Towards Economic Land Evaluation at the Farm Scale Based on Soil Physical-Hydrological Features and Ecosystem Services
Abstract
:1. Introduction
- Planning units: large homogeneous areas in which management decisions or constraints are taken, according to regional or local objectives (for example to preserve water for irrigation, for nitrate pollution, etc.).
- Map units of natural resource inventories, delineating the homogeneous areas with respect to land natural features, like soil type, climate, morphological units, etc.
- Management units, delineating the smallest areas that the land manager intends to manage as homogeneous units, and that cannot be subdivided. A field or a parcel can be considered a management unit.
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- Soil spatial variability within the parcel;
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- Potential biomass production of the different soil typologies within the parcel;
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- Environmental added value of the different soil typologies, in particular for carbon sequestration, water regulation and purification, biodiversity and nutrient cycling.
2. Materials and Methods
2.1. Study Area
2.2. Proximal Sensing and Soil Mapping
2.3. Yield Data
- -
- Harvester turns at the end of a harvest pass and headlands in a non-square field, where it is almost impossible to avoid overlapping harvest strips;
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- Accuracy of the positioning system and eventual offset between the true location of a yield observation and the coordinates recorded by the GNSS;
2.4. Statistical Analysis
2.5. Economic Land Evaluation (Biomass Production, Carbon Sequestration and Flood Risk)
3. Results
3.1. High Detail Soil Mapping by Proximal Sensing
- -
- GIA (San Giacomo unit): brown soils on fluvial overflow deposits, characterized by an Ap-Bw-C horizon sequence, silty loam, without coarse fragments, slightly calcareous, poor in organic matter, classified as Calcaric Fluvic Cambisol (Siltic).
- -
- ISN (Isolone unit): pale brown soils on fluvial overflow deposits, characterized by an Ap-Bg-C horizon sequence, silty loam with a lower sand percentage than GIA, without coarse fragments, slightly calcareous, poor in organic matter, with moderate waterlogging of subsoil horizons (B or C), classified as Calcaric Endogleyic Cambisol (Siltic).
- -
- PCH1 (Porta Chiozza unit): pale brown soils on sandy alluvial lenses, characterized by an Ap-C horizon sequence, sandy loam (sometimes sandy in the subsoil) without coarse fragments, very poor in organic matter, classified as Calcaric Fluvisol (Arenic).
- -
- PES1 (Peschiera1 unit): dark brown soils on old fluvial terrace, characterized by an Ap-Bw-C horizon sequence, loamy sand with scarce coarse fragments, non-calcareous and sub-acid, moderately rich in organic matter, classified as Dystric Fluvic Cambisol (Arenic).
- -
- PES3 (Peschiera3 unit): brown soils on paleochannels of old fluvial terrace, characterized by Ap-C horizons sequence, sandy with scarce coarse fragments, neutral pH and moderate organic matter, classified as Eutric Fluvisol (Arenic).
3.2. Crop Yield Mapping and Standardized Yield Variation
3.3. Economic Evaluation
4. Discussion
5. Conclusions
- -
- Soil features and their associated functions can strongly vary within a land parcel and, in general, within a farm.
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- This soil spatial variability should be taken into account during economic land evaluation, because the natural capital of the soil and the economic return due to biomass production follow this variability.
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- The approach proposed in this paper can be applied in many other lands, specifically croplands in alluvial plains. With the awareness that most of the needed data, like the LMV, GSP, mean CS30, flood risk, etc., are usually available for developed countries.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- FAO. A Framework for Land Evaluation; Food and Agriculture Organization of the United Nations: Rome, Italy, 1976; Soil Bulletin 32; Available online: http://www.fao.org/3/x5310e/x5310e00.htm (accessed on 22 July 2019).
