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Climate change affects the timing of phenological events, such as the start, end, and length of the growing season of vegetation. A better understanding of how the phenology responded to climatic determinants is important in order to better anticipate future climate-ecosystem interactions. We examined the changes of three phenological events for the Mongolian Plateau and their climatic determinants. To do so, we derived three phenological metrics from remotely sensed vegetation indices and associated these with climate data for the period of 1982 to 2011. The results suggested that the start of the growing season advanced by 0.10 days yr-1, the end was delayed by 0.11 days yr-1, and the length of the growing season expanded by 6.3 days during the period from 1982 to 2011. The delayed end and extended length of the growing season were observed consistently in grassland, forest, and shrubland, while the earlier start was only observed in grassland. Partial correlation analysis between the phenological events and the climate variables revealed that higher temperature was associated with an earlier start of the growing season, and both temperature and precipitation contributed to the later ending. Overall, our findings suggest that climate change will substantially alter the vegetation phenology in the grasslands of the Mongolian Plateau, and likely also in biomes with similar environmental conditions, such as other semi-arid steppe regions.

AbstractChina is under pressure to improve its agricultural productivity to keep up with the demands of a growing population with increasingly resource‐intensive diets. This productivity improvement must occur against a backdrop of carbon intensity reduction targets, and a highly fragmented, nutrient‐inefficient farming system. Moreover, the Chinese government increasingly recognizes the need to rationalize the management of the 800 million tonnes of agricultural crop straw that China produces each year, up to 40% of which is burned in‐field as a waste. Biochar produced from these residues and applied to land could contribute to China's agricultural productivity, resource use efficiency and carbon reduction goals. However competing uses for China's straw residues are rapidly emerging, particularly from bioenergy generation. Therefore it is important to understand the relative economic viability and carbon abatement potential of directing agricultural residues to biochar rather than bioenergy. Using cost‐benefit analysis (CBA) and life‐cycle analysis (LCA), this paper therefore compares the economic viability and carbon abatement potential of biochar production via pyrolysis, with that of bioenergy production via briquetting and gasification. Straw reincorporation and in‐field straw burning are used as baseline scenarios. We find that briquetting straw for heat energy is the most cost‐effective carbon abatement technology, requiring a subsidy of $7 MgCO2e−1 abated. However China's current bioelectricity subsidy scheme makes gasification (NPV $12.6 million) more financially attractive for investors than both briquetting (NPV $7.34 million), and pyrolysis ($−1.84 million). The direct carbon abatement potential of pyrolysis (1.06 MgCO2e per odt straw) is also lower than that of briquetting (1.35 MgCO2e per odt straw) and gasification (1.16 MgCO2e per odt straw). However indirect carbon abatement processes arising from biochar application could significantly improve the carbon abatement potential of the pyrolysis scenario. Likewise, increasing the agronomic value of biochar is essential for the pyrolysis scenario to compete as an economically viable, cost‐effective mitigation technology.

Exploring the sensitivity of rural households’ livelihood strategies to livelihood capital is of great significance for improving rural households’ livelihood levels. This paper selects 23 livelihood capital measurement indicators and conducts an in-depth survey of rural households. In addition, the entropy method and a weighted comprehensive model are used to explore the basic characteristics of rural households’ livelihood capital in the upper reaches of the Min River, China, in 2017. Furthermore, econometric models are used to analyze the sensitivity of rural households’ livelihood strategies to livelihood capital. As indicated from the research, the livelihood capital levels of different types of rural households in the study area are not equivalent. The types of rural households with different livelihood strategies can be ordered in terms of quantity as follows: non-agricultural type > non-agricultural dominant type > agricultural dominant type > pure agricultural type. Livelihood strategies have different sensitivities to different livelihood capital measurement indicators. Among these indicators, cash income, the number of relatives and friends available for financial assistance, and the number of civil servants have positive effects on the livelihood strategy selection of non-agricultural dominant rural households and non-agricultural rural households. However, the average age of laborers, area of cultivated land and gardens, number of livestock and poultry, and present value of production tools have negative effects. These evaluation results can provide a scientific decision-making basis for the formulation of poverty alleviation policies by relevant government departments.

