ارزیابی و پهنه‌بندی شاخص‌های کیفیت تجمعی و نِمِرو خاک و ارتباط آنها با عملکرد گل محمدی (مطالعه موردی: شهرستان بردسیر، استان کرمان)

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 دانشجوی دکتری گروه علوم خاک، دانشگاه شهرکرد،

2 استاد گروه علوم خاک، دانشگاه شهرکرد،

3 دانشیار گروه علوم خاک، دانشگاه ولیعصر (عج) رفسنجان،

4 استادیار گروه خاک‌شناسی، دانشگاه شهرکرد

چکیده

سابقه و هدف: ارزیابی کیفیت خاک اراضی کشاورزی، امری ضروری برای موفقیت‌های اقتصادی و پایداری محیط زیست در مناطق در حال توسعه می‌باشد. در حال حاضر انواع بسیار زیادی از روش‌ها برای ارزیابی کیفیت خاک استفاده می‌شوند که هر کدام معیارهای متفاوتی را به‌کار می‌گیرند. با توجه به اینکه شهرستان بردسیر یکی از مهمترین قطب‌های تولیدکننده گل محمدی در استان کرمان و ایران می‌باشد و نظر به اهمیت ارزیابی کیفیت خاک به‌عنوان شاخصی از کشاورزی پایدار و بهره‌برداری بهینه از منابع طبیعی، در این پژوهش، کیفیت خاک در بخشی از اراضی کشت‌شده گل محمدی با استفاده از شاخص کیفیت خاک تجمعی (IQI) و شاخص کیفیت خاک نمرو (NQI) در ترکیب با دو روش انتخاب معیار کل مجموعه داده‌ها (TDS) و حداقل مجموعه داده‌ها (MDS) برای دو عمق 25-0 و
50-25 سانتی‌متری ارزیابی شد.
مواد و روش‌ها: برای اجرای این تحقیق مزرعه‌ای به مساحت 30 هکتار در شهرستان بردسیر در استان کرمان انتخاب شد. سپس موقعیت 100 محل برای اندازه‌گیری ویژگی‌های خاک (عمق 25-0 و 50-25 سانتی‌متر) و عملکرد گیاه مشخص و نمونه‌برداری صورت گرفت. با استفاده از روش تجزیه مولفه‌های اصلی (PCA) از میان کل ویژگی‌های موثر بر کیفیت خاک، مهمترین ویژگی‌های موثر بـر کیفیـت خاک (MDS) تعیین شدند. نتایج نشان داد که در عمق اول متغیرهای ماده آلی، شن، منگنز، کربنات کلسیم معادل، روی و مس و در عمق دوم هم متغیرهای شن، پتاسیم، کربنات کلسیم معادل، روی، فسفر، سنگریزه و منگنز به‌عنوان مجموعه حداقل داده‌ها انتخاب شدند. سپس کیفیت خاک، بـا اسـتفاده از دو مـدل شـاخص تجمعی کیفیت خاک (IQI) و شاخص کیفیت خاک نمرو (NQI) و هر کدام در دو مجموعه‌ی ویژگی‌های خاک TDS و MDS ارزیـابی شـد و نتایج چهار روش‌ ترکیبی ارزیابی کیفیت خاک مزبور از طریق مقایسه با عملکرد گل محمدی آنالیز شد.
یافته‌ها: نتایج نشان داد که ضریب همبستگی بین شاخص‌های IQITDS و IQIMDS و بین NQITDS و NQIMDSدر عمق 25-0 سانتی‌متر به‌ترتیب برابر با 85/0 و 79/0 بود. همچنین ضریب همبستگی بین شاخص‌های IQITDS و IQIMDS و بین NQITDS و NQIMDSدر عمق 50-25 سانتی‌متر به‌ترتیب برابر با 75/0 و 77/0 به‌دست آمد. تجزیه زمین‌آماری شاخص‌های کیفیت خاک نشان داد که تمامی شاخص-های بررسی شده خاک و عملکرد گل محمدی، دارای مدل کروی و ساختار مکانی قوی و متوسط می‌باشند. دامنه تأثیر تغییرنماها از 33/119 متر برای شاخص IQITDS در عمق دوم تا 8/151 متر برای شاخص NQITDS در عمق اول در نوسان بود. همچنین دامنه تأثیر عملکرد گل محمدی، 16/122 متر به‌دست آمد. همبستگی نقشه‌های کریجینگ عملکرد گل محمدی و شاخص‌های کیفیت خاک نشان داد که در هر دو عمق مطالعاتی، بیشترین همبستگی بین عملکرد و شاخص IQITDS می‌باشد. همچنین، نتایج همبستگی بین شاخص‌های کیفیت خاک و عمکلرد گل محمدی نشان داد که شاخص IQITDS نسبت به سایر شاخص‌های همبستگی بالاتری با عملکرد دارد.
نتیجه‌گیری: این نتایج نشان داد که شاخص کیفیت تجمعی (IQI) به‌ویژه در مجموعه TDS، کارایی بهتری برای ارزیابی کیفیت خاک منطقه مورد مطالعه دارد. همچنین، نتایج این پژوهش نشان داد هرچند استفاده از مجموعه TDSدر تعیین شاخص‌های کیفیت خاک نتایج بهتری ارائه می‌کند، اما به‌دلیل همبستگی نسبتاً خوب این مجموعه با مجموعه داده‌های حداقل (MDS) این امکان وجود دارد که با استفاده از MDS نیز بتوان شاخص‌های کیفیت خاک مزارع گل ‌محمدی در منطقه را با دقت مناسبی تعیین کرد که این کار موجب کاهش حجم مطالعات و هزینه می‌شود. با این حال، اگر هدف از ارزیابی کیفیت خاک، رسیدن به عملکرد بهینه و مطلوب باشد، استفاده از شاخص IQITDS به‌دلیل همبستگی بیشتر این شاخص با عملکرد گل‌ محمدی، کارایی بهتری دارد.