- Rossiter, D.G. Economic land evaluation: Why and how. Soil Use Manag. 1995, 11, 132–140. [Google Scholar] [CrossRef]
- Hoobler, B.M.; Vance, G.F.; Hamerlinck, J.D.; Munn, L.C.; Hayward, J.A. Applications of land evaluation and site assessment (LESA) and a geographic information system (GIS) in East Park County, Wyoming. J. Soil Water Conserv. 2003, 58, 105–112. [Google Scholar]
- De la Rosa, D.; Mayol, F.; Diaz-Pereira, E.; Fernandez, M.; de la Rosa, D., Jr. A land evaluation decision support system (MicroLEIS DSS) for agricultural soil protection: With special reference to the Mediterranean region. Environ. Model. Softw. 2004, 19, 929–942. [Google Scholar] [CrossRef]
- Manna, P.; Basile, A.; Bonfante, A.; De Mascellis, R.; Terribile, F. Comparative Land Evaluation approaches: An itinerary from FAO framework to simulation modelling. Geoderma 2009, 150, 367–378. [Google Scholar] [CrossRef]
- Baveye, P.C.; Baveye, J.; Gowdy, J. Soil “ecosystem” services and natural capital: Critical appraisal of research on uncertain ground. Front. Environ. Sci. 2016, 4, 41. [Google Scholar] [CrossRef]
- Halder, J.C. Land suitability assessment for crop cultivation by using remote sensing and GIS. J. Geogr. Geol. 2013, 5, 65–74. [Google Scholar] [CrossRef]
- Haines-Young, R.; Potschin, M. Common international classification of ecosystem services (CICES, Version 4.1). Eur. Environ. Agency 2012, 33, 107. [Google Scholar]
- Calzolari, C.; Ungaro, F.; Filippi, N.; Guermandi, M.; Malucelli, F.; Marchi, N.; Staffilani, F.; Tarocco, P. A methodological framework to assess the multiple contributions of soils to ecosystem services delivery at regional scale. Geoderma 2016, 261, 190–203. [Google Scholar] [CrossRef]
- Dominati, E.; Patterson, M.; Mackay, A. A framework for classifying and quantifying the natural capital and ecosystem services of soils. Ecol. Econ. 2010, 69, 1858–1868. [Google Scholar] [CrossRef]
- Earl, R.; Taylor, J.C.; Wood, G.A.; Bradley, I.; James, I.T.; Waine, T.; Welsh, J.P.; Godwin, R.J.; Knight, S.M. Soil factors and their influence on within-field crop variability, part I: Field observation of soil variation. Biosyst. Eng. 2003, 84, 425–440. [Google Scholar] [CrossRef]
- Casa, R.; Castrignanò, A. Analysis of spatial relationships between soil and crop variables in a durum wheat field using a multivariate geostatistical approach. Eur. J. Agron. 2008, 28, 331–342. [Google Scholar] [CrossRef] [Green Version]
- King, J.A.; Dampney, P.M.R.; Lark, R.M.; Wheeler, H.C.; Bradley, R.I.; Mayr, T.R. Mapping potential crop management zones within fields: Use of yield-map series and patterns of soil physical properties identified by electromagnetic induction sensing. Precis. Agric. 2005, 6, 167–181. [Google Scholar] [CrossRef]
- Martínez-Casasnovas, J.; Arnó, J. Use of farmer knowledge in the delineation of potential management zones in precision agriculture: A case study in maize (Zea mays L.). Agriculture 2018, 8, 84. [Google Scholar]
- Corwin, D.L.; Scudiero, E. Mapping Soil Spatial Variability with Apparent Soil Electrical Conductivity (ECa) Directed Soil Sampling. Soil Sci. Soc. Am. J. 2019, 83, 3–4. [Google Scholar] [CrossRef] [Green Version]
- Morari, F.; Castrignanò, A.; Pagliarin, C. Application of multivariate geostatistics in delineating management zones within a gravelly vineyard using geo-electrical sensors. Comput. Electron. Agric. 2009, 68, 97–107. [Google Scholar] [CrossRef]
- Priori, S.; Fantappiè, M.; Magini, S.; Costantini, E.A.C. Using the ARP-03 for high-resolution mapping of calcic horizons. Int. Agrophys. 2013, 27, 313–321. [Google Scholar] [CrossRef] [Green Version]
