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To clarify the current situation, hotspots, and development trends in the field of rice and climate change topic research, a massive literature dataset were analyzed from the Web of Science database by bibliometric method. The research theme was chosen given the continuous increase of studies related to climatic changes and their consequences to rice. Based on the Web of Science core database, this study analyzed 4170 papers in the field of rice and climate change topic research from 1990 to July 2022, which include 86 highly cited papers and 3 hot papers. Papers were mainly written in English (4157, 99.688%), from 16,363 authors, 4017 organizations, and 129 countries/territories, published in 841 journals and seven book series. The top five Journals are Science of the Total Environment (136, 3.261%), Sustainability (89, 2.134%), Agronomy Basel (81, 1.942%), Agricultural and Forest Meteorology (77, 1.847%), and Climatic Change (74, 1.775%), each published more than 74 papers. Top five countries and regions of People's Republic of China, the USA, India, Australia, and Japan were the major article contributors, each published more than 360 papers. Top five organizations of Chinese Acad Sci, Nanjing Agr Univ, Univ Chinese Acad Sci, Chinese Acad Agr Sci, and Int Rice Res Inst (IRRI) were popular based on contribution of articles more than 133 papers each. Among the all authors, top five authors were Tao Fulu, Pan Genxing, Zhang Zhao, Hasegawa Toshihiro, and Iizumi Toshichika, each published more than thirty papers. All keywords were separated into eight clusters for different research topics. Visualizations offer exploratory information on the current state in a scientific field or discipline as well as indicate possible developments in the future. The results will help researchers clarify the current situation in rice and climate change adaptation science but also provide guidance for future research. This work is also useful for student identifying graduate schools and researchers selecting journals. [ABSTRACT FROM AUTHOR]

This paper explores the under-appreciated role of evolving temperature variability in crop production in the overall relationship between crop production and climate change. Prior studies can be characterized by two broad approaches: estimation of the impact of climate change over some observed period and forecasting crop production levels under feasible climate scenarios. A standard method for predicting crop production under climate change involves regressing logged crop production on quadratic weather variables and region-level fixed effects (Lobell and Burke, I Agricultural and Forest Meteorology i , 2010). [Extracted from the article]

This paper analyzes the effects of climatic variables on food crop yields at the prefectural level from 1996 to 2016 in Togo. Using the panel‐corrected standard errors method and panel data from departments in charge of agricultural statistics and meteorology, the results show that meteorological variables have various effects on food crops yields, but the negative effects are dominant. In addition, adaptation strategies through agricultural land reallocation have not enabled farmers to improve food crops yields. There is an urgent need for public authorities to implement actions to strengthen farmers' resilience through practice adoption and cultural innovations for adaptation to climate change. [ABSTRACT FROM AUTHOR]

This paper describes the development of the agrometeorology science in Greece from the mid-1970s, when the first "Agrometeorology and Hydrology Division" was established within the Hellenic National Meteorological Service (HNMS), until today. To do so, a series of representative agrometeorology studies conducted mainly by HNMS is discussed in chronological order. Through these studies, one can observe the gradual integration of the fields of agriculture and meteorology and the formulation of a proper multidisciplinary agrometeorology science in Greece, as well as the constant progress in the relative scientific production. Finally, perceiving climate change as a driving force for the conduction of long-term planning for the agricultural sector in Greece, the paper proposes as priorities for the future development of agrometeorology the provision of an adequate network of agro-meteorological stations, the exploitation of satellite information, the reinforcement of the communication between scientists and farmers, and the strengthening of the role of HNMS as a channel for ensuring mutual understanding. [ABSTRACT FROM AUTHOR]

