| publications-2141 |
Peer reviewed articles |
2022 |
Noreika, N; Li, TL; Winterova, J; Krasa, J; Dostal, T |
The Effects of Agricultural Conservation Practices on the Small Water Cycle: From the Farm- to the Management-Scale |
Land |
10.3390/land11050683 |
Simulation & Modeling |
Irrigation Systems |
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Reinforcing the small water cycle is considered to be a holistic approach to both water resource and landscape management. In an agricultural landscape, this can be accomplished by incorporating agricultural conservation practices; their incorporation can reduce surface runoff, increase infiltration, and increase the water holding capacity of a soil. Some typical agricultural conservation practices include: conservation tillage, contour farming, residue incorporation, and reducing field sizes; these efforts aim to keep both water and soil in the landscape. The incorporation of such practices has been extensively studied over the last 40 years. The Soil and Water Assessment Tool (SWAT) was used to model two basins in the Czech Republic (one at the farm-scale and a second at the management-scale) to determine the effects of agriculture conservation practice adoption at each scale. We found that at the farm-scale, contour farming was the most effective practice at reinforcing the small water cycle, followed by residue incorporation. At the management-scale, we found that the widespread incorporation of agricultural conservation practices significantly reinforced the small water cycle, but the relative scale and spatial distribution of their incorporation were not reflected in the SWAT scenario analysis. Individual farmers should be incentivized to adopt agricultural conservation practices, as these practices can have great effects at the farm-scale. At the management-scale, the spatial distribution of agricultural conservation practice adoption was not significant in this study, implying that managers should incentivize any adoption of such practices and that the small water cycle would be reinforced regardless. |
773903 |
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| publications-2142 |
Peer reviewed articles |
2022 |
Liebhard, G; Klik, A; Stumpp, C; Morales Santos, AG; Eitzinger, J; Nolz, R |
Estimation of evaporation and transpiration rates under varying water availability for improving crop management of soybeans uusing oxygen isotope. G |
International Agrophysics |
10.31545/intagr/150811 |
AI & Machine Learning |
Precipitation & Ecological Systems |
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No abstract available |
773903 |
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| publications-2143 |
Peer reviewed articles |
2020 |
José A. Gómez, Alon Ben-Gal, Juan J. Alarcón, Gabrielle De Lannoy, Shannon de Roos, Tomáš Dostál, Elias Fereres, Diego S. Intrigliolo, Josef Krása, Andreas Klik, Gunther Liebhard, Reinhard Nolz, Aviva Peeters, Elke Plaas, John N. Quinton, Rui Miao, Peter Strauss, Weifeng Xu, Zhiqiang Zhang, Funing Zhong, David Zumr, Ian C. Dodd |
SHui, an EU-Chinese cooperative project to optimize soil and water management in agricultural areas in the XXI century |
International Soil and Water Conservation Research |
10.1016/j.iswcr.2020.01.001 |
AI & Machine Learning |
Precipitation & Ecological Systems |
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No abstract available |
773903 |
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| publications-2144 |
Peer reviewed articles |
2020 |
Thomas Weninger, Edith Kamptner, Tomas Dostal, Adelheid Spiegel, Peter Strauss |
Detection of physical hazards in soil profiles using quantitative soil physical quality assessment in the Pannonian basin, Eastern Austria |
International Agrophysics |
10.31545/intagr/130450 |
Simulation & Modeling |
Precipitation & Ecological Systems |
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No abstract available |
773903 |
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| publications-2145 |
Peer reviewed articles |
2019 |
Noa Ohana-Levi, Idan Bahat, Aviva Peeters, Alexandra Shtein, Yishai Netzer, Yafit Cohen, Alon Ben-Gal |
A weighted multivariate spatial clustering model to determine irrigation management zones |
Computers and Electronics in Agriculture |
10.1016/j.compag.2019.05.012 |
Data Management & Analytics |
Irrigation Systems |
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No abstract available |
773903 |
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| publications-2146 |
Peer reviewed articles |
2022 |
Guzman, G; Boumahdi, A; Gomez, JA |
Expansion of olive orchards and their impact on the cultivation and landscape through a case study in the countryside of Cordoba (Spain) |
Land Use Policy |
10.1016/j.landusepol.2022.106065 |
AI & Machine Learning |
Uncategorized |
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No abstract available |
773903 |
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| publications-2147 |
Peer reviewed articles |
2022 |
Jeřábek, J.; Zumr, D.; Laburda, T.; Krása, J.; Dostál, T. |
Soil surface connectivity of tilled soil with wheel tracks and its development under simulated rainfall |
Journal of Hydrology |
10.1016/j.jhydrol.2022.128322 |
AI & Machine Learning |
Uncategorized |
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No abstract available |
773903 |
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| publications-2148 |
Peer reviewed articles |
2020 |
Tomás R. Tenreiro, Margarita GarcÃa-Vila, José A. Gómez, José A. Jimenez-Berni, ElÃas Fereres |
Water modelling approaches and opportunities to simulate spatial water variations at crop field level |
Agricultural Water Management |
10.1016/j.agwat.2020.106254 |
Hydrological modeling |
River Basins |
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No abstract available |
773903 |
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| publications-2149 |
Peer reviewed articles |
2022 |
Katz, L; Ben-Gal, A; Litaor, MI; Naor, A; Peres, M; Bahat, I; Netzer, Y; Peeters, A; Alchanatis, V; Cohen, Y |
Spatiotemporal normalized ratio methodology to evaluate the impact of field-scale variable rate application |
Precision Agriculture |
10.1007/s11119-022-09877-4 |
Data Management & Analytics |
River Basins |
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No abstract available |
773903 |
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| publications-2150 |
Peer reviewed articles |
2022 |
Gomez, JA; Reyna-Bowen, L; Rebollo, PF; Soriano, MA |
Comparison of Soil Organic Carbon Stocks Evolution in Two Olive Orchards with Different Planting Systems in Southern Spain |
Agriculture |
10.3390/agriculture12030432 |
AI & Machine Learning |
River Basins |
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This study presents an evaluation of soil organic carbon (SOC) and stock (SOCstock) for the whole rooting depth (60 cm), spaced 55 months in two adjacent olive orchards with similar conditions but different tree densities: (i) intensive, planted in 1996 at 310 tree ha−1; (ii) superintensive, planted in 2000 at 1850 tree ha−1. This was carried out to test the hypothesis that olive orchards at different plant densities will have different rates of accumulation of SOC in the whole soil rooting depth. SOC increased significantly in the superintensive orchard during the 55-month period, from 1.1 to 1.6% in the lane area, and from 1.2 to 1.7% in the tree area (average 0–60 cm), with a significant increase in SOCstock from 4.7 to 6.1 kg m−2. In the intensive orchard, there was not a significant increase in SOCstock in 0–60 cm, average of 4.06 and 4.16 kg m−2 in 2013 and 2018, respectively. Results indicate a potential for a significant increase in SOC and SOCstock in olive orchards at higher tree densities when combined with temporary cover crops and mulch of chopped pruning residues. The increase is associated with an increase in SOC, mainly at a 0–15 cm depth. Results also point to the need for improve our monitoring capabilities to detect moderate increases in SOC. |
773903 |
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