Desertification Risk Research Articles

Spatial indicators for desertification in southeast Vietnam

Abstract. Desertification is influenced by different factors that relate to climate, soil, topography, geology, vegetation, human pressure, and land and water management. The quantification of these factors into spatially explicit indicators and subsequent evaluation provides for a framework that allows us to identify areas currently at risk of desertification and to evaluate important contributing biophysical and socio-economic factors. Based on local knowledge of environmental contributing factors to the risk of desertification in the Binh Thuan Province of southeast Vietnam, a baseline 2010 map showed that 14.4 % of the area, mainly along the coast and in the northeast, is desertified with another 35.4 % at severe risk of desertification. The Vietnamese Ministry of Natural Resource and Environment has defined the area with a ratio of rainfall to evapotranspiration smaller or equal to 0.65, which equals 1233 km2 or 15 % of the province, as desertified area, which corresponds well with the baseline 2010 map. The developed framework incorporates the important contributing factors and therefore allows for decision support in a “what if” structure and for the projection of potentially vulnerable areas under future scenarios. With projected climate change and population growth, the desertified area is expected to increase by 122 % (or 137 850 ha) towards 2050. The developed methodology can be extended to neighbouring provinces that experience similar sensitivities to desertification.

Desertification vulnerability and risk assessment for Turkey via an analytical hierarchy process model

AbstractProducing a scientific desertification risk map has become one of the national primary tasks in order to effectively combat desertification in Turkey. In this respect, General Directorate of Combating Desertification and Erosion in cooperation with TÜBİTAK BİLGEM, generated the Desertification Model of Turkey (DMT), an original geographical and mathematical country‐specific model under the umbrella project called the “Watershed Monitoring and Evaluation System” (HIDS). The DMT is a tool used for estimating land degradation, assessing desertification vulnerability and risk, preparing watershed scale risk maps, and monitoring desertification hot spots of the country. The DMT has been designed to consist of 48 indicators and 37 subindicators under seven main criteria defined by an expertise group and using the analytical hierarchy process. Percentage distribution of desertification risk among watersheds was calculated in nine classes based on the Desertification Risk Map of Turkey and revised in accordance with field surveys carried out in 2016–2017 within the scope of the DMT Verification and Calibration Project. Results for major watersheds of Turkey revealed that at least 15% of lands of all river basins are under moderate–high desertification risk, with the exception of Aras, Western Black Sea, Konya Closed, Marmara, and Meriç–Ergene watersheds. In addition, it was identified that the DMT and the Desertification Risk Map of Turkey present results at an accuracy rate of 84% and with 90% level of confidence at microwatershed scale.

MULTI-PURPOSE CHESTNUT CLUSTERS DETECTION USING DEEP LEARNING: A PRELIMINARY APPROACH

Abstract. In the early 1980′s, the European chestnut tree (Castanea sativa, Mill.) assumed an important role in the Portuguese economy. Currently, the Trás-os-Montes region (Northeast of Portugal) concentrates the highest chestnuts production in Portugal, representing the major source of income in the region (€50M-€60M). The recognition of the quality of the Portuguese chestnut varieties has increasing the international demand for both industry and consumer-grade segments. As result, chestnut cultivation intensification has been witnessed, in such a way that widely disseminated monoculture practices are currently increasing environmental disaster risks. Depending on the dynamics of the location of interest, monocultures may lead to desertification and soil degradation even if it encompasses multiple causes and a whole range of consequences or impacts. In Trás-os-Montes, despite the strong increase in the cultivation area, phytosanitary problems, such as the chestnut ink disease (Phytophthora cinnamomi) and the chestnut blight (Cryphonectria parasitica), along with other threats, e.g. chestnut gall wasp (Dryocosmus kuriphilus) and nutritional deficiencies, are responsible for a significant decline of chestnut trees, with a real impact on production. The intensification of inappropriate agricultural practices also favours the onset of phytosanitary problems. Moreover, chestnut trees management and monitoring generally rely on in-field time-consuming and laborious observation campaigns. To mitigate the associated risks, it is crucial to establish an effective management and monitoring process to ensure crop cultivation sustainability, preventing at the same time risks of desertification and land degradation. Therefore, this study presents an automatic method that allows to perform chestnut clusters identification, a key-enabling task towards the achievement of important goals such as production estimation and multi-temporal crop evaluation. The proposed methodology consists in the use of Convolutional Neural Networks (CNNs) to classify and segment the chestnut fruits, considering a small dataset acquired based on digital terrestrial camera.