Agriculture is the one of the most important priorities and economic activities in developing countries. Climate change, which is already affecting the society at a global level, has an impact on the increase in magnitude and frequency of severe weather events. As such, both agriculture and industry are affected by climate change and their revenues decrease, thus affecting the entire economic cycle. The aim of this paper is to present theoretical considerations regarding climate change induced severe weather events and to highlight some practical techniques used in operational meteorology in order to improve the forecast of severe weather events. By analysing the morphology and dynamics of thunderstorms, by applying the correct parametrization in weather forecast models, nowcasting and by issuing on time weather alerts, financial losses may be limited. Throughout this paper, the parametrization of convective cells will be described, operational forecasting techniques will be presented and the application of ingredient-based forecasting methodology will be discussed. Severe weather associated hazards like hail, heavy rainfall, damaging winds and even frost will be analysed in order to establish a local climatology of these phenomena. With the aid of Weather Surveillance Doppler Radar (WSDR) and by associating some instability convection indices with synoptic charts, the agriculture-related severe weather events can be identified and their formation pattern can be established. Further on, by establishing an analysis methodology and by compelling all available data, weather-related hazard maps are to be compelled and are to be used by agrometeorology forecasters in order to seek viable solutions. The Western part of Romania is characterized as having a moderatecontinental climate, with hot influences from the Southern part of Europe and with humid advections from the South-Western part of Europe. In combination with cold advections from the Northern Europe, both summer as well as winter specific agriculture-related severe weather events are present [ABSTRACT FROM AUTHOR]

This paper examines the impact of climate change on the decision of farmers to engage or not to engage in livestock activities and also on the choice of different livestock species in Kenya. To this end, cross-sectional household level data supplemented by long-term averages of climate data are used. The probit model is employed to derive the response of the probability of engaging in livestock activities to climate change. Probit and multivariate probit methods are employed to model the choice of different livestock species. Atmosphere–ocean global circulation models are used to project the impact of different climate scenarios on the probability of engaging in livestock activities and also of adopting different livestock species according to variations in climate. The results suggest that farmers adapt livestock management decisions to climate change. At low levels of temperature increase, the probability of engaging in livestock activities falls, but at higher levels of climate change, the probability rises. The results further show that as it gets hotter, farmers change their livestock choices from dairy cattle and sheep to beef cattle and goats. [ABSTRACT FROM AUTHOR]

Abstract In agrometeorology and management of meteorology related natural resources, many traditional methods and indigenous technologies are still in use or being revived for managing low external inputs sustainable agriculture (LEISA) under conditions of climate variability. This paper starts with the introduction of an end-to-end climate information build up and transfer system in agrometeorology, in which the use of such methods and technologies must be seen to operate. It then reviews the options that LEISA farmers have in risk management of agrometeorological and agroclimatological calamities. This is based on the role that the pertinent meteorological/climatological parameters and phenomena play as limiting factors in agricultural production and the expectations on their variability. Subsequently, local case studies are given as examples of preparedness strategies to cope with i). variable water/moisture flows, including mechanical impacts of rain and/or hail, ii). variable temperature and heat flows, including fires, and iii). fitting cropping periods to the varying seasons, everywhere including related phenomena as appropriate. The paper ends with a series of important additional considerations without which the indicated strategies cannot be successful on a larger scale and in the long run. [ABSTRACT FROM AUTHOR]

