| 000 | 01730nam a22003737a 4500 | ||
|---|---|---|---|
| 001 | G67882 | ||
| 003 | MX-TxCIM | ||
| 005 | 20240624235240.0 | ||
| 008 | 121211s1999|f| mx |p||0|| | e eng dd | ||
| 040 | _aMX-TxCIM | ||
| 041 | _aeng | ||
| 072 | 0 | _aP40 | |
| 090 | _aLook under series title | ||
| 100 |
_97228 _aHartkamp, A.D. |
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| 245 | 1 | 0 | _aInterpolation techniques for climate variables |
| 260 |
_aMexico : _bCIMMYT, _c1999. |
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| 300 | _a26 pages | ||
| 340 | _aPrinted|Computer File | ||
| 490 |
_aCIMMYT NRG-GIS Series ; _v99-01 _x1405-7484 |
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| 500 | _aOpen Access | ||
| 520 | _aThis paper examines statistical approaches for interpolating climatic data over large regions., providing a brief introduction to interpolation techniques for climate variables of use in agricultural research, as well as general recommendations for future research to assess interpolation techniques. Three approaches 1) inverse distance weighted averaging (IDWA), 2)thin plate smoothing splines and 3) co-kriging were evaluated for a 2,000 km2 square area covering the state of Jalisco, México. Taking into account valued error prediction, data assumptions, and computational simplicity, we recommend use of thin-plate smoothing splines for interpolating climate variables. | ||
| 546 | _aText in English | ||
| 591 | _aLSLinks|9912|AGRIS 0001|R99-00CIMPU|EE|DSpace 1 | ||
| 595 | _aCPC | ||
| 599 | _a5951.jpg | ||
| 650 | 1 | 7 |
_aAgriculture _gAGROVOC _91007 |
| 650 | 1 | 0 |
_aClimatic factors _91048 |
| 650 | 1 | 0 |
_aStatistical methods _92624 |
| 700 |
_aDe Beurs, K. _97229 |
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| 700 |
_aStein, A. _97230 |
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| 700 | 1 |
_aWhite, J.W. _91789 |
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| 856 | 4 |
_uhttp://hdl.handle.net/10883/988 _yOpen Access through DSpace |
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| 942 |
_cBK _2ddc |
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| 999 |
_c53652 _d53652 |
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