Montado is a silvo-pastoral ecosystem of the Mediterranean region, a mixed system of trees and grass, where livestock graze. The information about the spatial and temporal variability of pastures constitutes the basis to estimate available feed, a fundamental decision support tool for the farm manager to define the animal stocking or the rotation of the grazed paddocks. In this study, the intrinsic features of high spatial–temporal variability of Mediterranean grazed pastures were assessed with the objective of evaluating the suitability of two proximal sensing techniques (an active optical sensor, AOS and a capacitance probe) for easily monitoring seasonal variability of pasture productivity and quality linked to animal grazing patterns. The correlation between pasture and sensor parameters was consistent between capacitance and pasture productivity (r2 = 0.68, P < 0.01; and r2 = 0.87, P < 0.01, respectively for green pasture biomass, PB and pasture moisture content, PMC), between NDVI and pasture productivity (r2 = 0.73, P < 0.01; and r2 = 0.96, P < 0.01, respectively for PB and PMC) and between NDVI and pasture quality (r2 = 0.44, P < 0.05; r2 = 0.69, P < 0.01; and r2 = 0.78, P < 0.01, respectively for ash, crude protein, CP and neutral detergent fibre, NDF). The approach is a promising methodology for assessing seasonal changes in pasture that have values of biomass that range between 2000 and 85,000 kg ha−1 and vegetative sates from de green and leafy to dry. These results can be an important starting point for studies of evaluation and calibration of the optical sensor specifically for pasture quality assessment in different types of biodiverse pastures. This is a key factor for the management of animal grazing intensity and calculation of feed supplementation needs.
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Albayrak S (2008) Use of reflectance measurements for the detection of N, P, K, ADF and NDF contents in Sainfoin pasture. Sensors 14(8):7275–7286
Almeida M, Azeda C, Guiomar N, Pinto-Correia T (2016) The effects of grazing management in montado fragmentation and heterogeneity. Agrofor Syst 90(1):69–85
AOAC Official Method (2005) Official methods of analysis of AOAC International, 18th edn. AOAC International, Arlington
Bilotta GS, Brazier RE, Haygarth PM (2007) The impacts of grazing animals on the quality of soils, vegetation, and surface waters in intensively managed grasslands. Adv Agron 94:237–280
Brown JS (1988) Patch use as an indicator of habitat preference, predation risk, and competition. Behav Ecol Sociobiol 22(1):37–47
Cicore P, Serrano J, Shahidian S, Sousa A, Costa JL, Marques da Silva J (2016) Assessment of the spatial variability in tall wheatgrass forage using LANDSAT 8 satellite imagery to delineate potential management zones. Environ Monit Assess 188:1–11
David TS, Pinto CA, Nadezhdina N, Kurz-Besson C, Henriques MO, Quilhó T, Cermak J, Chaves MM, Pereira JS, David JS (2013) Root functioning, tree water use and hydraulic redistribution in Quercus suber trees: a modeling approach based on root sap flow. For Ecol Manag 307:136–146
de Oliveira MIF, Lamy E, Bugalho MN, Vaz M, Pinheiro C, d’Abreu MC, e Silva FC, Sales-Baptista E (2013) Assessing foraging strategies of herbivores in Mediterranean oak woodlands: a review of key issues and selected methodologies. Agrofor Syst 87(6):1421–1437
de Oliveira MIF, Azeda C, Pinto-Correia T (2016) Management of Montados and Dehesas for high nature value: an interdisciplinary pathway. Agrofor Syst 90(1):1–6
Demanet R, Mora ML, Herrera MA, Miranda H, Barea JM (2015) Seasonal variation of the productivity and quality of permanent pastures in Adisols of temperate regions. J Soil Sci Plant Nutr 15(1):111–128
Edirisinghe A, Hill MJ, Donald GE, Hyder M (2011) Quantitative mapping of pasture biomass using satellite imagery. Int J Remote Sens 32(10):2699–2724
FAO (2006) World reference base for soil resources. Food and Agriculture Organization of the United Nations, World Soil Resources Reports N 103, Rome
Flynn ES, Dougherty CT, Wendroth O (2008) Assessment of pasture biomass with the normalized difference vegetation index from active ground-based sensors. Agron J 100(1):114–121
Freeman KW, Girma K, Arnall DB, Mullen RW, Martin KL, Teal RK, Raun WR (2007) By-plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height. Agron J 99:530–536
Gitelson AA (2004) Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J Plant Physiol 161(2):165–173
Handcock RN, Gobbett DL, González LA, Bishop-Hurley GJ, McGavin SL (2016) A pilot project combining multispectral proximal sensors and digital cameras for monitoring tropical pastures. Biogeosciences 13:4673–4695
Hanna M, Steyn-Ross D, Steyn-Ross M (1999) Estimating biomass for New Zealand pasture using optical remote sensing techniques. Geocarto Int 14:89–94