- André, F.; van Leeuwen, C.; Saussez, S.; Van Durmen, R.; Bogaert, P.; Moghadas, D.; Rességuier, L.; Delvaux, B.; Vereecken, H.; Lambot, S. High-resolution imaging of a vineyard in south of France using ground penetrating radar, electromagnetic induction and electrical resistivity tomography. J. Appl. Geophys. 2012, 78, 113–122. [Google Scholar] [CrossRef]
- Ortuani, B.; Chiaradia, E.A.; Priori, S.; L’Abate, G.; Canone, D.; Comunian, A.; Giudici, M.; Mele, M.; Facchi, A. Mapping Soil Water Capacity Through EMI Survey to Delineate Site-Specific Management Units Within an Irrigated Field. Soil Sci. 2016, 181, 252–263. [Google Scholar] [CrossRef]
- Hedley, C.B.; Yule, I.J.; Eastwood, C.R.; Shepherd, T.G.; Arnold, G. Rapid identification of soil textural and management zones using electromagnetic induction sensing of soils. Soil Res. 2004, 42, 389–400. [Google Scholar] [CrossRef]
- Priori, S.; Bianconi, N.; Costantini, E.A. Can γ-radiometrics predict soil textural data and stoniness in different parent materials? A comparison of two machine-learning methods. Geoderma 2014, 226, 354–364. [Google Scholar] [CrossRef]
- Kovačević, M.; Bajat, B.; Gajić, B. Soil type classification and estimation of soil properties using support vector machines. Geoderma 2010, 154, 340–347. [Google Scholar] [CrossRef]
- Zhao, Z.; Chow, T.L.; Rees, H.W.; Yang, Q.; Xing, Z.; Meng, F. Predict soil texture distributions using an artificial neural network model. Comput Electron. Agric. 2009, 65, 36–48. [Google Scholar] [CrossRef]
- Brady, M.V.; Hedlund, K.; Cong, R.G.; Hemerik, L.; Hotes, S.; Machado, S.; Mattsson, L.; Schulz, E.; Thomsen, I.K. Valuing supporting soil ecosystem services in agriculture: A natural capital approach. Agron. J. 2015, 107, 1809–1821. [Google Scholar] [CrossRef]
- Robinson, D.A.; Fraser, I.; Dominati, E.J.; Davidsdottir, B.; Jonsson, J.O.G.; Jones, L.; Jones, S.B.; Tuller, M.; Lebron, I.; Bristow, K.L.; et al. On the value of soil resources in the context of natural capital and ecosystem service delivery. Soil Sci. Soc. Am. J. 2014, 78, 685–700. [Google Scholar] [CrossRef]
- Stern, N. Stern Review: The Economics of Climate Change. 2006. Available online: http://mudancasclimaticas.cptec.inpe.br/~rmclima/pdfs/destaques/sternreview_report_complete.pdf (accessed on 17 July 2019).
- Wander, M.; Nissen, T. Value of soil organic carbon in agricultural lands. Mitig. Adapt. Strat. Gl. 2004, 9, 417–431. [Google Scholar] [CrossRef]
- European Commission (EC). 2019 EU Emissions Trading System (EU ETS). Available online: https://ec.europa.eu/clima/policies/ets_en (accessed on 17 July 2019).
- Sandbag. Carbon Price Viewer, Sandbag Climate Campaign CIC, London. 2019. Available online: https://sandbag.org.uk/carbon-price-viewer/ (accessed on 17 July 2019).
- Amadio, M.; Mysiak, J.; Carrera, L.; Koks, E. Improving flood damage assessment models in Italy. Nat. Hazards 2016, 82, 2075–2088. [Google Scholar] [CrossRef] [Green Version]
- Carrera, L.; Standardi, G.; Bosello, F.; Mysiak, J. Assessing direct and indirect economic impacts of a flood event through the integration of spatial and computable general equilibrium modelling. Environ. Model. Softw 2015, 63, 109–122. [Google Scholar] [CrossRef] [Green Version]
- FAO. Guidelines for Soil Description, 4th ed.; FAO: Rome, Italy, 2006; 97p. [Google Scholar]
- Ministero per le Politiche Agricole. Metodi ufficiali di analisi fisica del suolo. Off. Ital. Gazzette GU 1997. Available online: http://www.gazzettaufficiale.it/eli/id/1997/09/02/097A6592/sg (accessed on 22 July 2019).
- Ministero per le Politiche Agricole. Metodi ufficiali di analisi chimica del suolo. Off. Ital. Gazzette GU 1999. Available online: http://www.gazzettaufficiale.it/eli/id/1999/10/21/099A8497/sg (accessed on 22 July 2019).
