Opportunities for expanding the use of wastewaters for irrigation of olives
Received 29 April 2020, Revised 9 June 2020, Accepted 11 June 2020, Available online 23 June 2020.
Water modelling approaches and opportunities to simulate spatial water variations at crop field level
Agricultural Water Management 2020, 240, 106254; https://doi.org/10.1016/j.agwat.2020.106254
Partner Publication (CSIC & UCO):
Tomás R. Tenreiro a,*, Margarita García-Vila b, José A. Gómez a, José A. Jimenez-Berni a, Elías Fereres a,b
• Scaling up point-based simulation modelling is a challenge due to the heterogeneity of water-related processes, and it is essential for many applications in precision agriculture.
• Seven crop simulation models and five hydrologic models were selected and their water modelling approaches were systematically reviewed for comparison. Regarding spatial modelling of water at crop field level, our analysis indicates that there is scope for conceptual improvements, but that combining both types of models may not be the best way forward.
• The most promising advances are related to the incorporation of surface inflow and subsurface lateral flows, by using differential equations or through novel water spatial partitioning relations to use in discrete-type approaches.
Effect of using pruning waste as an organic mulching on a drip-irrigated vineyard evapotranspiration under a semi-arid climate
Received 18 December 2019, Revised 25 May 2020, Accepted 31 May 2020, Available online 16 June 2020.
Alternation of wet and dry sides during partial rootzone drying irrigation enhances leaf ethylene evolution
Variation of soil organic carbon, stable isotopes and soil quality indicators across an eroding-deposition catena in an historical Spanish olive orchard
SOIL Discuss., 2019; https://doi.org/10.5194/soil-2019-59
Partner Publication (IAS – CSIC):
José A. Gómez1, Gema Guzmán2, Arsenio Toloza3, Christian Resch3, Roberto García-Ruíz4, and Lionel Mabit3
1Institute for Sustainable Agriculture-CSIC, Córdoba, Spain
2Applied Physics Dept., University of Córdoba, Spain
3Soil and Water Management and Crop Nutrition Laboratory, FAO/IAEA Agriculture & Biotechnology Laboratory, IAEA Laboratories Seibersdorf, Austria
4Animal and Plant Biology and Ecology Dept., Ecology section, Center for advance studies in olive groves and olive oils, University of Jaén, Spain
This study compares the distribution of bulk soil organic carbon (SOC also reported as Corg), its fractions (unprotected, physical, chemical and biochemically protected), available P (Pavail), organic nitrogen (Norg) and stable isotopes (δ15N and δ13C) signatures at four soil depths (0–10, 10–20, 20–30, 30–40 cm) between a nearby forested reference area and an historical olive orchard (established in 1856) located in Southern Spain. In addition, these soil properties, as well as water stable aggregates (Wsagg) were contrasted at eroding and deposition areas within the olive orchard, previously determined using 137Cs. Results highlight a significant depletion of SOC stock in the olive orchard as compared to the forested area, approximately 120 vs. 55 t C ha−1 at the top 40 cm of soil respectively, being severe in the case of unprotected carbon fraction. Erosion and deposition within the old olive orchard created large differences in soil properties along a catena, resulting in higher Corg, Pavail and Norg contents and δ15N at the deposition area and therefore defining two areas with a different soil quality status (degraded vs. non-degraded). Differences in δ15N at such different catena locations suggest that this isotopic signature has the potential for being used as an indicator of soil degradation magnitude, although additional studies would be required to confirm this finding. These overall results indicate that proper understanding of Corg content and soil quality in olive orchards require the consideration of the spatial variability induced by erosion/deposition processes for a convenient appraisal at farm scale.
