پهنه‎‎ بندی پارامترهای مؤثر در کیفیت حاصلخیزی خاک شالیزار برای مدیریت بهینه مصرف کود

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 عضو هیئت علمی موسسه تحقیقات خاک و آب، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران

2 فارغ‎التحصیل مقطع دکتری فیزیک و حفاظت خاک، دانشگاه تهران

3 گروه علوم خاک دانشگاه تربیت مدرس

چکیده

سابقه و هدف: گیاهان از جمله برنج برای تأمین نیاز عناصر غذایی خود به مصرف کود احتیاج دارند تا تولید بیشتر در واحد سطح فراهم شود. برای این منظور شناسایی مقادیر عناصر پرمصرف در خاک و تهیه نقشه مناسب آن‎ها ضروری است. ظرفیت تبادل کاتیونی (CEC ) خاک به عنوان شاخص مهمی از کیفیت حاصل‌خیزی و ظرفیت توقیف آلاینده‎های خاک و ویژگی‎های ازت، فسفر و پتاسیم به‎عنوان عناصر پرمصرف تغذیه گیاه محسوب می‎شود. این پژوهش با هدف برآورد و پهنه‎بندی خصوصیات مورد نظر انجام شد تا نتایج و نقشه‎های به دست آمده بتواند در مدیریت بهینه مصرف کودها و کنترل آلاینده‎های منابع آب زیرزمینی مورد استفاده قرار گیرد.
مواد و روش‎ها: منطقه مورد مطالعه با وسعت حدود 40000 هزار هکتار، جزء نواحی مرکزی استان گیلان می‎باشد. تعداد 247 نمونه خاک از عمق 30-0 سانتی‎متری برداشت شد. مقادیر ویژگی‎های CEC، ازت کل، فسفر و پتاسیم نمونه‎های خاک مشخص و آماره‎های توصیفی آن‎ها تعیین شد. بررسی توزیع نرمال داده‎ها با استفاده از آزمون کولموگروف-اسمیرنوف صورت گرفت. داده‌هایی که دارای توزیع نرمال نبود، با تبدیل‎های مناسب، نرمال شدند. قبل از استفاده از روش‌ درون‌یابی، آزمون روند و ناهمسانگردی انجام شد. نیم‎تغییرنما با روش کریجینگ معمولی محاسبه و نقشه‎ها ترسیم گردید.
یافته‎ها و بحث: مقدار پتاسیم از 78 تا 5/269 با میانگین 03/192 میلی‎گرم بر کیلوگرم، فسفر از 3/2 تا 56 با میانگین 51/16 میلی‎گرم بر کیلوگرم، مقدار ازت کل از 02/0 تا 8/0 با میانگین 26/0 درصد و CEC از 6/10 تا 1/47 با میانگین 72/26 سانتی‎مول‎بار بر کیلوگرم متغیر بود. مدل برازش شده بر نیم‎تغییرنمای ازت کل نمایی و ویژگی‎های فسفر، پتاسیم و CEC کروی بود. ضریب تبیین (R2) مدل‎ها دارای ارزش زیاد و نسبت اثر قطعه‎ای به آستانه کمتر از 25 درصد است. این مشخصات نشان می‎دهد که نیم‎تغییرنمای ویژگی‎ها دارای ساختار مکانی قوی است. پس از تعیین نیم‎تغییرنمای تجربی ویژگی‎ها، نقشه برآورد مقادیر آن‎ها با استفاده از روش کریجینگ معمولی تهیه شد. مقادیر معیارهای ارزیابی R2، RMSE و MAE برای پتاسیم 79/0، 84/27 و 106/0، فسفر 73/0، 17/8 و 63/4، ازت کل 72/0، 059/0 و 025/0 و ظرفیت تبادل کاتیونی 76/0، 06/4 و 09/3 به دست آمد. دقت نقشه‎های تهیه شده با توجه به مقادیر R2، RMSE و MAE قابل قبول بود. با دقت در نقشه‎های پهنه‎بندی، پراکنش مکانی مقدار پتاسیم در نواحی شمال‎غرب، غرب و مرکزی منطقه مورد مطالعه خوب بوده و بیشتر در شمال‎شرق و جنوب دارای کمبود می‎باشد. مقادیر فسفر و ازت در نواحی مرکزی تا شمال مناسب بوده و در جنوب منطقه دارای کمبود هستند. با توجه به نقشه‎ ازت و فسفر خاک، مصرف بیش از حد بهینه کودهای نیتراته و فسفره باعث آلودگی آب‎های زیرزمینی می‎شود. همچنین مصرف کودهای پتاسه در اراضی با مقادیر زیاد CEC، باعث تثبیت آن می‎شود که دقت در نقشه CEC و مصرف در موقع نیاز گیاه، این مشکل را مرتفع می‎سازد. بنابراین توجه دقیق به مقادیر این پارامترها در نقشه‎ها و حدود بحرانی و بهینه آن‎ها، می‎تواند، مصرف کودها را به‎طور قابل ملاحظه‎ای مدیریت بهینه کرده، از تحمیل هزینه‎های اضافی به کشاورز و آلودگی منابع آب زیرزمینی جلوگیری نماید.
نتیجه‎گیری: بررسی عناصر اصلی ازت، فسفر، پتاسیم و CEC در شناخت کیفیت حاصلخیزی خاک دارای اهمیت است. برای این منظور نقشه‎های پراکنش مکانی پارامترهای ذکر شده با تعیین نیم‎تغییرنمای تجربی با ساختار مکانی قوی، با استفاده از روش کریجینگ تهیه شد. معیارهای R2، RMSE و MAE نشان داد که دقت نقشه‎های پهنه‎بندی قابل قبول است. پراکنش مکانی مقدار پتاسیم در نواحی شمال‎غرب، غرب و مرکزی منطقه مورد مطالعه مناسب بوده و غالباً در شمال‎شرق و جنوب دارای کمبود می‎باشد. همچنین مقادیر فسفر و ازت در نواحی مرکزی تا شمال خوب بوده و در جنوب منطقه دارای کمبود هستند.