Spatial assessment and mapping of soil quality index for desertification in the semi-arid terrestrial ecosystem using MCDM in interval type-2 fuzzy environment

Land degradation is a significant phenomenon on the world particularly arid, semi-arid and semi-humid terrestrial ecosystem environments due to variety of factors such as through unsustainable land management, and through physical and climatic characteristics. One of the main indicators and commonly used in the land degradation and desertification is the soil quality index (SQI). Soil quality refers to the soil's capacity to function. The aim of this study is to determine SQI in terms of degradation and desertification in the Çorum Basin by taking into consideration of nine soil criteria in Desertification Model of Turkey (DTM). The study area includes semi-arid terrestrial ecosystem. Multi-criteria decision analysis was performed in Fuzzy environment integrated with GIS techniques and different interpolation models, which was chosen as the basis for the present study. In order to assign weighting value for each criterion, interval type-2 fuzzy-AHP approach was used to make sufficiently sensitive levels of importance of the criteria. Results classified more than 45% of the total area of approx. 68,755 ha as low and very low, while only about 31% of the study area’ soil indicated area high and very high by the SQI in terms of land degradation and desertification risk.

English

Obviously, there is land-use/cover change with multifaceted driving factors and associated adverse impacts out there in different parts of Ethiopia. Evidence from published journals justifies this fact but not yet compiled in a manner to convey information on the trend of change and its criticality with the aim to handle the problems. Thus, filing up all the driving factors and its adverse out come from different corners of Ethiopia together under one reviewed journal would help for policy measures and management aspects. In view of this, critical driving factors of land-use/cover change and all its possible consequences bridged together by reviewing highly relevant published journals. Deforestation, high human and livestock population, investment (coffee, tea plantations), agricultural activities ranging from small-scale subsistence agriculture to large-scale commercial agricultural schemes, urban sprawling, charcoal production, woodland collection, poor law enforcement, land rotation searching for better grazing land, resettlement (spontaneous and planned), prevalence of drought, were the leading drivers of land-use/cover change in Ethiopia. In response to these driving factors, the following are major identified adverse effects such as loss of biodiversity and ecosystem service degradation, deterioration of wetlands, creation of new landscape, rainfall variability, reduction in stream flow, increase frequent extreme in weather, conflicts of interest due to resource scrambling, soil and land degradation, prevalence of drought, increase in runoff and sedimentations, increased risk of desertification and woody land cover reduction. In general, these systematic reviews hopefully uncover all the existing scenarios and give mental picture of wide range driving factors of land-use/cover change and associated problems for stakeholders across the country as they might develop ways to end/reduce further vulnerabilities. Key words: Land-use/cover change, driving factors, adverse impacts, Ethiopia.

A Combined AHP- and TOPSIS-Based Approach in the Assessment of Desertification Disaster Risk

