000			03578nab a22003977a 4500
999			_c61326 _d61318
001			61326
003			MX-TxCIM
005			20240919021227.0
008			200212s2019 xxu|||p|op||| 00| 0 eng d
022			_a1932-6203
024	8		_ahttps://doi.org/10.1371/journal.pone.0200118
040			_aMX-TxCIM
041			_aeng
100	1		_96759 _aAdnan, A. A.
245	1	0	_aOptions for calibrating CERES-maize genotype specific parameters under data-scarce environments
260			_aSan Francisco, CA (USA) : _bPublic Library of Science, _c2019.
500			_aPeer review
500			_aOpen Access
520			_aMost crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect of most modelling exercises because it requires expensive and time-consuming field experiments. GSPs could also be estimated using multi-year and multi locational data from breeder evaluation experiments. This research was set up with the following objectives: i) to determine GSPs of 10 newly released maize varieties for the Nigerian Savannas using data from both calibration experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. Breeder evaluation data were also collected for 2 years and 7 locations. The calibrated GSPs were evaluated using data from a 4-year experiment conducted under varying nitrogen rates (0, 60 and 120kg N ha-1). For the model calibration using experimental data, calculated model efficiency (EF) values ranged between 0.88–0.94 and coefficient of determination (d-index) between 0.93–0.98. Calibration of time-series data produced nRMSE below 7% while all prediction deviations were below 10% of the mean. For breeder experiments, EF (0.58–0.88) and d-index (0.56–0.86) ranges were lower. Prediction deviations were below 17% of the means for all measured variables. Model evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen contents. It is concluded that higher calibration accuracy of CERES-Maize model is achieved from detailed experiments. If unavailable, data from breeder experimental trials collected from many locations and planting dates can be used with lower but acceptable accuracy.
526			_aMCRP _bFP1
546			_aText in English
650		7	_aMaize _gAGROVOC _2 _91173
650		7	_aPlant breeding _gAGROVOC _2 _91203
650		7	_2AGROVOC _92623 _aCrop modelling
650		7	_2AGROVOC _99002 _aData
700	1		_911308 _aDiels, J.
700	1		_96760 _aJibrin, J. M.
700	1		_96761 _aKamara, A. Y.
700	1		_aCraufurd, P. _gSustainable Intensification Program _gSustainable Agrifood Systems _8I1705950 _9792
700	1		_96763 _aShaibu, A. S.
700	1		_910854 _aMohammed, I.B.
700	1		_92734 _aTonnang, H. _8I1706688 _gSustainable Intensification Program
773	0		_dSan Francisco, CA (USA) : Public Library of Science, 2019. _gv. 14, no. 2, art. : e0200118 _tPLoS One _x1932-6203 _wu94957
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/20699
942			_2ddc _cJA _n0