Showing papers in "Land Degradation & Development in 2021"
TL;DR: In this paper, the effects of grazing on plant and soil microbial communities may vary with grassland types and pasture seasons, which may be related to grazing-induced changes in available nitrogen, the ratio of available nitrogen to phosphorus and soil moisture.
Abstract: Synthetic responses of plant and soil microbial communities to grazing are indefinite in alpine grasslands on the Tibetan Plateau. Three paired, fenced and free grazing sites (alpine steppe meadow for winter pasture [ASMWP]; alpine steppe meadow for summer pasture [ASMSP]; alpine meadow for summer pasture [AMSP]) were used to compare how pasture season and grassland type affect responses of the α‐diversity and community composition of plant, soil bacteria and fungi to grazing. Cold‐season grazing reduced soil moisture by 12.10%, ammonium nitrogen (NH₄⁺‐N) by 53.71%, the ratio of available nitrogen to phosphorus by 64.11%, species richness (SR) by 31.4% and the Shannon by 11.9% of plant community on the ASMWP. Warm‐season grazing reduced nitrate nitrogen by 30.45%, SR of soil bacterial community by 21.98% on the ASMSP, but increased soil NH₄⁺‐N by 90.02% on the AMSP. Warm‐season grazing‐induced changes in plant community composition were mainly related to the composition of forbs on the AMSP. Grazing‐induced changes in the community composition of soil bacteria were mainly related to Proteobacteria, Acidobacteria, Bacteroidetes, Firmicutes and Verrucomicrobia on the ASMWP, and Proteobacteria, Acidobacteria, Bacteroidetes, Chloroflexi and TM7 on the ASMSP. Grazing‐induced changes in the community composition of soil fungi were mainly related to Ascomycota and Basidiomycota on the ASMWP, Basidiomycota on the ASMSP and Ascomycota on the AMSP. Therefore, the effects of grazing on plant and soil microbial communities may vary with grassland types and pasture seasons, which may be related to grazing‐induced changes in available nitrogen, the ratio of available nitrogen to phosphorus and soil moisture.
46 citations
TL;DR: In this article, the authors assess the determinants of grassland greenness on the Mongolian Plateau, one of the world's largest grassland biomes, which covers Mongolia and the province of Inner Mongolia in China.
Abstract: Changes in land management and climate alter vegetation dynamics, but the determinants of vegetation changes often remain elusive, especially in global drylands. Here we assess the determinants of grassland greenness on the Mongolian Plateau, one of the world's largest grassland biomes, which covers Mongolia and the province of Inner Mongolia in China. We use spatial panel regressions to quantify the impact of precipitation, temperature, radiation, and the intensity of livestock grazing on the normalized difference vegetation indices (NDVI) during the growing seasons from 1982 to 2015 at the county level. The results suggest that the Mongolian Plateau experienced vegetation greening from 1982 to 2015. Precipitation and animal density were the most influential factors contributing to higher NDVI on the grasslands of Inner Mongolia and Mongolia. Our results highlight the dominant effect of climate variability, and especially of the precipitation variability, on the grassland greenness in Mongolian drylands. The findings challenge the common belief that higher grazing pressure is the key driver for land degradation. The analysis exemplifies how representative wall‐to‐wall results for large areas can be attained from exploring space–time data and adds empirical insights to the puzzling relationship between grazing intensity and vegetation growth in dryland areas.
43 citations
TL;DR: In this article, the effects of pH on organic phosphorus mineralization and microbial diversity were investigated, and significant correlations were found between phosphorus components and alkaline phosphatase, phytase, and pH, between phoD and bpp gene abundance.
Abstract: Microbial diversity response to abiotic and biotic factors provides a sensitive indicator for estimating the potential stability and degradation of soils in agro‐ecosystems. To determine the effects of pH on organic phosphorus mineralization and microbial diversity, phospholipid fatty acid analysis, quantitative polymerase chain reaction (qPCR), and multiple ecological analyses were performed. Significant correlations were found between phosphorus components and alkaline phosphatase, phytase, and pH, between phoD and phytase, between bpp and alkaline phosphatase. phoD and bpp gene abundance presented significant linear relationships with soil pH and microbial diversity. Abiotic and biotic factors explained 25.1% of the total variation in organic phosphorus‐mineralizing‐related gene abundance, and abiotic factors accounted for 13.2% of the total variation in microbial community composition. Soil pH was the determinant, accounting for 11.2 and 7.7% of the total variation in organic phosphorus‐mineralizing‐related gene abundance and microbial community composition, respectively. Our results emphasized that the phosphorus components, pH, and organic phosphorus‐mineralizing‐related gene abundance were responsible for organic phosphorus‐mineralizing‐related enzyme activity. To our knowledge, this is the first report that pH is a key factor in directly and indirectly determining organic phosphorus‐mineralizing‐related gene abundance, which in turn affects microbial diversity, on a large spatial scale. The differences in phosphorus components, enzyme activity, organic phosphorus‐mineralizing‐related gene abundance, microbial community composition and diversity caused by pH might explain crop yield reduction.
