000			02874nab|a22003737a|4500
001			66002
003			MX-TxCIM
005			20251210153752.0
008			20231s2023||||mx |||p|op||||00||0|eng|d
022			_a0002-1962
022			_a1435-0645 (Online)
024	8		_ahttps://doi.org/10.1016/j.compag.2022.106965
040			_aMX-TxCIM
041			_aeng
100	1		_aKinoshita, R. _92600
245	1	0	_aSoil sensing and machine learning reveal factors affecting maize yield in the mid-Atlantic United States
260			_bAmerican Society of Agronomy : _bWiley, _c2022. _aMadison, WI (USA) :
500			_aPeer review
500			_aEarly View
520			_aIn large-scale arable cropping systems, understanding within-field yield variations and yield-limiting factors are crucial for optimizing resource investments and financial returns, while avoiding adverse environmental effects. Sensing technologies can collect various crop and soil information, but there is a need to assess whether they reveal within-field yield constraints. Spatial data regarding grain yields, proximal soil sensing data, and topographical and soil properties were collected from 26 maize (Zea mays L.) growing fields in the U.S. Mid-Atlantic. Apparent soil electrical conductivity (ECa) collected by an on-the-go sensor (Veris) was an effective method for estimating subsoil textural variation and water holding capacity in the Coastal Plain region, which was also the best predictor of spatial yield pattern when combined with surface pH and topographic wetness index in a Random Forest (RF) model. In the Piedmont Plateau region, proximal soil sensors showed a lower correlation to measured soil properties, while topographical properties (aspect and slope) were important estimators of spatial yield patterns in an RF model. In locations where the RF model failed to predict yield variation, soil compaction appeared to be limiting crop yields. In conclusion, the application of RF models using ECa sensors and topographical properties was effective in revealing within-field yield constraints, especially in the Coastal Plain region. On the Piedmont Plateau, the calibration of proximal sensor information needs to be improved with a particular focus on soil compaction.
546			_aText in English
650		7	_aSoil _2AGROVOC _94828
650		7	_aMachine learning _2AGROVOC _911127
650		7	_aMaize _2AGROVOC _91173
650		7	_aYields _2AGROVOC _91313
650		7	_aSensors _2AGROVOC _92530
651		7	_2AGROVOC _929087 _aUnited States of America
700	1		_aTani, M. _929876
700	1		_aSherpa, S.R. _8001712516 _gSustainable Agrifood Systems _928790
700	1		_aGhahramani, A. _929877
700	1		_avan Es, H.M. _92607
773	0		_tAgronomy Journal _gIn press _dMadison, WI (USA) : American Society of Agronomy : Wiley, 2022. _x0002-1962 _w444482
942			_cJA _n0 _2ddc
999			_c66002 _d65994