In this study, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP) methods were used to determine, prioritize, and assess the most effective desertification indices. A list of 32 influential indices was identified using existing literature in Iran and around the globe through questionnaires. Criteria for evaluating indices were weighted using Shannon entropy, and then TOPSIS ranking method was applied to determine the most effective desertification assessment to deal with this issue in Kashan region, Isfahan province. Indices were weighed and prioritized by analytics hierarchy process. Finally, the Mediterranean Desertification and Land Use (MEDALUS) model was used to assess the intensity and develop a desertification risk map. The results asserted that sensitivity to minor changes with a weight of 0.341 has the highest importance, while time and cost with a weight of 0.11 has the least importance for the evaluation of indices. Among the 32 ranked indices, 20 of which were identified as the most effectual indices including annual precipitation, salinity, and vegetation, to name a few. The results also showed that the land use change index with a weight of 0.217 and annual rainfall with a weight of 0.166 have the highest priority among human and natural indices that should be prioritized in managing and controlling desertification. The average weight of quantitative desertification intensity for the whole area was 1.391 placed in critical (1) class of desertification. Among the six considered desertification criteria, the climate and the destruction of water resources were the most important criteria.

Progress in Semi-arid Climate Change Studies in China

This article reviews recent progress in semi-arid climate change research in China. Results indicate that the areas of semi-arid regions have increased rapidly during recent years in China, with an increase of 33% during 1994–2008 compared to 1948–62. Studies have found that the expansion rate of semi-arid areas over China is nearly 10 times higher than that of arid and sub-humid areas, and is mainly transformed from sub-humid/humid regions. Meanwhile, the greatest warming during the past 100 years has been observed over semi-arid regions in China, and mainly induced by radiatively forced processes. The intensity of the regional temperature response over semi-arid regions has been amplified by land-atmosphere interactions and human activities. The decadal climate variation in semi-arid regions is modulated by oceanic oscillations, which induce land-sea and north-south thermal contrasts and affect the intensities of westerlies, planetary waves and blocking frequencies. In addition, the drier climates in semi-arid regions across China are also associated with the weakened East Asian summer monsoon in recent years. Moreover, dust aerosols in semi-arid regions may have altered precipitation by affecting the local energy and hydrological cycles. Finally, semi-arid regions in China are projected to continuously expand in the 21st century, which will increase the risk of desertification in the near future.

Known unknowns: Filling the gaps in scientific knowledge production in the Caatinga

The Caatinga is an ecologically unique semi-arid region of northeast Brazil characterized by high levels of endemism and severe anthropogenic threats from agricultural development and climate change. It is also one of the least known biomes in Brazil due to a combination of inadequate investment, low regional research capacity and difficult working conditions. However, while the lack of scientific knowledge of the Caatinga is well known, the spatial and temporal distribution of knowledge production has not been investigated. This is important because such biases undermine the development of effective conservation policy and practice and increase the uncertainty associated with conservation actions. Here, we map the geography of conservation knowledge production in the Caatinga and use an innovative hurdle model to identify the presumptive factors driving these patterns. Our analysis revealed strong geographic patterns, with research sites concentrated in the east of the region and in areas close to roads and research centres. There was also a positive association between conservation knowledge production and risk of desertification, indicating that conservation scientists are responding to conservation challenges faced by Caatinga’s fauna and flora arising from climate change. Our results also highlight the pivotal role of pioneer scientists (those who develop research sites in previously unstudied/understudied areas) in determining the future geographic patterns of knowledge production. We conclude our article with a brief discussion of potential policies for increasing the spatial representativeness of conservation research in this remarkable ecosystem.

The Latent Relationship Between Soil Vulnerability to Degradation and Land Fragmentation: A Statistical Analysis of Landscape Metrics in Italy, 1960-2010.

Land degradation leads to almost unpredictable spatial outcomes and environmental dynamics demanding a more integrated monitoring approach. In this framework, we debate on (apparent and latent) connections between land fragmentation and soil degradation by identifying areas with increased levels of soil degradation that underlie distinctive spatial trends of land fragmentation. Moving from land-use maps to an empirical study of desertification, the framework proposed in this work may support environmental monitoring and inform land conservation policies. To assess land fragmentation, a quantitative approach grounded on a comprehensive analysis of landscape metrics available in FRAGSTATS package was illustrated and applied to Italy as a representative case of complex landscape dynamics in the Mediterranean basin. The Environmental Sensitive Area methodology was adopted to monitor the level of soil vulnerability to degradation. Three classes of land vulnerability-unaffected, fragile and critical-were identified and analysed using metrics to investigate possible links between soil degradation and land fragmentation. During the study period (1960-2010), Italy evolved towards a more fragmented landscape, characterised by increasingly smaller and contiguous patches, heterogeneous land-use structures and more irregular patches. We also introduced concepts focusing on syndromes of soil degradation characterised by a variety of attributes that correlate with land fragmentation. The present study makes an important contribution towards an operational system for identifying areas at risk of desertification. Analysis of land fragmentation as a proxy of soil degradation allows the characterisation of general landscape changes and identification of place-specific patterns associated with spatio-temporal dynamics leading to higher risk of desertification.

