Resources, 34
S Delin, B Lindén, K Berglund (2005)
Yield and protein response to fertilizer nitrogen in different parts of a cereal field: potential of site-specific fertilizationEuropean Journal of Agronomy, 22
M Semenov, J Porter (1995)
Climatic variability and the modelling of crop yieldsAgricultural and Forest Meteorology, 73
F Ewert, MK Ittersum, T Heckelei, O Therond, I Bezlepkina, E Andersen (2011)
Scale changes and model linking methods for integrated assessment of agri-environmental systemsAgriculture, Ecosystems & Environment, 142
B Basso, L Sartori, D Cammarano, C Fiorentino, PR Grace, S Fountas (2012)
Environmental and economic evaluation of N fertilizer rates in a maize crop in Italy: A spatial and temporal analysis using crop modelsBiosystems Engineering, 113
N Beaudoin, M Launay, E Sauboua, G Ponsardin, B Mary (2008)
Evaluation of the soil crop model STICS over 8 years against the ‘on farm’ database of Bruyères catchmentEuropean Journal of Agronomy, 29
N Brisson, C Gary, E Justes, R Roche, B Mary, D Ripoche (2003)
An overview of the crop model sticsEuropean Journal of Agronomy, 18
J Constantin, N Beaudoin, F Laurent, J-P Cohan, F Duyme, B Mary (2011)
Cumulative effects of catch crops on nitrogen uptake, leaching and net mineralizationPlant and Soil, 341
V Houlès, B Mary, M Guérif, D Makowski, E Justes (2004)
Evaluation of the ability of the crop model STICS to recommend nitrogen fertilisation rates according to agro-environmental criteriaAgronomie, 24
JR Porter, MA Semenov (2005)
Crop responses to climatic variationPhilosophical Transactions of the Royal Society B: Biological Sciences, 360
SJ Riha, DS Wilks, P Simoens (1996)
Impact of temperature and precipitation variability on crop model predictionsClimatic Change, 32
AR Mosier, M Bleken, P Chaiwanakupt, EC Ellis, J Freney, RB Howarth (2001)
Policy implications of human-accelerated nitrogen cyclingBiogeochemistry, 52
S Nonhebel (1994)
The effects of use of average instead of daily weather data in crop growth simulation modelsAgricultural Systems, 44
K Loague, RE Green (1991)
Statistical and graphical methods for evaluating solute transport models: Overview and applicationJournal of Contaminant Hydrology, 7
S Guillaume, JE Bergez, D Wallach, E Justes (2011)
Methodological comparison of calibration procedures for durum wheat parameters in the STICS modelEuropean Journal of Agronomy, 35
J Link, W Batchelor, S Graeff, W Claupein (2008)
Evaluation of current and model-based site-specific nitrogen applications on wheat (Triticum aestivum L.) yield and environmental qualityPrecision Agriculture, 9
C Lawless, MA Semenov (2005)
Assessing lead-time for predicting wheat growth using a crop simulation modelAgricultural and Forest Meteorology, 135
N Brisson, B Mary, D Ripoche, MH Jeuffroy, F Ruget, B Nicoullaud (1998)
STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and cornAgronomie, 18
B Basso, C Fiorentino, D Cammarano, G Cafiero, J Dardanelli (2012)
Analysis of rainfall distribution on spatial and temporal patterns of wheat yield in Mediterranean environmentEuropean Journal of Agronomy, 41
B Basso, JT Ritchie, D Cammarano, L Sartori (2011)
A strategic and tactical management approach to select optimal N fertilizer rates for wheat in a spatially variable fieldEuropean Journal of Agronomy, 35
B Dumont, V Leemans, M Mansouri, B Bodson, J Destain, M Destain (2014)
Parameter optimisation of the STICS crop model, with an accelerated formal MCMC approach (DREAM algorithm)Environmental Modelling and Software, 52
MA Semenov, FJ Doblas-Reyes (2007)
Utility of dynamical seasonal forecasts in predicting crop yieldClimate Research, 34
B Basso, M Bertocco, L Sartori, EC Martin (2007)
Analyzing the effects of climate variability on spatial pattern of yield in a maize–wheat–soybean rotationEuropean Journal of Agronomy, 26
