A Review of Soil-Improving Cropping Systems for Soil Salinization

A major challenge of the Sustainable Development Goals linked to Agriculture, Food Security, and Nutrition, under the current global crop production paradigm, is that increasing crop yields often have negative environmental impacts. It is therefore urgent to develop and adopt optimal soil-improving cropping systems (SICS) that can allow us to decouple these system parameters. Soil salinization is a major environmental hazard that limits agricultural potential and is closely linked to agricultural mismanagement and water resources overexploitation, especially in arid climates. Here we review literature seeking to ameliorate the negative effect of soil salinization on crop productivity and conduct a global meta-analysis of 128 paired soil quality and yield observations from 30 studies. In this regard, we compared the effectivity of different SICS that aim to cope with soil salinization across 11 countries, in order to reveal those that are the most promising. The analysis shows that besides case-specific optimization of irrigation and drainage management, combinations of soil amendments, conditioners, and residue management can contribute to significant reductions of soil salinity while significantly increasing crop yields. These results highlight that conservation agriculture can also achieve the higher yields required for upscaling and sustaining crop production

Weed cover controls soil and water losses in rainfed olive groves in Sierra de Enguera, eastern Iberian Peninsula

[EN] Soil erosion is a threat for the sustainability of agriculture and severely affects the Mediterranean crops. Olive groves are among the rainfed agriculture lands that exhibit soil and water losses due to the impact of unsustainable practices such as conventional tillage and herbicides abuse. To achieve a more sustainable olive oil production, alternative, greener crop management practices need to be tested in the field. Here, a weed cover (CW) treatment is tested at an olive tree plantation that has undergone conventional mechanical tillage for 20 years and results were compared against an adjacent control plantation that maintained tillage as a weed control strategy (CO). Both plantations were under the same tillage management for centuries and macroscopic analysis confirms they are otherwise comparable. Compared to the CO, where tilled soil cover was zero, 20 years of CW (weeds cover 64%; litter cover 5%) had led to significantly higher values of soil bulk density and soil organic matter. Results from rainfall simulation experiments at 55 mm h¿1 on 0.25 m2 plots under CO (N = 25) and CW (N = 25) show that as a result of the improved soil structure, CW (i) reduced soil losses by two orders of magnitude (140 times), (ii) decreased runoff yield by one order of magnitude (from 2.65 till 27.6% of the rainfall), (iii) significantly reduced runoff sediment concentration (from 18.6 till 1.43 g l¿1), and (iv) significantly delayed runoff generation (CO = 273 s; CW = 788 s). These results indicate that weed cover is a sustainable land management practice in Mediterranean olive groves and promotes sustainable agriculture production in mountainous areas under rainfed conditions, which are typically affected by high erosion rates such those found in the CO plots. Due to the spontaneous recovery of plant cover, we conclude that weed cover is an excellent nature-based solution to increase in the soil organic matter content and soil erosion reduction in rainfed olive orchards.We thank Nathalie Elisseou Leglise for her kind management of our financial support. We wish to thank the Department of Geography members for their support along three decades to our research at the Soil Erosion and Degradation Research team (SEDER), with special thanks to the scientific researchers that as visitors from other research teams contributed to the SEDER research. And we also thank the Laboratory for Geomorphology technicians (Leon Navarro) for the key contribution to our research. The collaboration of the Geography and Environmental Sciences students was fruitful and enjoyable. The music of Feliu Ventura and Els Jovens was an inspiration during the writing of this paper at the COVID19 time. We thank the editor and the reviewers for the wise advises. This research was funded by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no 603498 (RECARE project). A.C. thanks the Co-operative Research programme from the OECD (Biological Resource Management for Sustainable Agricultural Systems) for its support with the 2016 CRP fellowship (OCDE TAD/CRP JA00088807). I.N.D. conducted this research in the framework of "DRip Irrigation Precise-DR.I.P: Development of an Advanced Precision Drip Irrigation System for Tree Crops" (Project Code: T1EDK-03372) which is co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCHCREATE-INNOVATE.Cerdà, A.; Terol, E.; Daliakopoulos, IN. (2021). Weed cover controls soil and water losses in rainfed olive groves in Sierra de Enguera, eastern Iberian Peninsula. Journal of Environmental Management. 290:1-9. https://doi.org/10.1016/j.jenvman.2021.112516S1929

Data-driven competitive facilitative tree interactions and their implications on nature-based solutions

Spatio-temporal data are more ubiquitous and richer than even before and the availability of such data poses great challenges in data analytics. Ecological facilitation, the positive effect of density of individuals on the individual's survival across a stress gradient, is a complex phenomenon. A large number of tree individuals coupled with soil moisture, temperature, and water stress data across a long temporal period were followed. Data-driven analysis in the absence of hypothesis was performed. Information theoretic analysis of multiple statistical models was employed in order to quantify the best data-driven index of vegetation density and spatial scale of interactions. Sequentially, tree survival was quantified as a function of the size of the individual, vegetation density, and time at the optimal spatial interaction scale. Land surface temperature and soil moisture were also statistically explained by tree size, density, and time. Results indicated that in space both facilitation and competition co-exist in the same ecosystem and the sign and magnitude of this depend on the spatial scale. Overall, within the optimal data-driven spatial scale, tree survival was best explained by the interaction between density and year, sifting overall from facilitation to competition through time. However, small sized trees were always facilitated by increased densities, while large sized trees had either negative or no density effects. Tree size was more important predictor than density in survival and this has implications for nature-based solutions: maintaining large tree individuals or planting species that can become large-sized can safeguard against tree-less areas by promoting survival at long time periods through harsh environmental conditions. Large trees had also a significant effect in moderating land surface temperature and this effect was higher than the one of vegetation density on temperature