کلیدواژه‌ها


عنوان مقاله [English]

Mapping of Effective Parameters on Paddy Soils Fertility Quality for Optimum Management of Fertilizer Application

نویسندگان [English]

  • Hamed Rezaei 1
  • Leila Esmaeel Nejad 2
  • Saeed Saadat 1
  • Parisa Malaki 3
1 Faculty member of soil and water research institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
2 PhD of soil physics and conservation, University of Tehran
3 Ph.D student of soil physics and conservation, Soil science department, University of Tarbiat Modarres
چکیده [English]

Objective and background: Plants such as rice need to provide their nutrient elements by using fertilizers for much more production in surface unit. For this purpose, it is essential to recognize macro-elements amount in soils and prepare their ideal maps. Soil CEC is a vital indicator of soil fertility quality and pollutant sequestration capacity as well as characteristics of N, P, K as macro-elements. This research was conducted with the aim of estimating and mapping the desired properties in order to obtain the results and maps that could be used in optimum management of fertilizer use and control of groundwater contaminants.
Materials and methods: The study area with an area of about 40,000 hectares is one of the central areas of Guilan province. 247 soil samples were collected from depth 0-30cm. The values of CEC, total nitrogen, phosphorus and potassium in soil samples and their descriptive statistics were determined. The normal distribution of data was analyzed using Kolmogrov-Smirnov test. Data that did not have normal distribution was converted to normal with appropriate transformations. Before the use of interpolation method, trend and anisotropy analysis were performed. Semi-variograms were calculated using ordinary Kriging and maps were plotted.
Results and discussion: The amount of K and P varied from 78 to 269.5 mgkg-1 and from 2.3 to 56 mgkg-1, respectively. The average contents of K and P were 192.03 and 16.51 mgkg-1, respectively. The amount of total N changed from 0.02% to 0.8%, which its average was 0.26%. Also, the content of CEC varied from 10.6 to 47.1 cmolckg-1 and its average was 26.72 cmolckg-1. The fitted model was based on semi-variograms of total nitrogen was exponential and those of phosphorus, potassium, and CEC were spherical. Determination coefficient (R2) of models had high value and the nugget effect/threshold is less than 25%. These characteristics showed that semi-variograms of properties had strong spatial structure. After specifying the semi-variograms, a map of their values was prepared using ordinary Kriging. Evaluation criteria values R2, RMSE and MAE derived for K 0.79, 27.84 and 0.106, P 0.73, 8.17 and 4.63, total nitrogen 0.72, 0.059 and 0.025 and CEC 0.76, 4.06 and 3.09. Criteria values R2, RMSE and MAE showed that accuracy of prepared maps was ideal. With attention to interpolation maps, spatial distribution of K was good in western, north-western, and central area of studied region. K deficiency was concentrated in southern and north-eastern areas. The amounts of P and total N were suitable in central and northern areas which their deficiencies were observed in southern area. With regard to soil nitrogen and P maps, usage of more than optimum limit of nitrate and phosphorus fertilizers causes ground waters pollution. Potash fertilizers application in land with high CEC results its fixation, too. Precise attention to CEC map and on-time fertilizer application can solve this problem. Therefore, accurate notice to different amounts of these parameters in maps, critical and optimum limits can well manage fertilizers application, prevents additional costs to farmer and pollution of ground water resources.
Conclusion: Since the investigation of N, P, K, and CEC is important for determination of soil fertility quality, so, the maps of spatial distribution of mentioned parameters were prepared via determination of experimental semi-variogram with strong spatial structure using kriging. The criteria of R2, RMSE and MAE showed that maps accuracy was acceptable. Spatial distribution of K was good in western, north-western, and central area of studied region. K deficiency was almost concentrated in southern and north-eastern areas. The contents of P and total N were suitable in central and northern areas which their deficiencies were observed in southern area.