Carbon Cycling in Global Drylands

The aim of this paper is to describe the carbon cycle in drylands in relation to the processes, factors, and causes affecting it. A specific focus is placed on both biotic and abiotic mechanisms of carbon sequestration in drylands in relation to mitigation of the anthropogenic climate change. Global dryland area is increasing along with an increase in risks of desertification, salinization, and eolian/hydrologic processes of accelerated soil erosion with strong impacts on the carbon cycle. Nonetheless, drylands contribute strongly towards the land-based sink of the atmospheric carbon dioxide through sequestration of carbon in the soil, ground water, and biomass. Thus, dryland ecosystems affect inter-annual variability in the global carbon cycle and create a negative feedback through carbon sequestration. Global drylands, covering 66.7 M km2 or 45.36% of the Earth’s land area, strongly impact the ecosystem carbon stock, contribute to the land-based carbon sink, and provide a negative feedback to the global carbon cycle. Whereas the net primary productivity is limited by the water scarcity, especially in hyper-arid and arid ecoregions, sequestration of inorganic carbon in soil and ground water is an important control of the carbon cycle. Desertification, caused by eolian and hydrologic erosion along with salinization, must be controlled and reversed to enhance carbon sequestration, achieve land degradation neutrality, and create a negative feedback. Carbon sequestration strategy recognizes “soil” as a rights holder to be protected, restored and naturally evolve.

Correction To: Desertification Risk Assessment of Sand Dunes in Middle Egypt: A Geotechnical Environmental Study

In the original publication the grant number is incorrectly published. The grant number D-071-145-1439.

Geostatistical analysis of precipitation in the island of Crete (Greece) based on a sparse monitoring network.

Based on the predictions of General Circulation Models, significant reduction of precipitation in Mediterranean areas is a possible scenario. Hence, better understanding of the spatial and temporal precipitation patterns is necessary in order to quantify desertification risks and design suitable mitigation measures. This study uses monthly precipitation measurements from a sparse network of 54 monitoring stations on the Mediterranean island of Crete (Greece). The study period extends from 1948 to 2012. The data reveal strong correlations between the western and eastern parts of the island. However, the average annual precipitation in the West is about 450 mm higher than that in the East. We construct a spatial model of average annual precipitation in Crete. The model involves a topographic trend and residuals with anisotropic spatial correlations which are fitted with a recently developed variogram function. We use regression kriging to generate annual precipitation maps and to identify locations of high estimation uncertainty. To our knowledge, this is the most detailed spatial analysis of precipitation in Crete to date. We present the analysis in detail for the year 1971. The trend accounts for ≈ 74% of the total variance. The highest precipitation estimate is 2331 mm in the West and 1781 mm in the East. The highest relative estimation uncertainty (≈ 20%) is observed along the southeastern coastline of the island, where the lowest values of annual precipitation are observed. This region includes one of the major agricultural areas of the island. The same overall patterns are found for other years in the study. Finally, we find no statistical evidence for a decrease in the global (over the entire island) annual precipitation during the study period.