Climate change and extreme weather events are threatening agricultural production worldwide. The anticipated increase in atmospheric temperature may reduce the potential yield of cultivated crops. Agroforestry is regarded as a climate-resilient system that is profitable, sustainable, and adaptable, and has strong potential to sequester atmospheric carbon. Agroforestry practices enhance agroecosystems' resilience against adverse weather conditions via moderating extreme temperature fluctuations, provisioning buffers during heavy rainfall events, mitigating drought periods, and safeguarding land resources from cyclones and tsunamis-type events. Therefore, it was essential to comprehensively analyze and discuss the role of agroforestry in providing resilience during extreme weather situations. We hypothesized that integrating trees in to the agro-ecosystems could increase the resilience of crops against extreme weather events. The available literature showed that the over-story tree shade moderates the severe temperature (2-4°C) effects on understory crops, particularly in the wheat and coffee-based agroforestry as well as in the forage and livestock-based silvipasture systems. Studies have shown that intense rainstorms can harm agricultural production (40-70%) and cause waterlogging. The farmlands with agroforestry have been reported to be more resilient to heavy rainfall because of the decrease in runoff (20-50%) and increase in soil water infiltration. Studies have also suggested that drought-induced low rainfall damages many crops, but integrating trees can improve microclimate and maintain crop yield by providing shade, windshield, and prolonged soil moisture retention. The meta-analysis revealed that tree shelterbelts could mitigate the effects of high water and wind speeds associated with cyclones and tsunamis by creating a vegetation bio-shield along the coastlines. In general, existing literature indicates that implementing and designing agroforestry practices increases resilience of agronomic crops to extreme weather conditions increasing crop yield by 5-15%. Moreover, despite its widely recognized advantages in terms of resilience to extreme weather, the systematic documentation of agroforestry advantages is currently insufficient on a global scale. Consequently, we provide a synthesis of the existing data and its analysis to draw reasonable conclusions that can aid in the development of suitable strategies to achieve the worldwide goal of adapting to and mitigating the adverse impacts of climate change. [ABSTRACT FROM AUTHOR]

Climate variability and change are recognised as a major threat for West African agriculture, particularly for smallholder farmers. Moreover, population pressure, poverty, and food insecurity, are worsening the vulnerability of production systems to climate risks. Application of Climate Services in agriculture, specifically Agrometeorological Services, is acknowledged as a valuable innovation to assist decision-making and develop farmers' specific adaptive capacities. In West Africa, the World Meteorological Organisation and National Meteorological Services deployed considerable efforts in the development of Agrometeorological Services. Nevertheless, the impacts of such services on West African farming communities are still largely unknown. This paper aims to delineate the added value of agrometeorological services for farmers within the Agriculture Innovation System of Mauritania. The results of this quali-quantitative assessment demonstrate that farmers use agrometeorological information for a variety of choices: making strategic choice on the seed variety and on the geographical distribution of plots, choosing the most appropriate planting date, better tuning crop development cycle with the rhythm of the rains and choosing favourable periods for different cultural operations. Globally, the effects of all these good practices can be summarized by an increase of crops productivity and a decrease of cropping costs (including opportunity cost) in terms of inputs and working time. [ABSTRACT FROM AUTHOR]

In this paper, changes in the long and short spells of different rain intensities are statistically analyzed using daily gridded rainfall data prepared by the India Meteorological Department for the period 1951-2008. In order to study regional changes, analyses have been conducted over nine selected agro-meteorological (agro-met) divisions, five homogeneous zones, and also over the whole of India. Rain events of different intensities with continuous rainfall of more than or equal to 4 days are classified here as long spells. Those with less than 4 days are termed as short spells. Those results which are statistically significant at 95% confidence level are discussed in this paper. Trend analysis shows that during the summer monsoon months of June to September, short spell rain events with heavy intensity have increased over India as a whole. On the other hand, long spell rain events with moderate and low intensities have decreased in numbers. Results further show that the contributions of long spell moderate and short spell low-intensity rain events to the total rainfall have decreased whereas the contributions of short spell heavy and moderate-intensity rain events to the total seasonal rainfall have increased. Percentage changes in various categories of long and short spells in the decade 1991-2000 compared with the earlier decade 1951-1960, highlight the maximum increase in heavy-intensity short spell category and decrease in moderate-intensity long spell category in India as a whole and in most of the homogeneous zones and agro-met divisions. The changes in different types of rain events differ in the six homogeneous zones and nine selected agro-met divisions. However, in three homogeneous zones and three agro-met divisions, the short spell heavy-intensity rain events dominate as in the entire country. There are also changes observed in the monthly occurrences of above categories of rain events during the 4 months of summer monsoon. Such results with details of changes in rain categories in different parts of India have important implications in agriculture sector in the country. [ABSTRACT FROM AUTHOR]

