Contributions of individual variation in temperature, solar radiation and precipitation to crop yield in the North China Plain, 1961–2003

An understanding of the relative impacts of the changes in climate variables on crop yield can help develop effective adaptation strategies to cope with climate change. This study was conducted to investigate the effects of the interannual variability and trends in temperature, solar radiation and precipitation during 1961–2003 on wheat and maize yields in a double cropping system at Beijing and Zhengzhou in the North China Plain (NCP), and to examine the relative contributions of each climate variable in isolation. 129 climate scenarios consisting of all the combinations of these climate variables were constructed. Each scenario contained 43 years of observed values of one variable, combined with values of the other two variables from each individual year repeated 43 times. The Agricultural Production Systems Simulator (APSIM) was used to simulate crop yields using the ensemble of generated climate scenarios. The results showed that the warming trend during the study period did not have significant impact on wheat yield potential at both sites, and only had significant negative impact on maize yield potential at Beijing. This is in contrast with previous results on effect of warming. The decreasing trend in solar radiation had a much greater impact on simulated yields of both wheat and maize crops, causing a significant reduction in potential yield of wheat and maize at Beijing. Although decreasing trends in rainfed yield of both simulated wheat and maize were found, the substantial interannual variability of precipitation made the trends less prominent

Climate-Agriculture-Modeling and Decision Tool (CAMDT): A software framework for climate risk management in agriculture

Seasonal climate forecasts (SCFs) have received a lot of attention for climate risk management in agriculture. The question is, how can we use SCFs for informing decisions in agriculture? SCFs are provided in formats not so conducive for decision-making. The commonly issued tercile probabilities of most likely rainfall categories i.e., below normal (BN), near normal (NN) and above normal (AN), are not easy to translate into metrics useful for decision support. Linking SCF with crop models is one way that can produce useful information for supporting strategic and tactical decisions in crop production e.g., crop choices, management practices, insurance, etc. Here, we developed a decision support system (DSS) tool, Climate-Agriculture-Modeling and Decision Tool (CAMDT), that aims to facilitate translations of probabilistic SCFs to crop responses that can help decision makers adjust crop and water management practices that may improve outcomes given the expected climatic condition of the growing season

Wheat Yield Functions for Analysis of Land-Use Change in China

CERES-Wheat, a dynamic process crop growth model is specified and validated for eight sites in the major wheat-growing regions of China. Crop model results are then used to test functional forms for yield response to nitrogen fertilizer, irrigation water, temperature, and precipitation. The resulting functions are designed to be used in a linked biophysical-economic model of land-use and land-cover change. Variables explaining a significant proportion of simulated yield variance are nitrogen, irrigation water, and precipitation; temperature was not a significant component of yield variation within the range of observed year-to-year variability except at the warmest site. The Mitscherlich-Baule function is found to be more appropriate than the quadratic function at most sites. Crop model simulations with a generic soil with median characteristics of the eight sites were compared to simulations with site-specific soils, providing an initial test of the sensitivity of the functional forms to soil specification. The use of the generic soil does not affect the results significantly; thus, the functions may be considered representative of agriculturally productive regions with similar climate in China under intensifying management conditions

Climate services and insurance: scaling climate smart agriculture

One of the main challenges of climate-smart agriculture (CSA) is finding ways to promote the adoption at scale (Editor’s note: 'scaling', 'at scale' or 'to scale' are used throughout this article to mean ‘scaling-out’) of CSA practices and technologies. Climate services and insurance can constitute a tool to scale CSA by providing an enabling environment that can support the adoption of CSA practices while protecting against the impacts of climate extremes. By using a definition of climate services which includes the production, translation, transfer, and use of climate knowledge and information in climate-informed decision-making and climatesmart policy and planning, this paper aims to discuss how climate services and insurance can bring CSA to scale. Three case studies are presented. It is recognised that understanding the knowledge networks through which information flows, and affects the use of climate information, is critical for promoting CSA at scale

