Greenhouse gas budgets of crop production : current and likely future trends

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A Review of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data

Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes, and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness, and growing season length) often termed “land surface phenology,” as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data-processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multiscale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams, and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization

Adaptation and Invention during the Spread of Agriculture to Southwest China

The spread of an agricultural lifestyle played a crucial role in the development of social complexity and in defining trajectories of human history. This dissertation presents the results of research into how agricultural strategies were modified during the spread of agriculture into Southwest China. By incorporating advances from the fields of plant biology and ecological niche modeling into archaeological research, this dissertation addresses how humans adapted their agricultural strategies or invented appropriate technologies to deal with the challenges presented by the myriad of ecological niches in southwest China. This dissertation uses ecological niche modeling to examine the options and constraints associated with practicing different types of agriculture in the specific ecological niches of southwest China. The predictions made by these models are then tested against archaeobotanical data from a series of sites from across the region. This approach allows one to understand how the spread of agriculture took place in its particular social and economic contexts.Anthropolog

QUANTIFYING THE EFFECT OF SPRINKLER IRRIGATION ON GREENHOUSE GAS EMISSIONS

A declining area of arable land has heightened pressure to increase food production for a growing world population. The potential to enhance food production by increasing the number of irrigated farms is high on the Canadian Prairies. However, expansion of irrigated farms will likely influence agricultural greenhouse gas (GHG) emissions. Quantification and comparison of energy partitioning of surface energy fluxes, crop microclimatic modification, soil environment variation, and GHG emissions from irrigated and non-irrigated fields in the Canadian Prairies are explored in this research. The observed field data were also used to check the suitability of a regional version of a process-based GHG simulation model, the Denitrification-Decomposition (CDN-DNDC) model. It was found that irrigation alters energy partitioning noticeably, which promoted crop microclimatic modification leading to reduced vapor pressure deficit and canopy temperature. However, despite a much smaller proportion of the net radiation in non-irrigated systems being consumed by evaporation, the dryland fields did not exhibit markedly warmer soil temperatures. Soil water was found as the critical factor in influencing soil GHG emissions, and availability of soil nutrient was the dominant factor in soil N2O emissions from irrigated systems. The performance of the CDN-DNDC model to predict soil moisture under irrigation conditions during growing season was good, which allowed the model to be used to simulate different irrigated conditions. The CDN-DNDC model simulated and measured N2O emissions from irrigated and non-irrigated fields were compared, indicating that this model is suitable to assess N2O emissions from different management systems under irrigated conditions in the Canadian Prairies. According to the CDN-DNDC model, a future increase in irrigated fields will increase N2O emission. However, when crop yield is taken into consideration, there is actually a lower mean annual nitrous oxide intensity in the irrigated field. The performance of the CDN-DNDC model was less accurate in predicting N2O emission and soil water after the spring thaw, and in predicting soil temperature with respect to irrigation. This research provides a first look at energy partitioning, crop microclimatic, and soil environment modification, as well as GHG dynamics from irrigated agricultural fields in the Canadian Prairies