پیش‌بینی مکانی عملکرد گندم با استفاده از نقشه‌برداری رقومی خاک در منطقه گتوند استان خوزستان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه اردکان

2 موسسه خاک و آب

3 دانشگاه تهران

چکیده

در پژوهش حاضر، مقدار 110 عملکرد مشاهداتی در منطقه گتوند (استان خوزستان) با استفاده از مدل برنامه‌ریزی ژنتیک، به داده‌های کمکی (مستخرج شده از مدل رقومی ارتفاع و تصویر ماهواره) ارتباط داده شد. سپس با استفاده از معادله بدست آمده برای نقاط فاقد مشاهده میزان عملکرد برآورد و نقشه تغییرات مکانی محاسبه گردید. الگوریتم رپر پارامترهای مولفه تصویر ماهواره، شاخص نسبت گیاهی، شاخص گیاهی تعدیل کننده اثر خاک، شاخص خیسی و سطح پایه شبکه زهکشی را به عنوان مهم‌ترین عوامل تولید شناخته است. میانگین ریشه مربعات خطا، ضریب همبستگی تطابق لاین و ضریب تبیین ارزیابی متقابل مدل برنامه‌ریزی ژنتیک (1) با همه داده‌های کمکی به ترتیب 11/525، 87/0 و 82/0 است. همچنین نتایج نشان داد که مدل برنامه‌ریزی ژنتیک (2) با داده‌های کمکی انتخاب شده توسط الگوریتم رپر نیز به خوبی (میانگین ریشه مربعات خطا، ضریب همبستگی تطابق لاین و ضریب تبیین به ترتیب 82/530، 86/0 و 79/0 است) قادر به پیش‌بینی عملکرد گندم است. لذا پیشنهاد می‌گردد در مطالعات آتی جهت برآورد مکانی عملکرد محصولات زراعی از مدل برنامه‌ریزی ژنتیک در قالب نقشه‌برداری رقومی خاک استفاده گردد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Spatial Prediction of wheat crop yield Using Digital Soil Mapping in Gotvand, Khuzestan Province

نویسندگان [English]

  • Roohollah Taghizadeh Merjerdi 1
  • Seyed Alireza Seyed Jalali 2
  • Fereydoon Sarmadian 3
1
2
3
چکیده [English]

A number of 110 observed crop yields were correlated with auxiliary variables (DEM and Landsat images) using genetic programming (GP) in Gotvand area (Khuzestan Province). The spatial prediction map of wheat crop yield was prepared using the obtained equation. Wrapper algorithm identified some more important auxiliary variables Nof: DVI, SAVI, and wetness index and channel network based level. RMSE, coefficient of determination and Lin's concordance coefficient of GP (1) with all the auxiliary data were obtained as 525.11, 0.87 and 0.82, respectively. Moreover, results indicated GP (2) with auxiliary data selected through wrapper algorithm could also reasonably predict wheat crop yield (RMSE, coefficient of determination and Lin's concordance coefficient, 530.82, 0.86 and 0.79, respectively). It can, therefore, be recommended to use the same approach to predict spatial distribution of crop yields in the future studies.  

کلیدواژه‌ها [English]

