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

a InstituteforSustainableAgriculture(CSIC),14004Cordoba,Spain

b DepartmentofAgronomy,UniversityofCordoba,14014Cordoba,Spain

Highlights

• 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.

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

Abstract:

This study compares the distribution of bulk soil organic carbon (SOC also reported as C_org), its fractions (unprotected, physical, chemical and biochemically protected), available P (P_avail), organic nitrogen (N_org) 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 (W_sagg) 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 C_org, P_avail and N_org 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.

International Soil and Water Conservation Research, 2020, https://doi.org/10.1016/j.iswcr.2020.01.001

José A. Gómez^a, Alon Ben-Gal^b, Juan J. Alarcón^c, Gabrielle De Lannoy^d, Shannon de Roos^d, Tomáš Dostál^e, Elias Fereres^f, Diego S. Intrigliolo^c, Josef Krása^e, Andreas Klik^g, Gunther Liebhard^g, Reinhard Nolz^g, Aviva Peeters^h, Elke Plaasⁱ, John N. Quinton^j, Miao Rui^k, Peter Strauss^l, Xu Weifeng^k, Zhiqiang Zhang^m, Funing Zhongⁿ, David Zumr^e, Ian C. Dodd^j

^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

Abstract

本文概述了SHui项目的主要科学目标，该项目旨在通过考虑该领域当前面临的主要科学挑战，优化欧盟和中国农业系统中的土壤和水的利用。SHUI项目（建立土壤水文学研究平台，支持欧洲和中国种植制度的管理创新）是一个大型合作项目，旨在通过多尺度(地块、田地、流域和区域)的跨学科研究取得重大进展。本文阐述了在小区尺度上建立的长期试验研究平台，以及在田间尺度上整合作物和水文模型的方法，区域尺度上的作物耦合模型和卫星观测，针对特定农业情况的决策支持系统并结合这些技术，通过对水土保持技术影响的社会经济分析，提出政策建议。它还概述了利益相关者的培训，以制定一个基本的共同课程，尽管该主题分布在不同的学科和专业。还指出了欧盟和中国在改善土壤和水利用方面面临的主要挑战，以及有关获取水资源部提供的信息以及允许其他人参与该项目的可能性的信息。

Water 2019, 11(11), 2245; https://doi.org/10.3390/w11112245

参与单位发表论文（CSIC）

José M. Mirás-Avalos ^1,,José S. Rubio-Asensio ¹,Juan M. Ramírez-Cuesta ¹,José F. Maestre-Valero ² and Diego S. Intrigliolo ^1,3

1. 土壤学和应用生物学中心（CEBAS），高等科学研究理事会(CSIC)

2. 农业工程高级技术学校，卡塔赫纳理工大学，卡塔赫纳阿方索大道XIII 48号

3. 瓦伦西亚农业研究所(IVIA)，可持续农业发展中心(CEDAS)，CSIC“地中海农业灌溉”相关部门

摘要：

气候变化将加剧水资源短缺，因此灌溉必须与节约用水相适应，这就需要一种能够向终端用户提供浇水建议的操作工具。这项工作提出了一种新的工具——灌溉顾问(IA)，它能够基于天气预报，分别测定土壤蒸发和作物蒸腾，因此适用于多种农业情况。

通过计算几个统计指标以及利用当地作物系数，用FAO-56作物蒸散发(ETcFAO)方法对IA进行了检验。另外，由有经验的农民将IA建议与现行标准做法进行比较(F)。

在西班牙东南部对四种主要栽培品种(菊苣、莴苣、甜瓜和马铃薯)进行了六次田间试验，测定了灌溉用水、作物产量、地上生物量和水分生产力。将IA与ETcFAO进行比较时发现，作物需水量被低估(5%-20%)，尽管这一指数被证明是合理的调整。除生菜外，IA的建议与F相比节约了13%的水，当使用IA时灌溉盈余为31%。

Water 2019, 11(9), 1918; https://doi.org/10.3390/w11091918

作者单位：科尔多瓦大学
Margarita Garcia-Vila1, Rodrigo Morillo-Velarde 2 和 Elias Fereres 1,3
1. 科尔多瓦大学农学系，西班牙科尔多瓦，14007
2. 甜菜作物改良研究协会，西班牙瓦拉多利德， 47012
3. 可持续农业研究所，CSIC, 14004 ，科尔多瓦，西班牙

摘要：例如AquaCrop之类的基于作物过程的模型，应用广泛，但必须要经过精确地校准和验证。甜菜是缺水地区的重要作物。针对于过去出现的模型校准中存在的差异和不确定性，我们认为有必要进行一项研究。其主要目的是使用单次亏水灌溉实验的结果来校准6.1版本的AquaCrop 。该模型通过来自8个农场的不同位置、年份、品种、播种日期和灌溉等数据进行了验证。结果表明，AquaCrop模拟冠层覆盖度、生物量和最终产量的总体性能是准确的，呈现出的RMSE分别为11.39%、2.10 t ha−1和0.85 t ha−1。一旦模型得到适当的校准和验证，就会通过情景分析，来评估西班牙地区两个甜菜主要产地，不同灌溉用水分配对春秋季播种的甜菜产量和水分生产力产生的影响。结果表明，灌溉用水分配和播种时间对甜菜生产及其灌溉用水生产率的重要影响，凸显了该模型的潜力。

合作伙伴出版物（ARO）：

Ohana-Levi, N., Bahat, I., Peeters, A., Stein, A., Cohen, Y., Nezer Y., Ben-Gal, A.（2019）

根据时间和空间变化管理农田是精准农业的一个重要方面。精确管理依赖于将农田划分为同性质特征的区域和管理区域（MZs），这些区域可能受到多重相关因素的影响。

本文提出了一种基于机器学习和空间统计的方法，来分析一系列变量之间的空间关系，并确定葡萄园中的管理区域。该方法包括：

1.拟合一个可以确定多重变量和产量之间关系数量的模型

2.拟合一个可以确定多重变量的空间可变性对产量空间特征的影响数量的模型

3.开发一种加权多元空间聚类模型作为确定MZs的方法

在酿酒葡萄园中对3893株葡萄藤所涉及的12个变量进行抽样，包括土壤属性、地形特征、环境影响和作物条件等，使用遥感图像计算得到的指数。使用热点分析对预测变量进行空间表征，以评估其空间变异性。采用梯度