کلیدواژه‌ها

عنوان مقاله [English]

Assessing and Mapping of Integrated and Nemero Soil Quality Indices and their Relationship with Rose Yield (A Case Study: Bardsir, Kerman Province)

نویسندگان [English]

  • Morteza bahmani 1
  • Jahangard Mohammadi 2
  • Esa Esfandiyarpoor 3
  • Hamidreza Mottaghian 4
1 Department of Soil Science and Engineering, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran
2 Dept. of Soil Science, Shahrekord University
3 Dept. of Soil Science, Vali-e-Asr Univ. of Rafsanjan
4 Assistant Prof, Dept. of Soil Science, Shahrekord University,
چکیده [English]

Background and Objective: The assessment of soil quality for agricultural land is essential for the economic successes and sustainability of the environment in developing countries. Currently, many types of methods with different factors are used to evaluate soil quality. Soil quality evaluation is an indicator of sustainable agriculture and optimized application of utilization of natural resources. Moreover, the Bardsir city is one of the most important regions of Rose (Rosa Damasceneea Mill) in Kerman province and Iran. In this research, soil quality were evaluated in a part of the cultivated land of Rose using integrated soil quality index (IQI) and Nemoro soil quality index (NQI) in combination with two of data sets, total data set (TDS) and minimum data set (MDS) for 0-25 and 25-50 cm depth.
Materials and Methods: In this research, a farm with an area of 30 hectares was selected in Bardsir city, Kerman province. Then, 100 locations were sampled to determine soil characteristics (0-25 and 25-50 cm), then in each location, the yield was determined. Among the total characteristics of soil quality, the most important of characteristics were determined Using principal component analysis (PCA). The results showed that in the topsoil and subsoil, minimum data set were organic matter, sand, Mn, calcium carbonate equivalent, Zn, Cu and sand, K, calcium carbonate equivalent, Zn, P, fragments and Mn, respectively.
Results: The results showed that at the 0-25 cm, the correlation between IQITDS and IQIMDS and between NQITDS and NQIMDS were 0.85 and 0.79, respectively. Also, at the depth of 25-50 cm, the correlation between IQITDS and IQIMDS and between NQITDS and NQIMDS were 0.75 and 0.77, respectively. The geostatistical analysis of soil quality indices showed that all of the studied soil parameters and Rose yield have spherical model and strong to medium spatial structure.
The range of variograms is varied from 119.33 m for IQITDS in the subsoil to 151.8 m for NQITDS in the topsoil. Besides, the range of variogram for Rose yield was 122.16 m. The correlation between kriging maps of Rose yield and soil quality indices showed that the highest correlation was found between the yield and the IQITDS index at both studied depths. The results of correlation between soil quality indices and Rose yield showed that the IQITDS index has a higher correlation with yield than other indices.
Conclusion: These results demonstrated that the IQI index, especially in the TDS set, has a better performance for assessing the soil quality in the study area. Because of the relatively good correlation between this set and MDS, the TDS set is better to determine soil quality indices; this result may be attributed to use MDS to determine the soil quality indices with proper accuracy. However, if the purpose of soil quality assessment is to achieve the optimal yield, the use of IQITDS index, due to its greater correlation with the Rose yield, has a better performance.