B Basso, JT Ritchie (2005)
Impact of compost, manure and inorganic fertilizer on nitrate leaching and yield for a 6-year maize–alfalfa rotation in MichiganAgriculture, Ecosystems & Environment, 108
MA Semenov, EM Barrow (1997)
Use of a stochastic weather generator in the development of climate change scenariosClimatic Change, 35
JC Zadoks, TT Chang, CF Konzak (1974)
A decimal code for the growth stages of cerealsWeed Research, 14
B Basso, L Sartori, M Bertocco, D Cammarano, EC Martin, PR Grace (2011)
Economic and environmental evaluation of site-specific tillage in a maize crop in NE ItalyEuropean Journal of Agronomy, 35
S Arslan, T Colvin (2002)
Grain Yield Mapping: Yield Sensing, Yield Reconstruction, and ErrorsPrecision Agriculture, 3
J Binder, S Graeff, J Link, W Claupein, M Liu, M Dai (2008)
Model-Based Approach to Quantify Production Potentials of Summer Maize and Spring Maize in the North China PlainAgrononomy Journal, 100
DA Hennessy (2009)
Crop Yield Skewness and the Normal DistributionJournal of Agricultural and Resource Economics, 34
B Eickhout, AF Bouwman, H Zeijts (2006)
The role of nitrogen in world food production and environmental sustainabilityAgriculture, Ecosystems & Environment, 116
PD Jamieson, MA Semenov, IR Brooking, GS Francis (1998)
Sirius: a mechanistic model of wheat response to environmental variationEuropean Journal of Agronomy, 8
DA Hennessy (2009)
Crop Yield Skewness Under Law of the Minimum TechnologyAmerican Journal of Agricultural Economics, 91
P Koundouri, N Kourogenis (2011)
On the Distribution of Crop Yields: Does the Central Limit Theorem Apply?American Journal of Agricultural Economics, 93
B Dumont, B Basso, V Leemans, B Bodson, JP Destain, MF Destain (2013)
Precision agriculture’13 Proceedings of the 9th European Confernce on Precision Agriculture
N Brisson, F Ruget, P Gate, J Lorgeau, B Nicoulaud, X Tayo (2002)
STICS: a generic model for simulating crops and their water and nitrogen balances. II. Model validation for wheat and maizeAgronomie, 22
RH Day (1965)
Probability Distributions of Field Crop YieldsJournal of Farm Economics, 47
A McBratney, B Whelan, T Ancev, J Bouma (2005)
Future Directions of Precision AgriculturePrecision Agriculture, 6
RE Just, Q Weninger (1999)
Are Crop Yields Normally Distributed?American Journal of Agricultural Economics, 81
X Du, D Hennessy, C Yu (2012)
Testing Day’s Conjecture that More Nitrogen Decreases Crop Yield SkewnessAmerican Journal of Agricultural Economics, 94
P Racsko, L Szeidl, M Semenov (1991)
A serial approach to local stochastic weather modelsEcological Modelling, 57
J Constantin, N Beaudoin, M Launay, J Duval, B Mary (2012)
Long-term nitrogen dynamics in various catch crop scenarios: Test and simulations with STICS model in a temperate climateAgriculture, Ecosystems & Environment, 147
JR Porter, MA Semenov (1999)
Climate variability and crop yields in EuropeNature, 400
JP Welsh, GA Wood, RJ Godwin, JC Taylor, R Earl, S Blackmore (2003)
Developing Strategies for Spatially Variable Nitrogen Application in CerealsPart I: Winter Barley. Biosystems Engineering, 84
V Smil (1999)
Nitrogen in crop production: An account of global flowsGlobal Biogeochemical Cycles, 13
S Recous, J-M Machet (1999)
Short-term immobilisation and crop uptake of fertiliser nitrogen applied to winter wheat: effect of date of application in springPlant and Soil, 206
At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque–Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha−1), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of −1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha−1) of the distribution.
Precision Agriculture – Springer Journals
Published: Oct 1, 2014
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