On the use and misuse of climate change projections in international development

Climate resilience is increasingly prioritized by international development agencies and national governments. However, current approaches to informing communities of future climate risk are problematic. The predominant focus on end-of-century projections neglects more pressing development concerns, which relate to the management of shorter-term risks and climate variability, and constitutes a substantial opportunity cost for the limited financial and human resources available to tackle development challenges. When a long-term view genuinely is relevant to decisionmaking, much of the information available is not fit for purpose. Climate model projections are able to capture many aspects of the climate system and so can be relied upon to guide mitigation plans and broad adaptation strategies, but the use of these models to guide local, practical adaptation actions is unwarranted. Climate models are unable to represent future conditions at the degree of spatial, temporal, and probabilistic precision with which projections are often provided, which gives a false impression of confidence to users of climate change information. In this article, we outline these issues, review their history, and provide a set of practical steps for both the development and climate scientist communities to consider. Solutions to mobilize the best available science include a focus on decision-relevant timescales, an increased role for model evaluation and expert judgment and the integration of climate variability into climate change service

Assessing methods for developing crop forecasting in the Iberian Peninsula

Seasonal climate prediction may allow predicting crop yield to reduce the vulnerability of agricultural production to climate variability and its extremes. It has been already demonstrated that seasonal climate predictions at European (or Iberian) scale from ensembles of global coupled climate models have some skill (Palmer et al., 2004)

ADAPTATION OF AGRICULTURAL PRODUCTION SYSTEMS TO CLIMATE VARIABILITY AND CLIMATE CHANGE: LESSONS LEARNED AND PROPOSED RESEARCH APPROACH

Societies, cultures and economies in the world's history have successfully developed by mastering their abilities to adapt to climatic conditions. However, the last decades have been characterized by a dramatic growth in human population that is imposing unprecedented pressures on natural ecosystems and on existing agricultural production systems. In addition to this pressure, societies are expected to face changes in climate at also unprecedented rate. Agricultural production systems will require effective adaptive strategies to overcome these expected pressures in the immediate future. Against the very unfavorable economic scenarios of the last decades, farmers around the world have been struggling to maintain their income by continuously trying to increase yields in their production systems. But these higher productive systems have often become more vulnerable to climate variability and climate change. These existing pressures demand the development and implementation of methodologies to address issues of vulnerability to climate for assisting farmers and policy makers of the agricultural sector to further develop their adaptive capacity with improved planning and better management decisions. This article will focus in the mixed livestock/crops production systems of South America and will discuss the lessons learned in the research on the climate variability (CV) and climate change (CC) interactions with agricultural production systems. It also discusses a path for building on such experiences to establish activities (research and capacity building) in the next generation of studies on climate variability and climate change

Interannual-to-multidecadal Hydroclimate Variability and its Sectoral Impacts in northeastern Argentina

This study examines the joint variability of pre- cipitation, river streamflow and temperature over northeast- ern Argentina; advances the understanding of their links with global SST forcing; and discusses their impacts on water re- sources, agriculture and human settlements. The leading pat- terns of variability, and their nonlinear trends and cycles are identified by means of a principal component analysis (PCA)complemented with a singular spectrum analysis (SSA). In- terannual hydroclimatic variability centers on two broad fre- quency bands: one of 2.5?6.5 years corresponding to El Niño Southern Oscillation (ENSO) periodicities and the second of about 9 years. The higher frequencies of the precipita- tion variability (2.5?4 years) favored extreme events after 2000, even during moderate extreme phases of the ENSO. Minimum temperature is correlated with ENSO with a main frequency close to 3 years. Maximum temperature time se- ries correlate well with SST variability over the South At- lantic, Indian and Pacific oceans with a 9-year frequency. Interdecadal variability is characterized by low-frequency trends and multidecadal oscillations that have induced a tran- sition from dryer and cooler climate to wetter and warmer decades starting in the mid-twentieth century. The Paraná River streamflow is influenced by North and South Atlantic SSTs with bidecadal periodicities.The hydroclimate variability at all timescales had signif- icant sectoral impacts. Frequent wet events between 1970 and 2005 favored floods that affected agricultural and live- stock productivity and forced population displacements. On the other hand, agricultural droughts resulted in soil mois- ture deficits that affected crops at critical growth stages. Hy-drological droughts affected surface water resources, caus- ing water and food scarcity and stressing the capacity for hydropower generation. Lastly, increases in minimum tem- perature reduced wheat and barley yields.Fil: Lovino, Miguel Angel. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Müller, Omar Vicente. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; ArgentinaFil: Müller, Gabriela V.. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; ArgentinaFil: Sgroi, Leandro Carlos. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas; ArgentinaFil: Baethgen, Walter. Columbia University; Estados Unido