کلیدواژه‌ها [English]

  • Total data set
  • Minimum data set
  • Principal Component Analyze
  • Mapping
  • Yield

مراجع

1.Al-Kanani, T., Mackenzi, A.F., and Ross, G.J. 1984. Potassium status of some Quebec soils: K release by nitric acid and sodium tetraphenylboron as related to particle size and mineralogy. Can. J. Soil Sci. 64: 99-106.
2.Andrews, S.S., Karlen, D.L., and Mitchell, J.P. 2002a. A comparison of soil quality indexing methods for vegetable production systems in Northern California. Agriculture, Ecosystems and Environment. 90: 25-45.
3.Andrews, S.S., Mitchell, J.P., Mancinelli, R., Karlen, K.L., Hartz, T.K., Horwath, W.R., Pettygrove, G.S., Scow, K.M., and Munk, D.S. 2002b. On-farm assessment of soil quality in California's central valley. Agron. J. 94: 12-23.
4.Aparicio, V., and Costa, J.L. 2007.Soil quality indicators under continuous cropping systems in the Argentinean pampas. Soil Tillage Research. 96: 155-165.
5.Azarneshan, S., Khormali, F., Sarmadian, F., Kiani, F., and Eftekhari, K. 2014. Soil Quality Evaluation of Semi-arid and Arid Lands in Qazvin Plain, Iran. J. Water Soil. 32: 2. 359-374. (In Persian)
6.Batchelor, W.D., Basso, B., and Paz, J.O. 2002. Examples of strategies to analyse spatial and temporal yieldusing crop models. Europ. J. Agron. 18: 141-158.
7.Bone, J., Barraclough, D., Eggleton, P., Head, M., Jones, D., and Voulvoulis, N. 2012. Prioritising soil quality assessment through the screening of sites: the use of publicly collected data. Land Degradation and Development, 25: 3. 251-266.
8.Brady, N.C., and Weil, R.R. 2002. The nature and properties of soils, 14th ed. Prentice Hall, New Jersey. P 311-354.
9.Bremner, J.M., and Mulvaney, C.S. 1982. Total nitrogen. P 595-624. In: A.L.Page (Ed.), Methods of Soil Analysis. Agronomy. No. 9, Part 2: Chemical and Microbiological Properties, 2nd ed., American Society of Agronomy Madison, Wisconsin, USA.
10.Cambardella, C.A., Moorman, T.B., Parkin, T.B., Karlen, D.L., Turco, R.F., and Konopka, A.E. 1994. Field scale variability of soil properties in Central Iowa soils, Soil Sci. Soc. Amer. J.58: 1501-1511.
11.Carter, M.R., Gregorich, E.G., Anderson, D.W., Doran, J.W., Janzen, H.H., and Pierce, F.J. 1997. Concepts of soil quality and their significance.P 247-276. In: E.G. Gregorich, and M.R. Carter, (eds), Soil quality for crop production and ecosystem health.
12.Chen, Y.D., Wang, H.Y., Zhou, J.M., Xing, L., Zhu, B.S., Zhao, Y.C., and Chen, X.Q. 2013. Minimum Data Set for Assessing Soil Quality in Farmland of Northeast China. Pedosphere. 23: 5. 564-576.
13.DeAraújo, A.G., and Saraiva, A.M. 2003. Fuzzy modelling of soil compaction by agricultural machine traffic, Proceedings of the 4th Conference of the European Federation for Information Technology in Agriculture, Food and Environment (EFITA).5-9. July 2003. Debrecen. Hungary.
14.D’Hose, T., Cougnon, M., Vliegher, A.D., Vandecasteele, B., Viaene, N., Cornelis, W., Bockstaele, E.V., and Reheul, D. 2014. The positive relationship between soil quality and crop production: a case study on the effect of farm compost application. Applied Soil Ecology, 75: 189-198.
15.Doran, J.W., and Parkin, T.B. 1994. Defining and assessing soil quality.P 1-21. In J.W. Doran, D.C. Coleman, D.F. Bezdicek, and B.A. Stewart (eds.). Defining soil quality for a sustainable environment. Soil Science Society of America Special Publication no. 35, Madison, WI.
16.De Paz, J.M., Sanchez, J., and Visconti, F. 2006. Combined use of GIS and environmental indicators for assessment of chemical physical and biological soil degradation in a Spanish Mediterranean region. J. Environ. Manage. 79: 150-162.
