38 research outputs found
Simulated impacts of climate change on water use and yield of irrigated sugarcane in South Africa
Reliable predictions of climate change impacts on water use, irrigation requirements and yields of irrigated
sugarcane in South Africa (a water-scarce country) are necessary to plan adaptation strategies. Although previous
work has been done in this regard, methodologies and results vary considerably. The objectives were (1) to
estimate likely impacts of climate change on sugarcane yields, water use and irrigation demand at three irrigated
sugarcane production sites in South Africa (Malelane, Pongola and LaMercy) for current (1980–2010) and future
(2070–2100) climate scenarios, using an approach based on the Agricultural Model Intercomparison and
Improvement Project (AgMIP) protocols; and (2) to assess the suitability of this methodology for investigating
climate change impacts on sugarcane production.
Future climate datasets were generated using the Delta downscaling method and three Global Circulation Models
(GCMs) assuming atmospheric CO2 concentration [CO2] of 734 ppm (A2 emissions scenario). Yield and water use
were simulated using the DSSAT-Canegro v4.5 model.
Irrigated cane yields are expected to increase at all three sites (between 11 and 14%), primarily due to increased
interception of radiation as a result of accelerated canopy development. Evapotranspiration and irrigation
requirements increased by 11% due to increased canopy cover and evaporative demand. Sucrose yields are expected
to decline because of increased consumption of photo-assimilate for structural growth and maintenance
respiration. Crop responses in canopy development and yield formation differed markedly between the crop
cycles investigated.
Possible agronomic implications of these results include reduced weed control costs due to shortened periods of
partial canopy, a need for improved efficiency of irrigation to counter increased demands, and adjustments to
ripening and harvest practices to counter decreased cane quality and optimise productivity.
Although the Delta climate data downscaling method is considered robust, accurate and easily-understood,
it does not change the future number of rain-days per month. The impacts of this and other climate data simplifications
ought to be explored in future work. Shortcomings of the DSSAT-Canegro model include the simulated
responses of phenological development, photosynthesis and respiration processes to high temperatures, and
the disconnect between simulated biomass accumulation and expansive growth. Proposed methodology
refinements should improve the reliability of predicted climate change impacts on sugarcane yield.South African Sugarcane Research Institute (SASRI).http:// www.elsevier.com/ locate/agsy2016-10-31hb201
Interactions of Mean Climate Change and Climate Variability on Food Security Extremes
Recognizing that climate change will affect agricultural systems both through mean changes and through shifts in climate variability and associated extreme events, we present preliminary analyses of climate impacts from a network of 1137 crop modeling sites contributed to the AgMIP Coordinated Climate-Crop Modeling Project (C3MP). At each site sensitivity tests were run according to a common protocol, which enables the fitting of crop model emulators across a range of carbon dioxide, temperature, and water (CTW) changes. C3MP can elucidate several aspects of these changes and quantify crop responses across a wide diversity of farming systems. Here we test the hypothesis that climate change and variability interact in three main ways. First, mean climate changes can affect yields across an entire time period. Second, extreme events (when they do occur) may be more sensitive to climate changes than a year with normal climate. Third, mean climate changes can alter the likelihood of climate extremes, leading to more frequent seasons with anomalies outside of the expected conditions for which management was designed. In this way, shifts in climate variability can result in an increase or reduction of mean yield, as extreme climate events tend to have lower yield than years with normal climate.C3MP maize simulations across 126 farms reveal a clear indication and quantification (as response functions) of mean climate impacts on mean yield and clearly show that mean climate changes will directly affect the variability of yield. Yield reductions from increased climate variability are not as clear as crop models tend to be less sensitive to dangers on the cool and wet extremes of climate variability, likely underestimating losses from water-logging, floods, and frosts
Chapter 5: Food Security
The current food system (production, transport, processing, packaging, storage, retail, consumption, loss and waste) feeds the great majority of world population and supports the livelihoods of over 1 billion people. Since 1961, food supply per capita has increased more than 30%, accompanied by greater use of nitrogen fertilisers (increase of about 800%) and water resources for irrigation (increase of more than 100%). However, an estimated 821 million people are currently undernourished, 151 million children under five are stunted, 613 million women and girls aged 15 to 49 suffer from iron deficiency, and 2 billion adults are overweight or obese. The food system is under pressure from non-climate stressors (e.g., population and income growth, demand for animal-sourced products), and from climate change. These climate and non-climate stresses are impacting the four pillars of food security (availability, access, utilisation, and stability)
Outsourcing, Foreign Ownership, Exporting and Productivity: An Empirical Investigation with Plant Level Data
Phosphorus balance and use efficiency on 21 intensive grass-based dairy farms in the South of Ireland
AgMIP Climate Data and Scenarios for Integrated Assessment
Climate change presents a great challenge to the agricultural sector as changes in precipitation, temperature, humidity, and circulation patterns alter the climatic conditions upon which many agricultural systems rely. Projections of future climate conditions are inherently uncertain owing to a lack of clarity on how society will develop, policies that may be implemented to reduce greenhouse-gas (GHG) emissions, and complexities in modeling the atmosphere, ocean, land, cryosphere, and biosphere components of the climate system. Global climate models (GCMs) are based on well-established physics of each climate component that enable the models to project climate responses to changing GHG concentration scenarios (Stocker et al., 2013).The most recent iteration of the Coupled Model Intercomparison Project (CMIP5; Taylor et al., 2012) utilized representative concentration pathways (RCPs) to cover the range of plausible GHG concentrations out past the year 2100, with RCP8.5 representing an extreme scenario and RCP4.5 representing a lower concentrations scenario (Moss et al., 2010)
Representing Water Scarcity in Future Agricultural Assessments
Globally, irrigated agriculture is both essential for food production and the largest user of water. A major challenge for hydrologic and agricultural research communities is assessing the sustainability of irrigated croplands under climate variability and change. Simulations of irrigated croplands generally lack key interactions between water supply, water distribution, and agricultural water demand. In this article, we explore the critical interface between water resources and agriculture by motivating, developing, and illustrating the application of an integrated modeling framework to advance simulations of irrigated croplands. We motivate the framework by examining historical dynamics of irrigation water withdrawals in the United States and quantitatively reviewing previous modeling studies of irrigated croplands with a focus on representations of water supply, agricultural water demand, and impacts on crop yields when water demand exceeds water supply. We then describe the integrated modeling framework for simulating irrigated croplands, which links trends and scenarios with water supply, water allocation, and agricultural water demand. Finally, we provide examples of efforts that leverage the framework to improve simulations of irrigated croplands as well as identify opportunities for interventions that increase agricultural productivity, resiliency, and sustainability