Harmoney KR, Moore KJ, George JR, Brummer EC, Russell JR (1997) Determination of pasture biomass using four indirect methods. Agron J 89(4):665–672
Jamieson PD, Porter JR, Wilson DR (1991) A test of computer simulation model ARC-WHEAT1 on wheat crops grown in New Zealand. Field Crop Res 27:337–350
Krebs JR, Ryan JC, Charnov EL (1974) Hunting by expectation or optimal foraging? A study of patch use by chickadees. Anim Behav 22:953
Louhaichi M, Hassan S, Clifton K, Johnson DE (2017) A reliable and non-destructive method for estimating forage shrub cover and biomass in arid environments using digital vegetation charting technique. Agrofor Syst. https://doi.org/10.1007/s10457-017-0079-4
National Research Council (1985) Nutrient requirements of sheep, vol 5, 6th edn. National Academy Press, Washington DC
Peco B, Sánchez AM, Azcárate FM (2006) Abandonment in grazing systems: consequences for vegetation and soil. Agric Ecosyst Environ 113(1):284–294
Pinchak WE, Smith MA, Hart RH, Waggoner JW (1991) Beef cattle distribution patterns on foothill range. J Range Manag 44(3):267–275
Pinto-Correia T, Godinho S (2013) Chapter 4 Changing agriculture changing landscapes: what is going on in the high valued Montado. In: Agriculture in Mediterranean Europe: between old and new paradigms. Emerald Group Publishing Limited, p 75–90
Pinto-Correia T, Ribeiro N, Sá-Sousa P (2011) Introducing the montado, the cork and holm oak agroforestry system of Southern Portugal. Agrofor Syst 82(2):99
Pullanagari RR, Yule IJ, Tuohy MP, Hedley MJ, Dynes RA, King WM (2013) Proximal sensing of the seasonal variability of pasture nutritive value using multispectral radiometry. Grass Forage Sci 68:110–119
Rascher U, Pieruschka R (2008) Spatio-temporal variations of photosynthesis: the potential of optical remote sensing to better understand and scale light use efficiency and stresses of plant ecosystems. Precis Agric 9(6):355–366
Reddy AR, Chaitanya KV, Vivekanandan M (2004) Drought-induced responses of photosynthesis and antioxidant metabolism in higher plants. J Plant Physiol 161(11):1189–1202
Ren QC, Jin X, Zhang ZH, Yang HJ, Li SL (2015) Effects of dietary neutral detergent fibre to protein ratio on duodenal microbial nitrogen flow and nitrogen losses in lactating cows fed high-concentrate total mixed rations with different forage combinations. J Agric Sci 153(04):753–764
Sales-Baptista E, d’Abreu MC, de Oliveira MIF (2016a) Overgrazing in the Montado? The need for monitoring grazing pressure at paddock scale. Agrofor Syst 90(1):57–68
Sales-Baptista E, de Oliveira IF, Santos MB, de Castro JAL, Pereira A, Rafael J, Serrano J (2016b) Tecnologia GNSS de baixo custo na monitorização de ovinos em pastoreio. Revista de Ciências Agrárias 39(2):251–260 (in portuguese)
Schaefer MT, Lamb DW (2016) A combination of plant NDVI and LiDAR measurements improve the estimation of pasture biomass in Tall Fescue (Festuca arundinacea var. Fletcher). Remote Sens 8(109):1–10
Serrano J, Peça J, Silva JM, da Shahidian S (2011) Calibration of a capacitance probe for measurement and mapping of dry matter yield in Mediterranean pastures. Precis Agric 12:860–875
Serrano J, Shahidian S, Silva JM (2016a) Calibration of Grassmaster II to estimate green and dry matter yield in Mediterranean pastures: effects of pasture moisture content. Crop Pasture Sci 67:780–791
Serrano J, Shahidian S, Silva JM (2016b) Monitoring pasture variability: optical OptRx® crop sensor versus Grassmaster II capacitance probe. Environ Monit Assess 188(2):1–17
Tomkins N, O’Reagain P (2007) Global positioning systems indicate landscape preferences of cattle in the subtropical savannas. Rangel J 29(2):217–222
Trotter MG, Lamb DW, Donald GE, Schneider DA (2010) Evaluating an active optical sensor for quantifying and mapping green herbage mass and growth in a perennial grass pasture. Crop Pasture Sci 61:389–398
Turner LW, Udal MC, Larson BT, Shearer SA (2000) Monitoring cattle behavior and pasture use with GPS and GIS. Can J Anim Sci 80(3):405–413
Zhao D, Starks PJ, Brown MA, Phillips WA, Coleman SW (2007) Assessment of forage biomass and quality parameters of bermudagrass using proximal sensing of pasture canopy reflectance. Grassl Sci 53(1):39–49
This work was funded by National Funds through FCT (Foundation for Science and Technology) under the Project UID/AGR/00115/2013).
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Serrano, J., Sales-Baptista, E., Shahidian, S. et al. Proximal sensors for monitoring seasonal changes of feeding sites selected by grazing ewes. Agroforest Syst 95, 55–69 (2021). https://doi.org/10.1007/s10457-018-0219-5
- Agro-silvo-pastoral systems
- Proximal sensing
- Pasture seasonality