کلیدواژه‌ها [English]

  • CEC
  • Kriging
  • NPK
  • Paddy soil
  • Guilan
1.Adriana, L.D. 2007. On the use of soil hydraulic conductivity functions in the field. Soil Science. 93: 1. 162-170.
2.Aishah, A.W., Zauyah, S., Anuar, A.R., and Fauziah, C.I. 2010. Spatial variability of selected chemical characteristics of paddy soils in Sawash Sempadon, Selangor, Malaysia. Malaysi. J. Soil Sci. 14: 1. 27-39.
3.Altin, A., and Degirmenci, M. 2005. Lead (II) removal from natural soils by enhanced electrokinetic remediation. Science of the Total Environment. 337: 1-3. 1-10.
4.Andronikov, S.V., Davidson, D.A., and Spiers, R.B. 2000. Variability in contamination by heavy metals: sampling implications. Water, Air and Soil Pollution. 120: 1-2. 29-45.
5.Arias, M., Perez-Novo, C., Osorio, F., Lopez, E., and Soto, B. 2005. Adsorption and desorption of copper and zinc in the surface layer of acid soils. J. Coll. Inter. Sci. 288: 1. 21-29.
6.Asadzadeh, F., Akbarzadeh, A., Zolfaghari, A.A., Taghizadeh Mehrjardi, R., Mehrabanian, M., Rahimi Lake, H., and Sabeti, M.A. 2012. Study and comparison of some geostatistical methods for mapping cation exchange capacity in soils of northern Iran. Annals of Faculty Engineering Hunedoara. 1: 1. 59-66.
7.Behera, S.K., Mathur, R.K., Shukla, A.K., Suresh, K., and Prakash, C. 2018. Spatial variability of soil properties and delineation of soil management zones of oil palm plantations grown in a hot and humid tropical region of southern India. Catena. 165: 251-259.
8.Bogunovic, I., Trevisani, S., Seput, M., Juzbasic, D., and Durdevic, B. 2017. Short-range and regional spatial variability of soil chemical properties in an agro-ecosystem in eastern Croatia. Catena. 154: 50-62.
9.Burt, R. 2014. Soil survey laboratory methods manual. Soil survey investigations report No. 42, Version 5. United States Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center.
10.Chung, N., and Alexander, M. 2002. Effect of soil properties on bioavailability and extractability of phenanthrene and atrazine sequestered in soil. Chemosphere. 48: 1. 109-115.
 11.Davatgar, N., Neishabouri, M.R., and Sepaskhah, A.R. 2012. Delineation of site specific nutrient management zones for a paddy cultivated area based on soil fertility using fuzzy clustering. Geoderma. 173-174: 111-118.
12.Doberman, A., and Fairhurst, T.H. 2000. Rice: Nutrient Disorders and Nutrient Management. International Rice Research Institute, Philippines.
13.Dou, F., Yu, X., Ping, C., Michaelson, G., Guo, L., and Jorgenson, T. 2010. Spatial variation of tundra soil organic carbon along the coastline of northern Alaska. Geoderma. 154: 3-4. 328-335.
14.Fu, Q., Wang, Z., and Jiang, Q. 2010. Delineating soil nutrient management zones based on fuzzy clustering optimized by PSO. Mathematical and Computer Modelling. 51: 11-12. 1299-1305.
15.Fu, W., Tunney, H., and Zhang, C. 2010. Spatial variation of soil nutrients in a dairy farm and its implications for site-specific fertilizer application. Soil & Tillage Research. 106: 2. 185-193.
16.Isimail, M.H., and Junusi, R. 2009. Determining and mapping soil nutrient content using geostatistical technique in a Durian orchard in Malaysia. J. Agric. Sci. 1: 1. 86-91.
17.Karimi Amirkiasar, M., Ardalan, M., Kavoosi, M., and Shokri Vahed, H. 2011. Determination of Phosphorus Critical Level in Some of Paddy Soils in Guilan. J. Water Soil. 25: 4. 814-822. (In Persian)
18.Kavoosi, M., and Malakoti, M.J. 2006. Determination of available potassium critical level with ammonium acetate extractor in Guilan paddy soils. J. Sci. Technol. Agric. Natur. Resour. 10: 3. 113-123. (In Persian)
19.Keshavarzi, A., Sarmadian, F., Rahmani, A., Ahmadi, A., Labbafi, R., and Iqbal, M.A. 2012. Fuzzy clustering analysis for modeling of soil cation exchange capacity. Austr. J. Agric. Engin. 3: 1. 27-33.