17.Ghaemi, M., Astaraei, A.R., Sanaei Nezhad, S.H., Nasiri Mahalati, M., and Emami, H. 2013. Evaluating Chemical Quality of Several soil cultivation wheat-corn using of soil quality Models at some Parts of Southeast Mashhad area. Soil Research. 27: 4. 463-473.(In Persian)
18.Gee, W., and Bauder, J.W. 1986. Particle size analysis. P 383-411. In: A. Klute(ed.), Method of soil analysis.
Part 1. Soil Science Society of America. Madison, Wisconsin.
19.Golden Software. 2002. Surfer for Windows. Version 8, Golden Software Inc., Golden Co., USA.
20.Gorji, M., Kakeh, J., and Ali Mohammadi, A. 2017. Quantitative assessment of soil quality in different landuses in part of southern East of Qazvin. Iran. J. Soil Water Res.45. 4: 775-784. (In Persian)
21.Govaerts, B., Sayre, K.D., and Deckers, J. 2006. A minimum data set for soil quality assessment of wheat and maize cropping in the highlands of Mexico. Soil and Tillage Research. 87: 163-174.
22.Huang, B., Sun, W.X., Zhao, Y.C., Zhu, J., Yang, R.Q., Zou, Z., Ding, F., and Su, J.P. 2007. Temporal and spatial of soil organic matter and total nitrogen in an agricultural ecosystem as affected by farming practices. Geoderma. 139: 336-345.
23.Imaz, M.J., Virto, I., Bescansa, P., Enrique, A., Fernandez-Ugalde, O., and Karlen, D.L. 2010. Soil quality indicator response to tillage and residue management on semi-arid Mediterranean cropland. Soil and Tillage Research. 107: 17-25.
24.Karlen, D.L., Gardner, J.C., and Rosek, M.J. 1998. A soil quality framework for evaluating the impact of CRP. J. Prod. Agric. 11: 56-60.
25.Karlen, D.L., Andrews, S.S., and Doran, J.W. 2001. Soil quality: Current concepts and applications. Advances in Agronomy. 74: 1-40.
26.Li, P., Zhang, T., Wang, X., and Yu, D. 2013. Development of biological soil quality indicator system for subtropical China. Soil and Tillage Research.126: 112-118.
27.Lindsay, W.L., and Norvell, W.A. 1978. Development of a DTPA soil test for zinc, iron, manganese and copper. Soil Sci. Soc. Amer. J. 42: 421-428.
28.Liu, Z.J., Wei, Z., Shen, J.B., Li, S.T., Liang, G.Q., Wang, X.B., and Chao, A. 2014. Soil Quality Assessment of Acid Sulfate Paddy Soils with Different Productivities in Guangdong Province, China. J. Integr. Agric. 13: 1. 177-186.
29.Liu, Y., Wang, H., Zhang, H., and Liber, K. 2016. A comprehensive support vector machine-based classification model for soil quality assessment. Soil and Tillage Research. 155: 19-26.
30.Mandal, U.K., Warrington, D.N., Bhardwaj, A.K., BarTal, A., Kautsky, L., Minz, D., and Levy, G.J. 2008. Evaluating impact of irrigation water quality on a calcareous clay soil using principal component analysis. Geoderma, 144: 189-197.
31.Mausbach, M.J., and Seybold, C.A. 1998. Assess-ment of soil quality.P 33-43. In: L. Rattan (ed.).  Soil quality and agricul-tural sustainability. Sleeping Bear Press, Chelsea.
32.Mohammadi, J. 2006. Pedometer (Spatial Statistics), Second volume, published by Pelk, 240p. (In Persian)
33.Moral, F.J., Terrón, J.M., and Marques da Silva, J.R. 2010. Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil and Tillage Research. 106: 335-343.
34.Nelson, D.W., and Sommers, L.E. 1982. Total carbon, organic carbon, and organic matter, P 539-579. In: A.L. Page (ed.) Methods of Soil Analysis Part 2. 2nd ed. Agronomy. Monogr 9. American Society of Agronomy and Soil Science Society of America, Madison, WI.
35.Oliver, Y.M., and Robertson, M.J. 2013. Quantifying the spatial pattern of the yield gap within a farm in a low rainfall Mediterranean climate. Field Crops Research. 150: 29-41.
36.Olsen, S.R., and Sommers, L.E. 1982. Phosphorus. P 403-430. In: A.L. Page (ed.), Methods of soil analysis, Agron. No. 9, Part 2: Chemical and Microbiological Properties, 2nd ed., American Society of Agronomy, Madison, Wisconsin, USA.