42 citations
TL;DR: In this paper, the authors compared the impacts of cropland expansion on the quantity and quality of natural habitat in China between 2000 and 2015 with the impact of urban expansion and found that croplands expansion led to a loss of 35,811 km2 of natural habitats, which was twelve times as much as that from urban expansion.
Abstract: Natural habitat plays an important role in maintaining biodiversity. Both cropland expansion and urban expansion have an influence on natural habitat. However, it is not clear which one impacts more seriously on both the quantity and quality of natural habitat. This study compared the impacts of cropland expansion on the quantity and quality of natural habitat in China between 2000 and 2015 with the impacts of urban expansion. Map algebra in ArcGIS 10.6 was used to calculate the changes in the quantity of natural habitat, while the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Habitat Quality model was used to assess the changes in its quality. The results indicated that cropland expansion led to a loss of 35,811 km2 of natural habitat, which was twelve-times as much as that from urban expansion. Furthermore, the area of the heaviest habitat degradation due to cropland expansion was 9,530 km2, which was eight-times as much as that due to urban expansion. Noticeably, the greatest impacts of cropland expansion on natural habitat mostly occurred in areas where the ecological environment is already vulnerable (namely, the resistance and resilience of ecosystems in response to external interference are weak), whereas the impacts of urban expansion were much less in these areas. This study highlights that the impacts of cropland expansion on both the natural habitat loss and degradation far exceeded the impacts of urban expansion. It is necessary to improve cropland protection policies to guarantee food security while ensuring little or no harm to natural habitat.
40 citations
39 citations
TL;DR: In this paper, the authors determined the drought time-scale at which the vegetation photosynthesis response was highest based on the standardized precipitation evapotranspiration index (SPEI) and satellite SIF, and examined how the sensitivity of SIF signals from different ecosystems to drought varied along an aridity gradient in northern China.
Abstract: Satellite-based solar-induced chlorophyll fluorescence (SIF) has the potential for an early detection and accurate impact assessment of meteorological drought on vegetation photosynthesis However, how the response of satellite SIF to meteorological drought varies under different climatic conditions and biome types remains poorly understood In this study, we determined the drought time-scale at which the vegetation photosynthesis response was highest based on the standardized precipitation evapotranspiration index (SPEI) and satellite SIF, and examined how the sensitivity of SIF signals from different ecosystems to drought varied along an aridity gradient in northern China The results showed that spatial variability of the annual maximum SIF was constrained by wetness conditions and biome types Annual maximum SIF was positively correlated with SPEI in 579% of vegetated lands (P < 005) 348% of humid ecosystems were characterized by a significant SIF-SPEI correlation (P < 005) This percentage reached 44%, 714% and 862% for arid, sub-humid and semi-arid ecosystems, respectively The variation of SIF-SPEI correlations was a Gaussian function of the aridity index (AI), with the highest SIF-SPEI correlation appearing in the AI bin of 04 (037-046) The drivers for this pattern were vegetation composition and water availability The variation of SIF time-scales in response to SPEI was a linear function of the AI, but the slope varied among biomes To summarize with increasing aridity drought-induced declines in vegetation photosynthesis will be quicker and more significant
39 citations
TL;DR: In this article, the authors found preliminary evidence that there is an income threshold for the poverty trap and that raising incomes above this level may help residents of restoration areas escape the trap, and they showed that integrating ecological restoration with measures that provide a sustainable livelihood for residents of program areas can achieve the win-win goal of ecological restoration and poverty alleviation.
Abstract: Ecosystem degradation is a major cause of poverty, and poverty further aggravates ecosystem degradation through a feedback known as the 'poverty trap' that can prevent sustainable socioeconomic development in ecologically fragile areas. However, most ecological restoration programmes have failed to improve the lives of residents of the targeted areas because planners failed to understand the driving forces behind the poverty trap. Finding the threshold conditions for the poverty trap, which represent the conditions when the current state of a system changes to a new and inferior state, can help managers to avoid triggering the poverty trap in ecologically fragile areas. To avoid crossing the threshold, it's necessary to understand the driving mechanisms responsible for the poverty trap so that managers can break the vicious cycle that undermines the effectiveness of ecological restoration. China's ecological restoration has shown that integrating ecological restoration with measures that provide a sustainable livelihood for residents of programme areas can achieve the win–win goal of ecological restoration and poverty alleviation. We found preliminary evidence that there is an income threshold for the poverty trap, and that raising incomes above this level may help residents of restoration areas escape the trap. The examples described in this paper provide valuable guidance for other countries that must achieve similar goals.