Fuzzy AHP Integrated with GIS Analyses for Drought Risk Assessment: A Case Study from Upper Phetchaburi River Basin, Thailand

This study aimed to analyse and assess desertification risks in the Upper Phetchaburi River Basin. Upstream areas are especially crucial for aquatic ecosystems since the mid- and downstream areas are continuously being utilized for agricultural and community purposes. Many parts of the basin have been at moderate risk of drought. The fuzzy analytical hierarchy process (FAHP) is an effective and widely accepted model used to identify complicated environmental problems and disasters and prioritize factors in environmental studies. This study emphasized on four main factors influencing drought: Climate, physical factors, soil and land utilization factors. Each factor contains ten sub-criteria to identify severity levels and specific issues. The major areas of the basin were facing different risk levels: moderate (21%), high (5.79%) and severe (0.07%). Precipitation and slope gradient were the main factors affecting drought risks. The problematic areas were agricultural areas located in midstream and downstream areas. Therefore, spatial mitigations and possible ways forward should focus on increasing moisture contents—to reduce soil erosion and enhance soil fertility—and create restrictions to ensure appropriate land use. The mitigations must take into account spatially critical factors and must also include an integrated plan for the entire basin area.

Identifying agronomic options for better potato production and conserving water resources in the agro-pastoral ecotone in North China

Potato is a main food crop in the agro-pastoral ecotone (APE) of North China, but its yield is low and highly variable. Agronomic managements, such as irrigation, fertilization and intensive cropping systems, has been used for increasing potato yield but resulted in negative environmental sequences (e.g., soil dryness and groundwater table decline) in such arid and semi-arid regions. This study aims to quantify the agronomic options for better potato production and groundwater conservations based on field experiments together with APSIM-Potato model. The long-term simulated crop yield, economic income and environmental impact (surface soil water content and groundwater table) were analyzed for the different cropping systems of potato (continuous planting, CP; alternating planting and fallowing, APF; long-term continuous fallowing, LF) and irrigation scheduling (rainfed and one-four irrigations). The calibrated APSIM-Potato performed well in simulating the responses of soil water dynamics, leaf area index (LAI), biomass, evapotranspiration (ET) and yield of potato to different irrigation treatments. Long-term (1981–2010) simulated potato yield could be increased by 43.4–90.2% from rainfed yield if irrigation was applied from 60 to 240 mm and the yield gap between the potential and rainfed yields could be narrowed by 41.5–85.9%. Across the APE, CP with four irrigations (240 mm) produced the maximum income of 18,445 Yuan·ha−1 while WUE was the highest for both CP and APF with two irrigations (120 mm). However, such irrigation amounts would decrease the groundwater table by 4.1–42 m across the APE during this period. Rainfed CP could enhance the groundwater table by 0–3 m but decrease available soil water content in 1 m depth by 15.5 mm per decade from 1981 to 2010. Rainfed APF and LF increase available soil water content in 1 m depth by 2–79 mm and 100–143 mm respectively. The study suggested that CP would decrease the soil moisture significantly and increase the risk of soil desertification in the APE. APF with one irrigation by harvesting rainwater could be a feasible way of maintaining soil moisture and groundwater conservation if government subsidy could guarantee the incomes of local farmers in the APE in North China.

Influences of 1.5 °C and 2.0 °C global warming scenarios on water use efficiency dynamics in the sandy areas of northern China