20.Lin, H., Wheeler, D., Bell, J., and Wilding, L. 2004. Assessment of soil spatial variability at multiple scales. Ecological Modelling. 182: 3-4. 271-290.
21.Lotfi Arpachaei, Z., Esmali Ouri, A., Hashemimajd, K., and Najafi, N. 2013. Soil fertility evaluation of Ardabil plain for wheat and potato based on some soil chemical properties by AHP and GIS techniques. J. Water Soil. 27: 1. 45-53. (In Persian)
22.Meng, Y., Cave, M., and Zhang, C. 2018. Spatial distribution patterns of phosphorus in top-soils of Greater London Authority area and their natural and anthropogenic factors. Applied Geochemistry. 88: 213-220.
23.Moral, F.J., Terrón, J.M., and Rebollo, F.J. 2011. Site-specific management zones based on the Rasch model and geostatistical techniques. Computers and Electronics in Agriculture. 75: 2. 223-230.
24.Ramzan, Sh., and Wani, M.A. 2018. Geographic information system and geostatistical techniques to characterize spatial variability of soil micronutrients including toxic metals in an agricultural farm. Communications in Soil Science and Plant Analysis. 49: 4. 463-477.
25.Robinson, T.P., and Metternicht, G. 2006. Testing the performance of spatial interpolation techniques for mapping soil properties. Computers and Electronics in Agriculture. 50: 2. 97-108.
26.Rosemary, F., Vitharana, U.W.A., Indraratne, S.P., Weerasooriya, R., and Mishra, U. 2017. Exploring the spatial variability of soil properties in an Alfisol soil catena. Catena. 150: 53-61.
27.Sarmadian, F., and Keshavarzi, A. 2014. The use of a hybrid fuzzy-AHP system on the evaluation and mapping of soil fertility. Soil and Water Resources Conservation. 3: 2. 45-56. (In Persian)
28.Shi, J., Wang, H., Xu, J., Wu, J., Liu, X., Zhu, H., and Yu, C. 2007. Spatial distribution of heavy metals in soils: A case study of Changxing, China. Environ. Geol. J. 52: 1. 1-10.
29.Site, A.D. 2001. Factors affecting sorption of organic compounds in natural sorbent/water systems and sorption coefficients for selected pollutants. A review. J. Physic. Chem. Ref. Data. 30: 1. 187-439.
 30.Soil Survey Staff. 2014. Keys to Soil Taxonomy. 12th edition, United State Department of Agriculture, National Soil Survey Center. Natural Resources Conservation Service.
31.Takodjou Wambo, J.D., Ganno, S., Djonthu Lahe, Y.S., Kouankap Nono, G.D., Fossi, D.H., Tchouatcha, M.S., and Nzenti, J.P. 2018. Geostatistical and GIS analysis of the spatial variability of alluvial gold content in Ngoura-Colomines area, Eastern Cameroon: Implications for the exploration of primary gold deposit. J. Afric. Earth Sci. 142: 138-157.
32.Tang, L., Zeng, G.M., Nourbakhsh, F., and Shen, G.L. 2009. Artificial neural network approach for predicting cation exchange capacity in soil based on physico-chemical properties. Environmental Engineering Science. 26: 1. 137-146.
33.Tesfahunegn, G.B., Tamene, L., and Vlek, P.L.G. 2011. Catchment-scale spatial variability of soil properties and implications on site-specific soil management in northern Ethiopia. Soil & Tillage Research. 117: 124-139.
34.Tripathi, R., Nayaka, A.K., Shahid, M., Lal, B., Gautama, P., Raja, R., Mohanty, S., Kumar, A, Panda, B.B., and Sahoob, R.N. 2015. Delineation of soil management zones for a rice cultivated area in eastern India using fuzzy clustering. Catena. 133: 128-136.
35.Vasu, D., Singh, S.K., Sahu, N., Tiwary, P., Duraisami, V.P., Ramamurthy, V., Lalitha, M., and Kalaiselvi, B. 2017. Assessment of spatial variability of soil properties using geospatial techniques for farm level nutrient management. Soil & Tillage Research. 169: 25-34.
36.Webster, R., and Oliver, M. 2007. Geostatistics for Environmental Scientists. 2nd edition, John Wiley & Sons Ltd, Chichester UK.
37.Zhang, H., Xu, M.G., Zhang, W., and He, X.H. 2009. Factors affecting potassium fixation in seven soils under 15-year long-term fertilization. Chinese Science Bulletin, Articles/Geography. 54: 10. 1773-1780.