Promoting biodiesel industrialization is not only an important measure in addressing the energy crisis and global warming but is also a driver for industrial restructuring and rural development. To...

Facing the non-point source pollution caused by fertilizer loss, study on the agricultural factors that have an influence on fertilizer reduction could provide a basis for the implementation of policies on fertilizer reduction control. Based on the panel data during 2002-2016 with cumulative contribution model and Log-Mean Divisia Index (LMDI), this paper innovatively comes up with reduction driving functions (including agricultural scale, intensification, fertilizer utilization, and labor productivity), studies the cumulative contribution rate and driving factors of fertilizer reduction in county units of Zhejiang Province. Main conclusions are: (1) 37 of 63 county units (58.73%) showed decreased in fertilizer application; top five county units contributed 50+%; top county units that contribute to fertilizer reduction are in plain area in Northern Zhejiang, coastal area in Eastern and Southern Zhejiang, valley and basin area in Western and Southern Zhejiang where arable land is relatively even. (2) Fertilizer application intensity (fertilizer application per unit area of land cultivation) in Zhejiang Province increased by 18.67%, but there are only 15 county units where fertilizer application intensity is reduced. In general, fertilizer reduction in Zhejiang Province kept developing steadily. (3) Factors (fertilizer efficiency, intensification and agricultural scale) drive fertilizer reduction while labor productivity triggers the growth in application intensity. In Hangzhou, Ningbo and Shaoxing in Northern and Eastern Zhejiang, the above factors all have a prominent driving effect, but labor productivity also promotes fertilizer increment. In the end, this paper proposes policies on fertilizer reduction control in terms of efficiency, inputs and compensations.

Understanding the relationship between climate and crop productivity is a key component of projections of future food production, and hence assessments of food security. Climate models and crop yield datasets have errors, but the effects of these errors on regional scale crop models is not well categorized and understood. In this study we compare the effect of synthetic errors in temperature and precipitation observations on the hindcast skill of a process-based crop model and a statistical crop model. We find that errors in temperature data have a significantly stronger influence on both models than errors in precipitation. We also identify key differences in the responses of these models to different types of input data error. Statistical and process-based model responses differ depending on whether synthetic errors are overestimates or underestimates. We also investigate the impact of crop yield calibration data on model skill for both models, using datasets of yield at three different spatial scales. Whilst important for both models, the statistical model is more strongly influenced by crop yield scale than the process-based crop model. However, our results question the value of high resolution yield data for improving the skill of crop models; we find a focus on accuracy to be more likely to be valuable. For both crop models, and for all three spatial scales of yield calibration data, we found that model skill is greatest where growing area is above 10-15 %. Thus information on area harvested would appear to be a priority for data collection efforts. These results are important for three reasons. First, understanding how different crop models rely on different characteristics of temperature, precipitation and crop yield data allows us to match the model type to the available data. Second, we can prioritize where improvements in climate and crop yield data should be directed. Third, as better climate and crop yield data becomes available, we can predict how crop model skill should improve.

Resource-use efficiency and crop yield are significant factors in the management of agricultural greenhouse. Appropriate modeling methods effectively improve the control performance and efficiency of the greenhouse system and are conducive to the design of water and energy-saving strategies. Meanwhile, the extreme environment could be forecasted in advance, which reduces pests and diseases as well as provides high-quality food. Accordingly, the interest of the scientific community in greenhouse modeling and optimizing has grown considerably. The objective of this work is to provide guidance and insight into the topic by reviewing 73 representative articles and to further support cleaner and sustainable crop production. Compared to the existing literature review, this work details the approaches to improve the greenhouse model in the aspects of parameter identification, structure and process optimization, and multi-model integration to better model complex greenhouse system. Furthermore, a statistical study has been carried out to summarize popular technology and future trends. It was found that dynamic and neural network techniques are most commonly used to establish the greenhouse model and the heuristic algorithm is popular to improve the accuracy and generalization ability of the model. Notably, deep learning, the combination of “knowledge” and “data”, and coupling between the greenhouse system elements have been considered as future valuable development.