This paper examines the distribution of climate change impacts across the sixteen Agro-Ecological Zones (AEZs) of Africa. We combine net revenue from livestock and crops and regress total net revenue on a set of climate, soil, and socio-economic variables with and without country fixed effects. Although African crop net revenue is very sensitive to climate change, combined livestock and crop net revenue is more climate resilient. With the hot and dry CCC climate scenario, average damage estimates reach 27% by 2100, but with the mild and wet PCM scenario, African farmers will benefit. The analysis of AEZs implies that the effects of climate change will be quite different across Africa. For example, currently productive areas such as dry/moist savannah are more vulnerable to climate change while currently less productive agricultural zones such as humid forest or sub-humid AEZs become more productive in the future. [ABSTRACT FROM AUTHOR]

Anthropocentric climate change presents an existential threat through impacts such as rising sea levels, effects on agricultural crops and extreme weather events. However, governments, businesses and communities struggle to wean off fossil fuel dependency. In this article, we argue that this is due to the grip of fossil fuel hegemony. To explain this grip, we draw on the theoretical perspectives of new materialism to examine how fossil fuels and politics interact in upholding Australia's fossil fuel regime. Our analysis, based on 70 qualitative interviews conducted with politicians and political advisors, fossil fuel executives and experts and environmental activists, shows three processes – establishment, entrenchment and encroachment – through which political-material entanglements lock in a fossil fuel-based future. These processes are both discursive, with politicians and industry downplaying, if not outright denying, the climate emergency and material, with investment in new mines and infrastructure even while the negative ecological impacts of fossil fuel use gather pace. [ABSTRACT FROM AUTHOR]

Highlights • Proposed framework identifies the similarity among the weather condition over years. • A powerful domain specific weather-based decision support system (DSS) can be built using this framework. • Significant reuse of the developed content in a weather-based DSS can be achieved using this framework. Abstract Weather-based decision support systems (DSSs) are being built to improve the efficiency of the production systems in the domains of healthcare, agriculture, transport, governance and so on. Normally, a weather condition (WC) is represented by the statistical values of weather variables for a given duration (e.g. a day or a week). In a weather-based DSS, given a WC, the domain experts prepare the appropriate suggestions to improve the efficiency of the stakeholders. Normally, once the domain experts prepare a suggestion for a given WC that belongs to certain period (e.g. year or season), there is a scope to reuse the same suggestion for the similar WCs of other period(s). As a result, the performance of the DSS could be improved. In this paper, to improve reuse , we have proposed a notion of category-based WC (CWC) which is formed by using the categories of weather variables in the respective domain. By considering the context of agromet advisory service operated by India Meteorological Department (IMD) and the corresponding weather categories provided by IMD, we have analyzed the extent of reuse among CWCs by conducting the experiments on 30 years of weather data collected at Rajendranagar, Hyderabad, Telangana state. The experiments are conducted by considering two types of CWCs with the duration equal to one day and five days. By varying the number of weather variables in CWC from one to five, we have computed the extent of reuse among CWCs of different periods of the following period types: year, season, and phenophases (i.e., growth stages) of the Rice crop. The results show that there is a significant similarity among the CWCs of the given period and the CWCs of the preceding periods of each period type. For any domain including agriculture, the results provide an opportunity to improve the efficiency of weather-based agricultural DSSs by improving the reuse of the weather-based suggestions. [ABSTRACT FROM AUTHOR]