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, 2020, https://doi.org/10.1016/j.iswcr.2020.01.001
José A. Gómeza, Alon Ben-Galb, Juan J. Alarcónc, Gabrielle De Lannoyd, Shannon de Roosd, Tomáš Dostále, Elias Fereresf, Diego S. Intriglioloc, Josef Krásae, Andreas Klikg, Gunther Liebhardg, Reinhard Nolzg, Aviva Peetersh, Elke Plaasi, John N. Quintonj, Miao Ruik, Peter Straussl, Xu Weifengk, Zhiqiang Zhangm, Funing Zhongn, David Zumre, Ian C. Doddj
a Institute for Sustainable Agriculture, IAS, CSIC, Avda Menendez Pidal S/N, Cordoba, Spain
b Agricultural Research Organization, Gilat Research Center, Israel
c Centro de Edafología y Biología Aplicada Del Segura (CSIC), Dept. Riego, Murcia, Spain
d Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium
e Czech Technical University in Prague, Faculty of Civil Engineering. CVUT, Prague, Czech Republic
f Agronomy Department, University of Cordoba, Cordoba, Spain
g University of Agricultural Sciences Vienna (BOKU), Vienna, Austria
h TerraVision Lab, Midreshet Ben-Gurion, Israel
I Georg-August-Universität Göttingen, Germany
j Centre for Sustainable Agriculture, Lancaster Environment Centre, Lancaster University, UK
k Center for Plant Water-Use and Nutrition Regulation and College of Life Sciences, Joint International Research Laboratory of Water and Nutrient in Crops, Fujian Agriculture and Forestry University, Fuzhou, China
l Institute for Land and Water Management Research, Federal Agency for Water Management, Petzenkirchen, Austria
m College of Soil and Water Conservation, Beijing Forestry University, Beijing, China
n College of Economics and Management, Nanjing Agricultural University, NAU, Nanjing, China
This article outlines the major scientific objectives of the SHui project that seeks to optimize soil and water use in agricultural systems in the EU and China, by considering major current scientific challenges in this area. SHui (for Soil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping systems) is large cooperative project that aims to provide significant advances through transdisciplinary research at multiple scales (plot, field, catchment and region). This paper explains our research platform of long-term experiments established at plot scale, approaches taken to integrate crop and hydrological models at field scale; coupled crop models and satellite-based observations at regional scales; decision support systems for specific farming situations; and the integration of these technologies to provide policy recommendations through socio-economic analysis of the impact of soil and water saving technologies. It also outlines the training of stakeholders to develop a basic common curriculum despite the subject being distributed across different disciplines and professions. As such, this article provides a review of major challenges for improving soil and water use in EU and China as well as information about the potential to access information made available by SHui, and to allow others to engage with the project.
Irrigation-Advisor—A Decision Support System for Irrigation of Vegetable Crops
Water 2019, 11(11), 2245; https://doi.org/10.3390/w11112245
Partner Publication (CSIC):
Modeling Sugar Beet Responses to Irrigation with AquaCrop for Optimizing Water Allocation
Water 2019, 11(9), 1918; https://doi.org/10.3390/w11091918
Partner Publication (University of Cordoba):
A weighted multivariate spatial clustering model to determine irrigation management zones. Computers and Electronics in Agriculture 162, 719-731.
Partner Publication (ARO):
Ohana-Levi, N., Bahat, I., Peeters, A., Stein, A., Cohen, Y., Nezer Y., Ben-Gal, A. (2019)
Management of agricultural fields according to spatial and temporal variability is an important aspect of precision agriculture. Precision management relies on division of a field into areas with homogeneous characteristics, management zones (MZs), which are likely affected by multiple, interrelated factors.
We present a method, based on machine learning and spatial statistics, to analyze the spatial relationship between a set of variables and determine management zones in a vineyard. The method involves:
- Fitting a model that quantifies the relationship between multiple variables and yield;
- Fitting a model that quantifies the effect of the spatial variability of multiple variables on yield spatial characteristics;
- Developing a weighted multivariate spatial clustering model as a method to determine MZs.
Twelve variables were sampled for 3893 vines in the wine grape vineyard. These variables included soil properties, terrain characteristics, and environmental impact, as well as crop-condition, using indices calculated from remote sensing images. The predictor variables were spatially characterized using hot-spot analysis (Getis Ord Gi* Z-score values) to assess their spatial variability. A gradient boosted regression trees (BRT) algorithm was used to analyze the spatial multivariable effect on yield spatial characteristics. MZs were determined using multivariate K-means clustering, with relative weights given to the predictors, based on their relative influence on yield spatial variability provided by the BRT model.
This method was compared to ordinary K-means clustering and K-means with spatial representation of the variables without weights using a dissimilarity index and spatial autocorrelation measures. Model performance was found to be very high and demonstrated that among the evaluated predictors, crop condition indices were the most important regressors for yield and its spatial characteristics. The weighted multivariate spatial clustering was found to perform better in terms of separability of the points and their spatial distribution than the other two clustering techniques. Quantifying yield and its within-field spatial variability, ranking the effects of the predictors and their spatial variabilities, and segmentation of MZs through multivariable spatial analysis, are expected to benefit irrigation management and agricultural decision-making processes.