- Indorante, S.J.; Follmer, L.R.; Hammer, R.D.; Koenig, P.G. Particle-size analysis by a modified pipette procedure. Soil Sci. Soc. Am. J. 1990, 542, 560–563. [Google Scholar] [CrossRef]
- IPCC—Intergovernmental Panel on Climate Change. Good Practice Guidance for Land Use, Land Use Change and Forestry; Penman, J., Gytarsky, M., Hiraishi, T., Krug, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T., Tanabe, K., Eds.; IPCC/OECD/IEA/IGES: Hayama, Japan, 2003. [Google Scholar]
- Saxton, K.E.; Rawls, W.J. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Am. J. 2006, 705, 1569–1578. [Google Scholar] [CrossRef]
- IUSS Working Group WRB. World Reference Bases for Soil Resources; World Soil Resources Reports; FAO: Rome, Italy, 2014. [Google Scholar]
- ERSAF, Regione Lombardia. Banca dati suoli Losan. 2008. Available online: https://losan.ersaflombardia.it/ (accessed on 17 July 2019).
- Chang, C.C.; Lin, C.J. A library for support vector machines. ACM Trans. Intel. Syst. Technol. 2011, 2/3, 1–27. Available online: www.csie.ntu.edu.tw/~cjlin/libsvm (accessed on 17 July 2019).
- Leroux, C.; Jones, H.; Clenet, A.; Dreux, B.; Becu, M.; Tisseyre, B. A general method to filter out defective spatial observations from yield mapping datasets. Precis. Agric. 2018, 19, 789–808. [Google Scholar] [CrossRef]
- BURL n.6, 07 February 2018, Valori Agricoli Medi della Provincia di Pavia. Available online: https://www.agenziaentrate.gov.it/wps/content/Nsilib/Nsi/Schede/FabbricatiTerreni/omi/Banche+dati/Valori+agricoli+medi/Valori+agricoli+medi+Lombardia/?page=schedefabbricatieterreni (accessed on 17 July 2019).
- Rete di Informazione Contabile Agraria (RICA). 2019. Available online: http://rica.crea.gov.it (accessed on 17 July 2019).
- FAO; ITPS. Global Soil Organic Carbon Map—GSOCmap; Version 1.2.0; FAO: Rome, Italy, 2018; Available online: http://54.229.242.119/GSOCmap/ (accessed on 17 July 2019).
- Unione Nazionale Contoterzisti Agromeccanici e Industriali. Tariffario Provincia di Pavia. 2018. Available online: www.contoterzisti.it/tariffari.php (accessed on 17 July 2019).
- Heggemann, T.; Welp, G.; Amelung, W.; Angst, G.; Franz, S.O.; Koszinski, S.; Karsten, S.; Pätzold, S. Proximal gamma-ray spectrometry for site-independent in situ prediction of soil texture on ten heterogeneous fields in Germany using support vector machines. Soil Tillage Res. 2017, 168, 99–109. [Google Scholar] [CrossRef]
- Kuang, B.; Mouazen, A.M. Calibration of visible and near infrared spectroscopy for soil analysis at the field scale on three European farms. Eur. J. Soil Sci. 2011, 62, 629–636. [Google Scholar] [CrossRef]
- Priori, S.; Fantappiè, M.; Bianconi, N.; Ferrigno, G.; Pellegrini, S.; Costantini, E.A.C. Field-scale mapping of soil carbon stock with limited sampling by coupling gamma-ray and vis-NIR spectroscopy. Soil Sci. Soc. Am. J. 2016, 80, 954–964. [Google Scholar] [CrossRef]
- Wetterlind, J.; Stenberg, B. Near-infrared spectroscopy for within-field soil characterization: Small local calibrations compared with national libraries spiked with local samples. Eur. J. Soil Sci. 2010, 61, 823–843. [Google Scholar] [CrossRef]