  • Auxiliary variables
  • wrapper algorithm
  • Genetic programming
  • Spatial Variation
Becker-Reshef, E., Vermote, A., Lindeman, M. and Justice, C. (2010). A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment, 114, 1312–1323.
Brus, DJ., Kempen, B. and Heuvlink, GBM. (2011). Sampling for validation of digital soil maps. European Journal of Soil Science, 62, 394–407.
Cahn, M. D., Hummel, J. W. and Brouer, B. H. (1994). Spatial analysis of soil fertility for site-specific crop management. Soil Science Society American Journal, 58, 1240-1248.
Charles, J., Godfray, J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., Pretty, J., Robinson, S., Thomas, S.M. and Toulmin. C. (2010). Food security: the challenge of feeding 9 billion people. Science, 327, 812–818.
Chen, Z. X., Ren, J. Q., Zhou, Q. B. and Tang, H. J. (2008). Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation, 10, 403−413.
Chipanshi, A. C., Ripley, E. A. and Lawford, R. G. (1999). Large-scale simulation of wheat yields in a semi-arid environment using a crop-growth model. Agricultural Systems, 59, 57−66.
Doraiswamy, P. C., Moulin, S., Cook, P. W. and Stern, V. (2003). Crop yield assessment from remote sensing. Photogrammetric Engineering and Remote Sensing, 69, 665−674.
Florinsky, IV., McMahon, S. and Burton, DL. (2004). Topographic control of soil microbial activity: a case study of denitrifiers. Geoderma, 119: 33-53.
Johari, A., Habibagahi, G. and Ghahramani, A. (2006). Prediction of soil–water characteristic curve using genetic programming. Journal of Geotechnical and Geoenvironmental Engineering,  132, 661–665.
Johari, A., Habibagahi, G. and Ghahramani, A. (2006). Prediction of soil–water characteristic curve using genetic programming. Journal of Geotechnical and Geoenvironmental Engineering, 132, 661–665.
Khattree, R. and Naik, D. N. (2000). Multivariate Data Reduction and Discrimination with SAS Software. SAS Institute Inc., Cary, NC.
Koza, J., Bennett, H., Andre, D. and Keane, M. (1999). Genetic programming 400 III: Darwinian invention and problem solving. Burlington, MA: Morgan Kaufmann.
Kravchenko, AN. And Bullock, DG. (2000). Correlation of corn and soybean grain yield with topography and soil properties. Agronomy Journal, 92, 75-83.
Li, Y., Shi,  Z., Li,  F. and  Li, H. Y. (2007). Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land. Computers and Electronics in Agriculture, 56, 174-186.
Macdonald, RB. and Hall, FG. (1980). Global crop forecasting. Science, 208, 670–679.
Makkeasorn, A., Chang, NB., Beaman, M., Wyatt, C. and Slater, C. (2006). Soil moisture estimation in a semiarid watershed using RADARSAT- 1 satellite imagery and genetic programming. Water Resources Research, 42, 1-15.
Maselli, F. and Rembold, F. (2001). Analysis of GAC NDVI data for cropland identification and yield forecasting in Mediterranean African countries. Photogrammetric Engineering and Remote Sensing, 67, 593−602.
Mathworks, 2010. Matlab Version 7.0. The Mathworks Inc., Natick, MA.
McBratney, AB., Mendonca-Santos, ML. and Minasny, B. (2003). On digital soil mapping. Geoderma, 117,3–52.
Mouser, P.  J., Rizzo, D. M., Roling, W. F. M. and Van Breukelen,  B. M. (2005). A multivariate statistical approach to spatial representation of groundwater contamination using hydrochemistry and microbial community profiles. Environmental Science & Technology, 39, 7551-7559.
Nosrati, H. and Eftekhari, M. (2014). A new approach for variable selection using fuzzy logic. Computational Intelligence in Electrical Engineering, 4, 71-83.
Padarian, J., Minasny, B. and McBratney, A. (2012). Using genetic programming to transform from Australian to USDA/FAO soil particle-size classification system. Australian Journal of Soil Research, 50, 443-446.
Padarian, J., Minasny, B. and McBratney, A. (2012). Using genetic programming to transform from Australian to USDA/FAO soil particle-size classification system. Australian Journal of Soil Research, 50, 443–446.
Padilla, F. L. M., Maas, S. J., Gonz, M.P., Lez-Dugo., F.  Mansilla, N., Rajan, Gavil, P., and Donguez, J. (2012). Monitoring regional wheat yield in Southern Spain using the GRAMI model and satellite imagery. Field Crops Research, 130, 145–154.
Parasuraman, K., Elshorbagy, A. and Si, BC. (2007). Estimating saturated hydraulic conductivity using genetic programming. Soil Science Society of American Journal, 71, 1676–1684.
Rembold, F., Atzberger, C., Savin, I. and Rojas, O. (2013).  Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection.  Remote Sensing, 5, 1704-1733.
Shabani, A., Haghnia, Gh., Karimi, A. and Ahmadi, M.M. (2012). Influence of Topography and Soil Characteristics on the Rainfed Wheat Yield in Sisab Region, Northeastern Iran. Journal of Water and Soil, 26, 922-932. 
Shahbazi, F., Jafarzadeh, A.A., Sarmadian, F., Neyshaboury, M.R., Oustan, Sh., Anaya- Romero, M. and De la Rosa, D. (2009). Suitability of Wheat, Maize, Sugar Beet and Potato Using MicroLEIS DSS Software in Ahar Area, North-West of Iran. American-Eurasian Journal of Agriculture and Environment Science, 5, 45-52.
Soil survey staff. (2010). Keys to soil taxonomy. Eleventh Edition. USDA. NRCS. 338pp.
Taghizadeh-Mehrjardi, R. (2015). Digital mapping of cation exchange capacity using genetic programming and soil depth functions in Baneh region, Iran. Archive of Agronomy and Soil Science, xxx-xxx.
Toscano, P., Ranieri, R., Matese, A., Vaccari, F. P., Gioli , B. A., Zaldeia, M., Silvestri, C., Ronchi, P., La-Cava, J.R.,  Porter, A. and Miglietta, F. (2012). Durum wheat modeling: The Delphi system, 11 years of observations in Italy. European Journal of Agronomy, 43, 108–118
Tucker, C.J., Vanpraet, C., Boerwinkel, E. and Gaston, A. (1983). Satellite remote sensing of total dry matter production in the Senegalese Sahel. Remote Sensing of  Environment, 13, 461–474.
Tucker, CJ., Holben, BN., Elgin, JH. and McMurtrey, JE. (1981). Remote sensing of total dry-matter accumulation in winter wheat. Remote Sensing of Environment, 11, 171–189.
Wall, L., Larocque, D. and Leger, P. M. (2007). The early explanatory power of NDVI in crop yield modeling. International Journal of Remote Sensing, 29, 2211−2225.
Wilding, L. (1985). Spatial variability. Its documentation, accommodation, and implication to soil surveys. In: D. R. Nielson and J. Bouma (Eds). Soil Variability, Pudo, Wagenigen, the Netherlands.