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TL;DR: Wang et al. as mentioned in this paper investigated the effect of fractional vegetation cover (FVC) on soil salinization in sites different vegetation coverage in Jiefangzha Irrigation District in Inner Mongolia using satellite remote sensing.
Abstract: Soil salinization is a serious restrictive factor affecting sustainable agricultural development. In order to explore the effect of Fractional Vegetation Cover (FVC), we monitored soil salinization in sites different vegetation coverage in Jiefangzha Irrigation District in Inner Mongolia using satellite remote sensing. From May to August 2018, we carried out field sampling at different depths in each month, and calculated FVC and spectral covariates using GF‐1 satellite images in the corresponding sampling period. Based on the FVC division criteria for Inner Mongolia, we took the following steps: (a) setting up a control treatment A (the full data with undivided FVC, TA) and experimental treatments B (bare land, TB), C (mid‐low FVC, TC), D (mid FVC, TD) and E (high FVC, TE); (b) conducting the Best Subset Selection (BSS) for all spectral covariates at different depths of each treatment; and (c) constructing the Soil Salt Content (SSC) inversion models using partial least square regression (PLSR), Cubist, and Extreme Learning Machine (ELM). The results indicated that (a) classifying FVC could improve the stability and predictive ability of the models; (b) the performance of the three modeling methods were different (Cubist was the best, ELM next and PLSR the poorest); (c) the optimal inversion models for TB, TC and TE were constructed by Cubist at 0–20, 0–40 and 0–20 cm, and for TD was constructed by ELM at 0–60 cm, respectively. The results can provide references for soil salinization prevention and agricultural production in Jiefangzha Irrigation District and other areas with the similar vegetation cover.
TL;DR: In this paper, the authors use a technographic and participatory approach to understand how agricultural decision-making takes place including the knowledge construction, how agriculture is performed in a context of project intervention and how practice adaptation plays out in the context of interacting knowledge.
Abstract: The challenges of land degradation, climate change and food insecurity have led to the introduction of conservation agriculture (CA) aimed at enhancing yield and soil quality. Despite positive biophysical results, low adoption rates have been the focus of studies identifying constraints to wider uptake. While the adoption framework is popular for measuring agricultural innovation, objective adoption measurements remain problematic and do not recognize the contextual and dynamic decision‐making process. This study uses a technographic and participatory approach to move beyond the adoption framework and understand: (a) how agricultural decision‐making takes place including the knowledge construction, (b) how agriculture is performed in a context of project intervention and (c) how practice adaptation plays out in the context of interacting knowledge. Findings confirm that farmer decision‐making is dynamic, multidimensional and contextual. The common innovation diffusion model uses a theory of change, showcasing benefits through training lead farmers as community advocates and demonstration trials. Our study shows that the assumed model of technology transfer with reference to climate‐smart agriculture interventions is not as linear and effective as assumed previously. We introduce four lenses that contribute to better understanding complex innovation dynamics: (a) social dynamics and information transfer, (b) contextual costs and benefits, (c) experience and risk aversion, and (d) practice adaptation. Investments should build on existing knowledge and farming systems including a focus on the dynamic decision process to support the 'scaling up, scaling out and scaling deep' agenda for sustainable agricultural innovations.
TL;DR: In this paper, three machine learning models (i.e., Random Forest (RF), classification and regression tree (CART), and support vector machine (SVM) were used to evaluate the quality of rangeland in Firozkuh County, Iran.
Abstract: Increased use and increasing demands pose serious threats to rangelands. In this study, we document a pronounced downward trend in rangeland quality in the Alborz Mountains in Firozkuh County, Iran using analysis of three machine‐learning models (MLMs). A total of 1,147 transects were established to evaluate the rangeland quality trends from field data collected over a 7‐year period. Twelve independent conditional factors were analyzed for their relationships to range quality through three MLMs—Random Forest (RF), classification and regression tree (CART), and support vector machine (SVM). Based on assessments of the trained and validated models, RF, with a ROC‐AUC = 0.96, was determined to be the most robust. The results show that about 20% of the rangeland in the study area is in a critically degraded condition. Distances from roads and livestock density are the two factors most strongly linked to degradation. These results, in combination with field observations, indicate that the rangelands of the study area face two major challenges (overgrazing and early grazing) that require new strategies to mitigate and prevent damages. This study may provide important guidance for evaluating rangeland conditions in other regions of the world.