- Castaldi, F.; Palombo, A.; Santini, F.; Pascucci, S.; Pignatti, S.; Casa, R. Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon. Remote Sens. Environ. 2016, 179, 54–65. [Google Scholar] [CrossRef]
- Mulder, V.L.; De Bruin, S.; Schaepman, M.E.; Mayr, T.R. The use of remote sensing in soil and terrain mapping—A review. Geoderma 2011, 162, 1–19. [Google Scholar] [CrossRef]
- Studer, C.; Hu, Y.; Schmidhalter, U. Interactive effects of N-, P-and K-nutrition and drought stress on the development of maize seedlings. Agriculture 2017, 7, 90. [Google Scholar] [CrossRef]
- Kumar, S.; Kadono, A.; Lal, R.; Dick, W. Long-term no-till impacts on organic carbon and properties of two contrasting soils and corn yields in Ohio. Soil Sci. Soc. Am. J. 2012, 76, 1798–1809. [Google Scholar] [CrossRef]
- Celik, I.; Ortas, I.; Kilic, S. Effects of compost, mycorrhiza, manure and fertilizer on some physical properties of a Chromoxerert soil. Soil Tillage Res. 2004, 78, 59–67. [Google Scholar] [CrossRef]
- Bescansa, P.; Imaz, M.J.; Virto, I.; Enrique, A.; Hoogmoed, W.B. Soil water retention as affected by tillage and residue management in semiarid Spain. Soil Tillage Res. 2006, 87, 19–27. [Google Scholar] [CrossRef]
- Andrenelli, M.C.; Maienza, A.; Genesio, L.; Miglietta, F.; Pellegrini, S.; Vaccari, F.P.; Vignozzi, N. Field application of pelletized biochar: Short term effect on the hydrological properties of a silty clay loam soil. Agric. Water Manag. 2016, 163, 190–196. [Google Scholar] [CrossRef]
- Loveland, P.; Webb, J. Is there a critical level of organic matter in the agricultural soils of temperate regions: A review. Soil Tillage Res. 2003, 70, 1–18. [Google Scholar] [CrossRef]
- Perego, A.; Basile, A.; Bonfante, A.; De Mascellis, R.; Terribile, F.; Brenna, S.; Acutis, M. Nitrate leaching under maize cropping systems in Po Valley (Italy). Agric. Ecosyst. Environ. 2012, 147, 57–65. [Google Scholar] [CrossRef]
- Simmelsgaard, S.E. The effect of crop, N-level, soil type and drainage on nitrate leaching from Danish soil. Soil Use Manag. 1998, 14, 30–36. [Google Scholar] [CrossRef]
- Beaudoin, N.; Saad, J.K.; Van Laethem, C.; Machet, J.M.; Maucorps, J.; Mary, B. Nitrate leaching in intensive agriculture in Northern France: Effect of farming practices, soils and crop rotations. Agric Ecosyst. Environ. 2005, 111, 292–310. [Google Scholar] [CrossRef]
- Obayomi, O.; Bernstein, N.; Edelstein, M.; Vonshak, A.; Ghazayarn, L.; Ben-Hur, M.; Tebbe, C.C.; Gillor, O. Importance of soil texture to the fate of pathogens introduced by irrigation with treated wastewater. Sci. Total Environ. 2019, 653, 886–896. [Google Scholar] [CrossRef] [PubMed]
- Makó, A.; Kocsis, M.; Barna, G.Y.; Tóth, G. Mapping the Storing and Filtering Capacity of European Soils; EUR 28392; Publications Office of the European Union: Luxembourg, 2017; p. 54. [Google Scholar] [CrossRef]
- Giasson, E.; Bryant, R.B.; DeGloria, S.D. GIS-based spatial indices for identification of potential phosphorous export at watershed scale. J. Soil Water Conserv. 2002, 57, 373–380. [Google Scholar]
hor. | Depth | Sand | Silt | Clay | CF | pH | CaCO3 | SOC | TN | CEC | TBS | AWC | ER1 | ER2 | ER3 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
