{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:27:10Z","timestamp":1773800830427,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,4,25]],"date-time":"2020-04-25T00:00:00Z","timestamp":1587772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AgriEngineering"],"abstract":"<jats:p>The estimation of pasture productivity is of great interest for the management of animal grazing. The standard method of assessing pasture mass requires great effort and expense to collect enough samples to accurately represent a pasture. This work presents the results of a long-term study to calibrate a Grassmaster II capacitance probe to estimate pasture productivity in two phases: (i) the calibration phase (2007\u20132018), which included measurements in 1411 sampling points in three parcels; and (ii) the validation phase (2019), which included measurements in 216 sampling points in eight parcels. A regression analysis was performed between the capacitance (CMR) measured by the probe and values of pasture green matter and dry matter (respectively, GM and DM, in kg ha\u22121). The results showed significant correlations between GM and CMR and between DM and CMR, especially in the early stages of pasture growth cycle. The analysis of the data grouped by classes of pasture moisture content (PMC) shows higher correlation coefficients for PMC content &gt;80% (r = 0.775; p &lt; 0.01; RMSE = 4806 kg ha\u22121 and CVRMSE = 28.1% for GM; r = 0.750; p &lt; 0.01; RMSE = 763 kg ha\u22121 and CVRMSE = 29.7% for DM), with a clear tendency for the accuracy to decrease when the pasture vegetative cycle advances and, consequently, the PMC decreases. The validation of calibration equations when PMC &gt; 80% showed a good approximation between GM or DM measured and GM or DM predicted (r = 0.959; p &lt; 0.01; RMSE = 3191 kg ha\u22121; CVRMSE = 23.6% for GM; r = 0.953; p &lt;0.01; RMSE = 647 kg ha\u22121 and CVRMSE = 27.3% for DM). It can be concluded that (i) the capacitance probe is an expedient tool that can enable the farm manager to estimate pasture productivity with acceptable accuracy and support the decision-making process in the management of dryland pastures; (ii) the more favorable period for the use of this probe in dryland pastures in a Mediterranean climate, such as the Portuguese Alentejo, coincides with the end of winter and beginning of spring (February\u2013March), corresponding to PMC &gt; 80%.<\/jats:p>","DOI":"10.3390\/agriengineering2020015","type":"journal-article","created":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T01:24:18Z","timestamp":1588123458000},"page":"240-255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Estimation of Productivity in Dryland Mediterranean Pastures: Long-Term Field Tests to Calibration and Validation of the Grassmaster II Probe"],"prefix":"10.3390","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5178-8158","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Serrano","sequence":"first","affiliation":[{"name":"MED-Mediterranean Institute for Agriculture, Environment and Development, Instituto de Investiga\u00e7\u00e3o e Forma\u00e7\u00e3o Avan\u00e7ada, Universidade de \u00c9vora, P\u00f3lo da Mitra, Ap. 94, 7006-554 \u00c9vora, Portugal"}]},{"given":"Shakib","family":"Shahidian","sequence":"additional","affiliation":[{"name":"MED-Mediterranean Institute for Agriculture, Environment and Development, Instituto de Investiga\u00e7\u00e3o e Forma\u00e7\u00e3o Avan\u00e7ada, Universidade de \u00c9vora, P\u00f3lo da Mitra, Ap. 94, 7006-554 \u00c9vora, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8465-1318","authenticated-orcid":false,"given":"Francisco","family":"Moral","sequence":"additional","affiliation":[{"name":"Departamento de Expresi\u00f3n Gr\u00e1fica, Escuela de Ingenier\u00edas Industriales, Universidad de Extremadura, Avenida de Elvas s\/n, 06006 Badajoz, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7791-0991","authenticated-orcid":false,"given":"Fernando","family":"Carvajal-Ramirez","sequence":"additional","affiliation":[{"name":"Department of Engineering, Mediterranean Research Center of Economics and Sustainable Development (CIMEDES), University of Almer\u00eda (Agrifood Campus of International Excellence, ceiA3), La Ca\u00f1ada de San Urbano, s\/n. 04120 Almer\u00eda, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0305-8147","authenticated-orcid":false,"given":"Jos\u00e9","family":"Marques da Silva","sequence":"additional","affiliation":[{"name":"MED-Mediterranean Institute for Agriculture, Environment and Development, Instituto de Investiga\u00e7\u00e3o e Forma\u00e7\u00e3o Avan\u00e7ada, Universidade de \u00c9vora, P\u00f3lo da Mitra, Ap. 94, 7006-554 \u00c9vora, Portugal"},{"name":"AgroInsider Lda. (spin-off da Universidade de \u00c9vora), PITE, R. Circular Norte, NERE, Sala 18, 7005-841 \u00c9vora, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Schaefer, M.T., and Lamb, D.W. (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.","DOI":"10.3390\/rs8020109"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Serrano, J., Shahidian, S., Da Silva, J.M., Paix\u00e3o, L., Carreira, E., Pereira, A., and Carvalho, M. (2020). Climate changes challenges to the management of Mediterranean montado ecosystem: Perspectives for use of Precision Agriculture technologies. 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