Population growth, arable land and fresh water limits, and climate change have profound implications for the ability of agriculture to meet this century's demands for food, feed, fiber, and fuel ...while reducing the environmental impact of their production. Success depends on the acceptance and use of contemporary molecular techniques, as well as the increasing development of farming systems that use saline water and integrate nutrient flows.
Satellite-derived land cover maps play an important role in many applications, including monitoring of smallholder-dominated agricultural landscapes. New cloud-based computing platforms and satellite ...sensors offer opportunities for generating land cover maps designed to meet the spatial and temporal requirements of specific applications. Such maps can be a significant improvement compared to existing products, which tend to be coarser than 300m, are often not representative of areas with fast-paced land use change, and have a fixed set of cover classes. Here, we present two approaches for land cover classification using the Landsat archive within Google Earth Engine. Random forest classification was performed with (1) season-based composites, where median values of individual bands and vegetation indices were generated from four years for each of four seasons, and (2) metric-based composites, where different quantiles were computed for the entire four-year period. These approaches were tested for six land cover types spanning over 18,000 locations in Zambia, with ground “truth” determined by visual inspection of high-resolution imagery from Google Earth. The methods were trained on 30% of these points and tested on the remaining 70%, and results were also compared with existing land cover products. Overall accuracies of about 89% were achieved for the season- and metric-based approaches for individual classes, with 93% and 94% accuracy for distinguishing cropland from non-cropland. For the latter task, the existing Globeland30 dataset based on Landsat had much lower accuracies (around 77% on average), as did existing cover maps at coarser resolutions. Overall, the results support the use of either season or metric-based classification approaches. Both produce better results than those obtained from previous classifiers, which supports a general paradigm shift away from dependence on standard static products and towards custom generation of on-demand cover maps designed to fulfill the needs of each specific application.
•New cloud-computing platforms are opening new possibilities for land cover maps.•Generation of accurate maps with custom spatial/temporal resolution is now possible.•Two methods for land cover classification using Landsat composites are implemented.•Random forest classification was performed for both datasets.•Results were tested in Zambia and achieved 95% accuracy for mapping agriculture.
Models of yield responses to temperature change have often considered only changes in average temperature (
T
avg) with the implicit assumption that changes in the diurnal temperature range (DTR; the ...difference between daily maximum and minimum temperature) can safely be ignored. The goal of this study was to evaluate this assumption using a combination of historical datasets and climate model projections. Data on national crop yields for 1961–2002 in the ten leading producers of wheat (
Triticum spp.), rice (
Oryza spp.) and maize (
Zea mays) were combined with datasets on climate and crop locations to evaluate the empirical relationships between
T
avg, DTR and crop yields. In several rice and maize growing regions, including the two major nations for each crop, there was a clear negative response of yields to increased DTR. This finding reflects a nonlinear response of yields to temperature, which likely results from greater water and heat stress during hot days. In many other cases, the effects of DTR were not statistically significant, in part because correlations of DTR with other climate variables, and the relatively short length of the time series resulted in wide confidence intervals for the estimates.
To evaluate whether future changes in DTR are relevant to crop impact assessments, yield responses to projected changes in
T
avg and DTR by 2046–2065 from 11 climate models were estimated. The mean of climate model projections indicated an increase in DTR in most seasons and locations where wheat is grown, mixed projections for maize, and a general decrease in DTR for rice. These mean projections were associated with wide ranges that included zero in nearly all cases. The estimated impacts of DTR changes on yields were generally small (<5% change in yields) relative to the consistently negative impact of projected warming of
T
avg. However, DTR changes did significantly affect yield responses in several cases, such as in reducing US maize yields and increasing India rice yields. Because DTR projections tend to be positively correlated with
T
avg, estimates of yield changes for extreme warming were particularly affected by including DTR (up to 10%). Finally, based on the relatively poor performance of climate models in reproducing the magnitude of past DTR trends, it is possible that future DTR changes and associated yield responses will exceed the ranges considered here.
The quantity of land available to grow biofuel crops without affecting food prices or greenhouse gas (GHG) emissions from land conversion is limited. Therefore, bioenergy should maximize land-use ...efficiency when addressing transportation and climate change goals. Biomass could power either internal combustion or electric vehicles, but the relative land-use efficiency of these two energy pathways is not well quantified. Here, we show that bioelectricity outperforms ethanol across a range of feedstocks, conversion technologies, and vehicle classes. Bioelectricity produces an average of 81% more transportation kilometers and 108% more emissions offsets per unit area of cropland than does cellulosic ethanol. These results suggest that alternative bioenergy pathways have large differences in how efficiently they use the available land to achieve transportation and climate goals.