Water use efficiency (WUE) is an important variable used in hydrometeorology study to reveal the links between carbon-water cycles in sandy ecosystems which are highly sensitive to climate change and can readily reflect the effects of it. In light of the Paris Agreement, it is essential to identify the regional impacts of 0.5 °C of additional global warming to inform climate adaptation and mitigation strategies. Using the modified Carnegie-Ames-Stanford Approach (CASA) and Advection-Aridity (AA) models with global warming values of 1.5 °C and 2.0 °C above preindustrial levels from Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b) datasets, we conducted a new set of climate simulations to assess the effects of climate on WUE (the ratio of net primary productivity (NPP) to actual evapotranspiration (ETa)) in different sandy land types (mobile sandy land, MSL; semimobile/semifixed sandy land, SMSF; and fixed sandy land, FSL) during the period of baseline (1986–2005) and future (2006–2100). The spatiotemporal patterns of ETa, NPP, and WUE mostly showed increasing trends; the value of WUE decreased (6.40%) only in MSL with an additional 0.5 °C of warming. Meteorological and vegetation factors determined the variations in WUE. With warming, only the correlation between precipitation and WUE decreased in the three sandy land types, and the leaf area index (LAI) increased with an additional 0.5 °C of warming. The desertification degree comprehensively reflects the linkages among the standardized precipitation evapotranspiration index (SPEI), LAI and WUE. Simulation results indicated the sandy area extent could potential increase by 20 × 104 km2 per decade on average during 2016–2047 and that the increase could be gradual (2.60 × 104 km2 per decade) after 2050 (2050–2100). These results highlight the benefits of limiting the global mean temperature change to 1.5 °C above preindustrial levels and can help identify the risk of desertification with an additional 0.5 °C of warming.

Monitoring land sensitivity to desertification in Central Asia: Convergence or divergence?

In Central Asia, desertification risk is one of the main environmental and socioeconomic issues; thus, monitoring land sensitivity to desertification is an extremely urgent issue. In this study, the combination of convergence patterns and desertification risk is advanced from a technical perspective. Furthermore, the environmentally sensitive area index (ESAI) method was first utilized to monitor the risk of desertification in Central Asia. In the study, the spatial and temporal patterns of desertification risk were illustrated from 1992 to 2015 using fourteen indicators, including vegetation, climate, soil and land management quality. The ESAI spatial convergence across administrative subdivisions was explored for three time intervals: 1992–2000, 2000–2008 and 2008–2015. The results indicated that nearly 13.66% of the study area fell into the critical risk of desertification from 1992 to 2008. However, the risk of desertification has improved since 2008, with critical classifications decreasing by 19.70% in 2015. According to the mutation year detection in the ESAI, 25.89% of the pixels with mutation years from 1992 to 2000 were identified, and this value was higher than that during the other time periods. The convergence analysis revealed that the desertification risk for 1992–2000 tended to diverge with a positive convergence coefficient of 0.13 and converge over the 2000–2008 and 2008–2015 time periods with negative convergence coefficients of −0.534 and −0.268, respectively. According to the spatial convergence analysis, we found that the divergence patterns in northern Central Asia from 1992 to 2000 resulted from the effects of the Soviet Union collapse: cropland abandonment in northern Kazakhstan and rangeland abandonment in Tajikistan, Kyrgyzstan and eastern Kazakhstan. In contrast, most areas from 2000 to 2008 experienced increased sensitivity to desertification with the convergence pattern caused by decreased precipitation, especially in northern Central Asia. However, convergence patterns were found in most regions for 2008–2015 with regard to augmented precipitation, which resulted in decreased sensitivity to desertification. Moreover, the low sensitivity areas were more likely to converge under increased precipitation. In this region, the findings of our study suggested that spatial convergence and divergence acted as related predictors of climate change and human activities, respectively. Thus, the ESAI convergence analysis was considered to provide an early warning of potential desertification.

Identificación de estrategias empleadas para la optimización del uso eficiente del suelo.

La disminución de la aplicación de agroquímicos como una de las estrategias para que no llegue a afectar al suelo, es una prioridad donde el químico no debe ser aplicado a los cultivos para que no llegue a los productos que finalmente llegan al suelo, otra es la reforestación que ayuda a prevenir la escorrentía, a la protección de la capa superficial, a la acumulación de materia biodegradable que se convertirá en materia orgánica. Otra estrategia es lograr un contenido de materia orgánica que, por lo general, es más elevado en los suelos que se manejan orgánicamente, lo que indica no sólo una mayor fertilidad y estabilidad de los suelos orgánicos sino también una capacidad de retención de humedad más elevada, que reduce el riesgo de erosión y desertización. El material de menor costo es el más beneficioso para la protección del recurso, con el uso continuo se logran eficiencias de productividad, rentabilidad y economía, a diferencia de los más costosos que se ve la rentabilidad en el momento, pero cada vez es más la perdida de nutrientes hasta el punto de esterilizar el terreno. De acuerdo a los artículos estudiados, se logró determinar el aumento de Carbono Orgánico, aumento de la fertilidad, aumento de materia orgánica y disminución de temperatura, obtenido mediante los controles aplicados en las diferentes prácticas de mejoramiento de propiedades físicas y química del suelo.