A SIFT-Based DEM Extraction Approach Using GEOEYE-1 Satellite Stereo Pairs

A module for Very High Resolution (VHR) satellite stereo-pair imagery processing and Digital Elevation Model (DEM) extraction is presented. A large file size of VHR satellite imagery is handled using the parallel processing of cascading image tiles. The Scale-Invariant Feature Transform (SIFT) algorithm detects potentially tentative feature matches, and the resulting feature pairs are filtered using a variable distance threshold RANdom SAmple Consensus (RANSAC) algorithm. Finally, point cloud ground coordinates for DEM generation are extracted from the homologous pairs. The criteria of average point spacing irregularity is introduced to assess the effective resolution of the produced DEMs. The module is tested with a 0.5 m × 0.5 m Geoeye-1 stereo pair over the island of Crete, Greece. Sensitivity analysis determines the optimum module parameterization. The resulting 1.5-m DEM has superior detail over reference DEMs, and results in a Root Mean Square Error (RMSE) of about 1 m compared to ground truth measurements

Drought- and Salt-Tolerant Plants of the Mediterranean and Their Diverse Applications: The Case of Crete

Drought and salinity are two of the most urgent challenges faced in Mediterranean ecosystems, equally impacting natural systems, agricultural crops, and urban green. While many technical and soft approaches have been proposed to anticipate, mitigate, and remediate these impacts, a class of solutions has possibly been in front of us all along. Native Mediterranean fauna is well adapted, and when properly established still has unexploited conservation, restoration, and production diversification potential. Here, we outline the results of a long-term experiment taking place on the island of Crete, Greece that started in 1996 and involves over 70 native Mediterranean plants planted and monitored in various green spaces (private, shared, public) and a university campus under a diversity of adverse topographies (e.g., coastal, steep slopes), soils (e.g., disturbed, nutrient-deficient), and microclimatic conditions, taking various plant formations and serving various functions. After plant establishment, drought and salinity resistance were evaluated by gradually exposing plants (n = 5249) to deficit irrigation and saline environmental conditions, and plants were followed up for at least 5 years to empirically assess their ability to cope with abiotic stress. From the Mediterranean plants that were planted and tested, 52 were singled out because of their resistance and additional favorable traits. Motivated by this long-term assessment, a systematic literature review was conducted using the protocol Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) to validate empirical results, determine which were still unexplored, and bring to light additional uses. Results showed that 41 of the plants included in this research have significant medicinal properties, 26 have nutritional uses, 17 industrial uses, and 18 have evidence of cosmetology uses. Additionally, the empirical assessment gave new evidence of at least 40 new species–trait combinations. By formally documenting the characteristics of these native Mediterranean plants, this work highlights their versatile traits, and the prospect of creating new uses and value chains enables, for the first time their inclusion in planting-decision support systems and aims to increase demand and facilitate the scaling up of native greening in the context of sustainable land and water management within and beyond the Mediterranean basin

A weather radar data processing module for storm event analysis

Summarization: A pre- and post-processing weather radar data module was developed in the Matlab suite of software with GIS data exchange abilities for storm event analysis. During pre-processing, each radar sweep is converted from spherical to Cartesian coordinates in the desired temporal and spatial resolution. The module's functionality in post processing includes radar data display, geo-referencing over GIS maps, data filtering with the Wiener filter and single or multiple sweep processing. The user can perform individual storm cell detection and tracking, resulting in the storm's average velocity and track length. The tested methods are modifications of the LoG (Laplacian of the Gaussian) blob detection method and a Brownian particle trajectory linking algorithm. Radar reflectivity factor (Z) data can be referenced over predefined rainfall (R) gauges in order to determine the radar Z–R equation parameters. The user can also produce spatially distributed precipitation estimates by using standard Z–R equations from the literature. The module's functionality is demonstrated using data from a rainfall event captured by the NSA Souda Bay C-Band radar during a storm in October 2006. Results show that the Rosenfeld Tropical Z–R equation is the one that gives a satisfactory description of the spatial and temporal precipitation distribution of the investigated event.Παρουσιάστηκε στο: Journal of Hydroinformatic

Land subsidence hazard modeling : Machine learning to identify predictors and the role of human activities

Land subsidence caused by land use change and overexploitation of groundwater is an example of mismanagement of natural resources, yet subsidence remains difficult to predict. In this study, the relationship between land subsidence features and geo-environmental factors is investigated by comparing two machine learning algorithms (MLA): maximum entropy (MaxEnt) and genetic algorithm rule-set production (GARP) algorithms in the Kashmar Region, Iran. Land subsidence features (N = 79) were mapped using field surveys. Land use, lithology, the distance from traditional groundwater abstraction systems (Qanats), from afforestation projects, from neighboring faults, and the drawdown of groundwater level (DGL) (1991–2016) were used as predictive variables. Jackknife resampling showed that DGL, distance from afforestation projects, and distance from Qanat systems are major factors influencing land subsidence, with geology and faults being less important. The GARP algorithm outperformed the MaxEnt algorithm for all performance metrics. The performance of both models, as measured by the area under the receiver-operator characteristic curve (AUROC), decreased from 88.9–94.4% to 82.5–90.3% when DGL was excluded as a predictor, though the performance of GARP was still good to excellent even without DGL. MLAs produced maps of subsidence risk with acceptable accuracy, both with and without data on groundwater drawdown, suggesting that MLAs can usefully inform efforts to manage subsidence in data-scarce regions, though the highest accuracy requires data on changes in groundwater level.</p