37.Ortega, R.A., and Santibáñez, O.A. 2007. Determination of management zones in corn (Zea mays L.) based on soil fertility. Computers and Electronics in Agriculture. 58. 49-59.
38.Page, A., Miller, R., and Keeney, D. 1982. Methods of Soil Analysis. 2th ed. Part 2: Chemical and biological properties. Soil Science Society of America. incorporated. publisher. 1143p.
39.Paz, J.O., Batchelor, W.D., Tylka, G.L., and Hartzler, R.G. 2001. A modeling approach to quantifying the effects of spatial soybean yield limiting factors. Transactions of the American Society of Agricultural Engineers. 44: 5. 1329-1344.
40.Pierce, F.J., Larson, W.E., and Dowdy, R.H. 1985. Soil loss tolerance: maintenance of long-term soil productivity. J. Soil Water Cons.39: 136-138.
41.Popp, J., Hoag, D., and Ascough, J.I. 2002. Targeting soil conservation policies for sustainability: new empirical evidence. J. Soil Water Cons. 57: 66-74.
 42.Qi. Y., Jeremy, L.D., Huang, B., Zhao, Y., Sun, W., and Gu, Z. 2009. Evaluating soil quality indices in an agricultural region of Jiangsu Province, China. Geoderma, 149: 325-334.
43.Rahmanipour, F., Marzaiolib, R., Bahrami, H.A., Fereidouni, Z., and Rahimi Bandarabadi, S. 2014. Assessment of soil quality indices in agricultural lands of Qazvin Province, Iran. Ecological Indicator. 40: 19-26.
44.Ramezani, F., Jafari, S., and Salavati, A. 2015. Study the Soil Quality Changes Indicators Using Nemoro and Integrated Quality Index Models in Some Khuzestan’s Soils. J. Water Soil.29: 6. 1629-1639. (In Persian)
45.Reynolds, W.D., Drury, C.F., Tan, C.S., Fox, C.A., and Yang, X.M. 2009. Use of indicators and pore volume-function characteristics to quantify soil physical quality. Geoderma, 152: 3-4. 252-263.
46.Rezaei, S.A., Gilkes, R.J., and Andrews, S.S. 2006. A minimum data set for assessing soil quality in rangelands. Geoderma. 136: 229-234.
47.Roel, A., and Plant, R.E. 2004. Spatiotemporal analysis of rice yield Variability in two California fields. Agron. J. 96: 77-90.
48.Seybold, C.A., Dick, R.P., and Pierce, F.J. 2001. USDA soil quality test kit: Approaches for comparative assessments. Soil Survey Horizons. 42: 43-52.
49.Shahab, H., Emami, H., Haghnia, G.H., and Karimi, A. 2013. Pore size distribution as a soil physical quality index for agricultural and pasture soils in northeastern Iran. Pedosphere. 23: 312-320.
50.Shukla, M.K., Lal, R., and Ebinger, M. 2006. Determining soil quality indicators by factor analysis. Soil Tillage Research. 87: 194-204.
51.Sun, B., Zhou, S.L., and Zhao, Q.G. 2003. Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of subtropical China. Geoderma. 115: 85-99.
52.Wang, Y., Zhang, X., and Huang, C. 2009. Spatial variability of soil total nitrogen and soil total phosphorus under different landuses in a small watershed on the Loess Plateau, China. Geoderma. 150: 141-149.
53.Yao, R.J., Yang, J.S., Zhao, X.F., Li, X.M., and Liu, M.X. 2013. Dtermining minimum data set for soil quality assessment of typical salt-affected farmland in the coastal reclamation area. Soil and Tillage Research. 128: 137-148.
54.Zalidis, G., Stamatiadis, S., Takavakoglou, V., Eskridge, K., and Misopolinos, N. 2002. Impacts of agricultural practices on soil and water quality in the Mediterranean region and proposed assessment methodology. Agric. Ecosyst. Environ. J. 88: 
55.Zhang, B., Zhang, Y., Chen, D., White, R.E., and Li, Y. 2004. A quantitative evaluation system of soil productivity for intensive agriculture in China. Geoderma, 123: 319-331.
مجله مدیریت خاک و تولید پایدار
دوره 9، شماره 3
آذر 1398
صفحه 1-23

فایل ها

هم رسانی

ارجاع به این مقاله

آمار