(cm) | (%) | (g kg−1) | (meq∙cm−1) | (%) | (cm3∙cm−3) | (Ω∙m−1) | ||||||||||
PES1 (n = 6) | Ap | 41 | 74 | 20 | 6 | 5 | 6.8 | 0.0 | 17.1 | 1.89 | 11.9 | 50 | 0.09 | 216 | 200 | 332 |
C | 74 | 70 | 27 | 3 | 7 | - | - | - | - | - | - | |||||
PES3 (n = 3) | Ap | 40 | 89 | 7 | 4 | 3 | 7 | 11 | 11.6 | 1.4 | 8.4 | 52 | 0.06 | 366 | 380 | 939 |
C | 100 | 78 | 21 | 1 | 4 | - | - | - | - | - | - | |||||
GIA (n = 9) | Ap | 39 | 36 | 50 | 14 | 0 | 8.3 | 12 | 7.0 | 1.18 | 14.4 | 82 | 0.16 | 94 | 87 | 96 |
Bw | 88 | 58 | 36 | 6 | 0 | - | - | - | - | - | - | |||||
ISN (n = 10) | Ap | 40 | 7 | 70 | 23 | 0 | 8.3 | 31 | 9.1 | 1.45 | 16.9 | 89 | 0.20 | 86 | 82 | 76 |
Bw | 91 | 10 | 72 | 18 | 0 | - | - | - | - | - | - | |||||
PCH1 (n = 8) | Ap | 39 | 58 | 35 | 7 | 0 | 8.4 | 7.0 | 5.7 | 0.91 | 11.3 | 71 | 0.11 | 206 | 208 | 227 |
Bw | 97 | 71 | 25 | 4 | 0 | - | - | - | - | - | - |
Year | Surface | Mean | SD 1 | RMSE 2 | Regional Data 3 | |
---|---|---|---|---|---|---|
ha | Mg∙ha−1 | |||||
Silage Maize | 2016 | 93.2 | 62.3 | 7.7 | 6.1 | 55.8 |
2017 | 80.5 | 52.9 | 10.1 | 8.3 | 50.4 | |
2018 | 86.3 | 63.4 | 11.2 | 9.3 | n.a. | |
Soybean | 2016 | 12.8 | 3.0 | 0.6 | 0.4 | 4.1 |
2018 | 79.8 | 3.6 | 0.7 | 0.5 | n.a. | |
Sorghum | 2018 | 4.0 | 55.0 | 11.8 | 9.4 | n.a. |
ΔY | h | ER1 | ER2 | ER3 | |
---|---|---|---|---|---|
h | −0.33 | ||||
ER1 | −0.59 | 0.66 | |||
ER2 | −0.61 | 0.66 | 0.89 | ||
ER3 | −0.62 | 0.75 | 0.88 | 0.93 | |
AWC | 0.60 | −0.67 | −0.72 | −0.74 | −0.72 |
CF | −0.39 | 0.58 | 0.36 | 0.39 | 0.44 |
Sand | −0.53 | 0.70 | 0.74 | 0.76 | 0.73 |
Silt | 0.56 | −0.71 | −0.72 | −0.74 | −0.73 |
Clay | 0.33 | −0.57 | −0.68 | −0.68 | −0.63 |
pH | 0.35 | −0.82 | −0.46 | −0.53 | −0.60 |
SOC | −0.06 | 0.71 | 0.15 | 0.25 | 0.32 |
TN | 0.03 | 0.55 | −0.01 | 0.05 | 0.16 |
CaCO3 | 0.22 | −0.45 | −0.43 | −0.43 | −0.39 |
CEC | 0.51 | −0.46 | −0.72 | −0.74 | −0.66 |
TBS | 0.42 | −0.74 | −0.62 | −0.65 | −0.64 |
P | −0.17 | 0.76 | 0.49 | 0.49 | 0.51 |
STU | ΔY(1) | ΔCS(2) | ΔGSP(3) | ΔYv(4) | ΔCSv(5) | FRc(6) |
---|---|---|---|---|---|---|
(%) | (Mg∙ha−1) | (€∙ha−1) | ||||
PES1 | −6.5 | 19.9 | −105 | −2390 | 1162 | −1813 |
PES3 | −19.8 | −1.8 | −320 | −7281 | −102 | −1598 |
PCH1 | −5.2 | −23.6 | −84 | −1912 | −1376 | −1834 |
GIA | 4.1 | −17.6 | 66 | 1508 | −1026 | −1984 |
ISN | 11.0 | −11.2 | 178 | 4038 | −653 | −2096 |
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Priori, S.; Barbetti, R.; Meini, L.; Morelli, A.; Zampolli, A.; D’Avino, L. Towards Economic Land Evaluation at the Farm Scale Based on Soil Physical-Hydrological Features and Ecosystem Services. Water 2019, 11, 1527. https://doi.org/10.3390/w11081527
Priori S, Barbetti R, Meini L, Morelli A, Zampolli A, D’Avino L. Towards Economic Land Evaluation at the Farm Scale Based on Soil Physical-Hydrological Features and Ecosystem Services. Water. 2019; 11(8):1527. https://doi.org/10.3390/w11081527
Chicago/Turabian StylePriori, Simone, Roberto Barbetti, Luca Meini, Annalisa Morelli, Andrea Zampolli, and Lorenzo D’Avino. 2019. "Towards Economic Land Evaluation at the Farm Scale Based on Soil Physical-Hydrological Features and Ecosystem Services" Water 11, no. 8: 1527. https://doi.org/10.3390/w11081527