TL;DR: In this article, the impact of hydrophobic substances and pore structure on soil water repellency has been investigated in two long-term experimental fields with three treatments: conventional tillage, reduced tillage and no-tillage.
Abstract: Soil water repellency (SWR) has significant effects on soil degradation by changing some soil processes (e.g., carbon sequestration and soil erosion). Understanding the influence factors of SWR under conservation agriculture are playing a vital role in the sustainable development for improving soil quality. However, how soil pore structure influence on SWR remains unclear. We aim to assess the impact of hydrophobic substances and pore structure on SWR. Here we conducted two long‐term experimental fields with three treatments: conventional tillage (CT), reduced tillage (RT), and no‐tillage (NT). X‐ray tomography and the sorptivity method were used to measure soil pore structure and SWR, respectively. We found that soil organic carbon (SOC) and microbial biomass carbon (MBC) were higher in RT and NT treatments than in CT. MBC had significant influences on soil water sorptivity (Sw) and water repellency index (RI; p 0.05). MBC also showed a closer relationship with SWR than SOC in redundancy analysis. The RT and NT increased the porosity of 55–165 μm that had a positive relationship with ethanol sorptivity and RI (p < 0.05). Ethanol sorptivity increased with an increase in soil pore porosity and connectivity under RT and NT treatments. However, increasing the pore surface area could decrease Sw due to enhance contact area between hydrophobic substances and soil water. Overall, the RT and NT treatments increased the water repellency index, which was a result of the interactions between pore structure and hydrophobic substances.
TL;DR: In this article, the potential of those amendments to immobilize metal(loid)s and facilitate the establishment of a cover vegetation with willow (Salix dasyclados Wimm), a pot experiment was conducted using several carbon-based materials (biochar and activated carbon) and redmuds.
Abstract: Metal(loid) soil contamination is a widespread issue and its remediation a priority. Phytoremediation involves the use of plants to stabilize/extract metal(loid)s and prevent spreading of contaminants. Salicaceae are fast growing trees that can tolerate and accumulate metal(loid)s. Amendments may be needed to facilitate plant growth on the contaminated substrate. Biochar, the solid product of pyrolysis, can ameliorate soil properties and reduce metal(loid) bioavailability but showed low affinity toward arsenic. Redmud, a waste product, can immobilize arsenic and other metal(loid)s and improve soil conditions. To evaluate the potential of those amendments to immobilize metal(loid)s and facilitate the establishment of a cover vegetation with willow (Salix dasyclados Wimm.), a pot experiment was conducted using several carbon‐based materials (biochar and activated carbon) and redmuds. Two carbon‐based materials and two redmuds were selected for the pot experiment based on characteristics and sorption tests. A former mining soil was amended with these products and Salix dasyclados cuttings were planted. Biochar and one redmud improved growth of Salix dasyclados Wimm. Redmud, which is cheaper than biochar, performed equally well as the biochar at a lower application rate. It can therefore be concluded that the application of redmud, associated to Salix dasyclados, could be an option for the phytoremediation of an As and Pb contaminated soil. However, these results need to be evaluated on a bigger scale to assess the gain brought by the combination of redmud and Salix dasyclados on the contaminated area.
TL;DR: In this article, the authors analyze vegetation productivity underlying factors in order to assess land degradation and highlight the impact of definitions on its quantitative assessment, using Mozambique as case-study.
Abstract: Remote sensing observations such as normalized difference vegetation index (NDVI) trends can provide important insights into past and present land condition. However, they do not directly provide comprehensive information about our representation of land degradation and the processes at work. This study aimed to analyze vegetation productivity underlying factors in order to assess land degradation and to highlight the impact of definitions on its quantitative assessment, using Mozambique as case‐study. Land productivity change were first analyzed using NDVI time‐series (2000–2016), and a two‐step framework was then used to understand the main factors of these productivity changes. The impact of land degradation's definition was assessed based on four types of stakeholder, with different priorities in terms of ecosystem services. The results show that 25% of the country display a significant land productivity decrease, while only 3% display a land productivity increase. A large part of these land productivity changes (>61% of the decrease, and >98% of the increase) is directly assigned to human activities, such as native forest growth or tree plantations (for the increase), or forest degradation, deforestation and loss of grassland productivity (for the decrease). We showed that the fraction of degraded land varies according to stakeholders' definitions, ranging from 12% to 20% of the Country, much less than the 39% estimated by Tier 1 United Nations Convention to Combat Desertification. This study provides a sound methodological framework for assessing land degradation status that could help stakeholders to design national and locally relevant land degradation mitigation policies or programmes.