So far, various studies assessed global biomass potentials and came up with widely varying results. Existing potential estimates range from 0 EJ/a up to more than 1,550 EJ/a which corresponds to about three times the current global primary energy consumption. This paper provides an overview of the available research on bioenergy potentials and reviews the different assessments qualitative way with the objective to interpret previous research in an integrated way. In the context of this paper we understand bioenergy as energy from biomass sources including energy crops, residues, byproducts and wastes from agriculture, forestry, food production and waste management. In this review special attention was paid to the difference between residue and energy potentials, land availability estimates, and the geographical resolution of existing potential estimates. The majority of studies concentrate on energy crop potentials retrieved from surplus agricultural land and only few publications assess global potentials separated by different world regions. It results that land allocated to the exclusive production of energy crops varies from 0 to 7,000 ha, depending on land category and scenario assumptions. Only a small number of available potential assessments consider residue potentials as well as energy crop potentials from degraded land. Future energy crop potentials are assumed to vary in the mean from 200 to 600 EJ/yr. In contrast residue potentials are expected to contribute between 62 and 325 EJ/yr. The highest potentials are assigned to Asia, Africa and South America while Europe, North America and the Pacific region contribute minor parts to the global potential.

AbstractVineyard pruning is a potential lignocellulosic feedstock for bioethanol production from agricultural woody residues, due to its high sugar content and ready availability in whole Europe. Ethanol production from lignocellulosic biomass requires a pretreatment step and then enzymatic hydrolysis process to release sugars for fermentation to ethanol. In this work, alkaline pretreatment with NaOH on vineyard residues was investigated on laboratory scale. The raw material was firstly characterized in order to determine cellulose, hemicellulose and lignin content, using the standard laboratory analytical procedures for biomass analysis provided by the National Renewable Energy Laboratory (NREL). Then, based on Response Surface Methodology tool (RSM), a Box-Behnken model was chosen to define a design of experiments (DoE) in terms of the three independent variables that influence the process: the NaOH concentration, the reaction time and the temperature of the pretreatment process. According to the design, 15 samples of raw material were submitted to alkaline pretreatment and subsequent enzymatic hydrolysis and the glucose yield from whole process was calculated. The statistical optimization was carried out with Minitab 17 software, in order to determine the best operative pretreatment condition by maximizing the glucose yield from enzymatic hydrolysis.Results show that the best glucose yield was obtained at the highest sodium hydroxide concentration and temperature of the reaction; this condition allowed to achieve very significant glucose yield thanks to lignin removal performed with the pretreatment.

AbstractIran has experienced a drastic increase in water scarcity in the last decades. The main driver has been the substantial unsustainable water consumption of the agricultural sector. This study quantifies the spatiotemporal dynamics of Iran’s hydrometeorological water availability, land cover, and vegetation growth and evaluates their interrelations with a special focus on agricultural vegetation developments. It analyzes globally available reanalysis climate data and satellite time series data and products, allowing a country-wide investigation of recent 20+ years at detailed spatial and temporal scales. The results reveal a wide-spread agricultural expansion (27,000 km$$^2$$ 2 ) and a significant cultivation intensification (48,000 km$$^2$$ 2 ). At the same time, we observe a substantial decline in total water storage that is not represented by a decrease of meteorological water input, confirming an unsustainable use of groundwater mainly for agricultural irrigation. As consequence of water scarcity, we identify agricultural areas with a loss or reduction of vegetation growth (10,000 km$$^2$$ 2 ), especially in irrigated agricultural areas under (hyper-)arid conditions. In Iran’s natural biomes, the results show declining trends in vegetation growth and land cover degradation from sparse vegetation to barren land in 40,000 km$$^2$$ 2 , mainly along the western plains and foothills of the Zagros Mountains, and at the same time wide-spread greening trends, particularly in regions of higher altitudes. Overall, the findings provide detailed insights in vegetation-related causes and consequences of Iran’s anthropogenic drought and can support sustainable management plans for Iran or other semi-arid regions worldwide, often facing similar conditions.