Evaluation of indicators for desertification risk assessment in part of Thar Desert Region of Rajasthan using geospatial techniques

Desertification has emerged as a major economic, social and environmental problem in the western part of India. The best way of dealing with desertification is to take appropriate measures to arrest land degradation, especially in areas prone to desertification. This requires an early warning system for desertification based on scientific inputs. Hence, in the present study, an attempt has been made to develop a comprehensive model for the assessment of desertification risk in the Jodhpur district of Rajasthan, India, using 23 desertification indicators. Indicators including soil, climate, vegetation and socio-economic parameters were integrated into a GIS environment to get environmental sensitive areas (ESAs) to desertification. Desertification risk index (DRI) was calculated based on ESAs to desertification, the degree of land degradation and significant desertification indicators obtained from the stepwise multiple regression model. DRI was validated by using independent indicators such as soil organic matter content and cation exchange capacity. Multiple regression analysis shows that 16 indicators out of 23 were found to be significant for assessing desertification risk at a 99% confidence interval with \(R^{2}=0.83\). The proposed methodology provides a series of effective indicators that would help to identify where desertification is a current or potential problem, and what could be the actions to alleviate the problem over time.

An Iterative Approach for Knowledge Production in the Agricultural Systems and Insights for IS Development

There is motivation in many rural areas and communities to resolve the issues slowing achievement of a sustainable future, and to embrace the concept of the circular economy for agro-food systems. Increased consumption of resources is not an option and therefore best use must be made of capital, incorporating the “reduce, re-use, recycle” mantra. Research projects addressing sustainable land use can help to accomplish this aim, and the studies have demonstrated that stakeholders may be helped to understand and act on new knowledge especially if they are involved in more than one project. This is because they gain confidence to evaluate research ideas in the light of their own experience. In the Basilicata region of southern Italy there has been a succession of research projects since the 1990s to study the processes of land degradation and appropriate technologies to combat the risk of desertification. Most recently, the attitudes and perceptions of groups of cereal farmers included in both the DESIRE and REACT projects, or the REACT project alone, were compared using a Questionnaire, and the results highlighted the success of the iterative approach. This is an important finding, and can encourage understanding and action to overcome constraints and support the circular economy in agro-food systems.

Livestock Effects on Genetic Variation of Creosote Bushes in Patagonian Rangelands

SummaryGenetic diversity is the raw material for species’ persistence over time, providing the potential to survive stochastic events, as well as climate and/or human-induced environmental changes. Biodiversity in dry rangelands is decreasing due to intensification of livestock production, but its effects on the genetic diversity of the consumed biota have seldom been assessed. We examined livestock effects on the genetic diversity of two dominant creosote species of the Patagonian Monte Desert,Larrea divaricataandLarrea cuneifolia. We deployed competing hierarchical regression models to assess the relationship between genetic variation within natural populations as a function of increasing stocking rates on ten arid rangelands. These species exhibit similar levels and patterns of genetic structure, with high levels of both inbreeding and divergence among locations. We found that increased stocking reduces genetic variation and increases genetic subdivision between populations. Our results indicate that grazing pressures are impoverishing the gene pool of these dominant native species of the Monte Desert, decreasing the evolutionary potential of the primary plant producers and increasing the desertification risk for a vulnerable habitat. We highlight the importance of considering livestock as a major driver of genetic losses in dry rangelands under overgrazing pressure, especially given current forecasts of climate change.