There is a widely recognized need in the scientific and policy communities for probabilistic estimates of climate change impacts, beyond simple scenario analysis. Here we propose a methodology to ...evaluate one major climate change impact ‐ changes in global average yields of wheat, maize, and barley by 2030 ‐ by a probabilistic approach that integrates uncertainties in climate change and crop yield responses to temperature, precipitation, and carbon dioxide. The resulting probability distributions, which are conditional on assuming the SRES A1B emission scenario and no agricultural adaptation, indicate expected changes of +1.6%, −14.1%, −1.8% for wheat, maize, and barley, with 95% probability intervals of (−4.1, +6.7), (−28.0, −4.3), (−11.0, 6.2) in percent of current yields, respectively. This fully probabilistic analysis aims at quantifying the range of plausible outcomes and allows us to gauge the relative importance of different sources of uncertainty.
The prevention of deforestation and promotion of afforestation have often been cited as strategies to slow global warming. Deforestation releases CO₂ to the atmosphere, which exerts a warming ...influence on Earth's climate. However, biophysical effects of deforestation, which include changes in land surface albedo, evapotranspiration, and cloud cover also affect climate. Here we present results from several large-scale deforestation experiments performed with a three-dimensional coupled global carbon-cycle and climate model. These simulations were performed by using a fully three-dimensional model representing physical and biogeochemical interactions among land, atmosphere, and ocean. We find that global-scale deforestation has a net cooling influence on Earth's climate, because the warming carbon-cycle effects of deforestation are overwhelmed by the net cooling associated with changes in albedo and evapotranspiration. Latitude-specific deforestation experiments indicate that afforestation projects in the tropics would be clearly beneficial in mitigating global-scale warming, but would be counterproductive if implemented at high latitudes and would offer only marginal benefits in temperate regions. Although these results question the efficacy of mid- and high-latitude afforestation projects for climate mitigation, forests remain environmentally valuable resources for many reasons unrelated to climate.
Biomass energy: the scale of the potential resource Field, Christopher B.; Campbell, J. Elliott; Lobell, David B.
Trends in ecology & evolution (Amsterdam),
02/2008, Letnik:
23, Številka:
2
Journal Article
Recenzirano
Increased production of biomass for energy has the potential to offset substantial use of fossil fuels, but it also has the potential to threaten conservation areas, pollute water resources and ...decrease food security. The net effect of biomass energy agriculture on climate could be either cooling or warming, depending on the crop, the technology for converting biomass into useable energy, and the difference in carbon stocks and reflectance of solar radiation between the biomass crop and the pre-existing vegetation. The area with the greatest potential for yielding biomass energy that reduces net warming and avoids competition with food production is land that was previously used for agriculture or pasture but that has been abandoned and not converted to forest or urban areas. At the global scale, potential above-ground plant growth on these abandoned lands has an energy content representing ∼5% of world primary energy consumption in 2006. The global potential for biomass energy production is large in absolute terms, but it is not enough to replace more than a few percent of current fossil fuel usage. Increasing biomass energy production beyond this level would probably reduce food security and exacerbate forcing of climate change.
It is well known that expansion of agriculture into natural ecosystems can have important climatic consequences, but changes occurring within existing croplands also have the potential to effect ...local and global climate. To better understand the impacts of cropland management practices, we used the NCAR CAM3 general circulation model coupled to a slab‐ocean model to simulate climate change under extreme scenarios of irrigation, tillage, and crop productivity. Compared to a control scenario, increases in irrigation and leaf area index and reductions in tillage all have a physical cooling effect by causing increases in planetary albedo. The cooling is most pronounced for irrigation, with simulated local cooling up to ∼8°C and global land surface cooling of 1.3°C. Increases in soil albedo through reduced tillage are found to have a global cooling effect (∼0.2°C) comparable to the biogeochemical cooling from reported carbon sequestration potentials. By identifying the impacts of extreme scenarios at local and global scales, this study effectively shows the importance of considering different aspects of crop management in the development of climate models, analysis of observed climate trends, and design of policy intended to mitigate climate change.
Food security will be increasingly challenged by climate change, natural resource degradation, and population growth. Wheat yields, in particular, have already stagnated in many regions and will be ...further affected by warming temperatures. Despite these challenges, wheat yields can be increased by improving management practices in regions with existing yield gaps. To identify the magnitude and causes of current yield gaps in India, one of the largest wheat producers globally, we produced 30 meter resolution yield maps from 2001 to 2015 across the Indo-Gangetic Plains (IGP), the nation's main wheat belt. Yield maps were derived using a new method that translates satellite vegetation indices to yield estimates using crop model simulations, bypassing the need for ground calibration data. This is one of the first attempts to apply this method to a smallholder agriculture system, where ground calibration data are rarely available. We find that yields can be increased by 11% on average and up to 32% in the eastern IGP by improving management to current best practices within a given district. Additionally, if current best practices from the highest-yielding state of Punjab are implemented in the eastern IGP, yields could increase by almost 110%. Considering the factors that most influence yields, later sow dates and warmer temperatures are most associated with low yields across the IGP. This suggests that strategies to reduce the negative effects of heat stress, like earlier sowing and planting heat-tolerant wheat varieties, are critical to increasing wheat yields in this globally-important agricultural region.
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