The aim of this paper is to improve understanding of the adaptive capacity of European agriculture to climate change. Extensive data on farm characteristics of individual farms from the Farm Accountancy Data Network (FADN) have been combined with climatic and socio-economic data to analyze the influence of climate and management on crop yields and income and to identify factors that determine adaptive capacity. A multilevel analysis was performed to account for regional differences in the studied relationships. Our results suggest that socio-economic conditions and farmcharacteristics should be considered when analyzing effects of climate conditions on farm yields and income. Next to climate, input intensity, economic size and the type of land use were identified as important factors influencing spatial variability in crop yields and income. Generally, crop yields and income are increasing with farm size and farm intensity. However, effects differed among crops and high crop yields were not always related to high incomes, suggesting that impacts of climate and management differ by impact variable. As farm characteristics influence climate impacts on crop yields and income, they are good indicators of adaptive capacity at farm level and should be considered in impact assessment models. Different farm types with different management strategies will adapt differently. [ABSTRACT FROM AUTHOR]

The usual methods for optimal sizing of photovoltaic (PV) irrigation water pumping systems, which separately considered the demand for hydraulic energy and possibilities of its production from available solar energy with the PV pumping system, are not optimal. However, by systematic approach to the problem, taking into account all relevant elements, from the PV water pumping system, water intake, local climate, soil, crops to method of irrigation, it is possible to optimize the determination of nominal power of the PV generator and obtain quality solutions. This paper presents one such method for optimal sizing. For the purpose of testing the proposed model for optimal sizing, in order to show to what extent it describes the system and to obtain corresponding research results, two areas were selected, in typical climate regions of Osijek and Split, as patterns of continental and coastal Croatia. By variation of certain values in the system, their effects on calculation of the optimal electric power of PV generator were observed. [ABSTRACT FROM AUTHOR]

Changes in climatic conditions increase risks associated with crop production in certain regions. Early detection of these changes enables the implementation of suitable adaptation measures in the local area, thereby stabilising agricultural production. Our analysis shows a significant shift in climatic conditions in Czechia between 1961 and 2020. We examined the changes in observed temperature conditions, precipitation distribution, drought occurrences, and frost incidents at a high resolution (0.5 × 0.5 km). The outputs show a significant increase in air temperatures and drought occurrence. Temperature totals above 5 °C in 1991–2020 were 15% higher than in 1961–1990. Furthermore, the relative change in totals above 10 °C was 26% after 1991. Over the last 30 years, drought incidence was four times more frequent than in 1961–1990, particularly in spring. In contrast, no significant changes in the distribution of precipitation occurred, and there was a slight decrease in the probability of frost during the growing season. Ongoing climate change brings warmer and drier conditions to higher-altitude regions in Czechia. Assessing climatic conditions on a global scale is less precise for relatively small and topographically diverse countries like Czechia due to coarse resolution. Therefore, a high-resolution analysis is more appropriate for these countries. [ABSTRACT FROM AUTHOR]

Climate change such as rising temperature, atmospheric carbon dioxide levels, rainfall variability and altering soil physical, chemical and biological properties are observed all around the world. A review over impacts of clim ate change with relation to crops and soil health was done at Department of Agricultural Meteorology, CCS HAU Hisar. IPCC, 2021 provides new estimates of the chances of crossing the global warming level of 1.5°C in the next decades and finds that unless there are immediate, rapid and large-scale reductions in greenhouse gas emissions, limiting warming to close to 1.5°C or even 2°C will be beyond reach. Impacts of climate change on soil properties are long time process as weather elements induce Physio-chemical reaction with soil which happens slowly in relation to weather elements. Climate change is generally expected to increase the crop yields due to CO2 fertilization, radiation use efficiency and longer growing season but on the other hand, beyond a critical limit it may develop stress conditions like water stress, sun burns, scorching, bark cracking, closure stomata, leaf senescence and abscission etc. that can decrease crop production. Acidification, sodicity and salinization problem can develop in soil due to increase in temperature and acidic rainfall. So it can be held that, climate change would have intensive impacts on cereal and horticulture crops health and production as well as on soil properties. [ABSTRACT FROM AUTHOR]

The impacts of extreme weather events on crop production are largely heterogeneous along the timing dimension of the shocks, and the varieties being affected. We investigate the yield-temperature relationships for three categories of earliness of durum wheat: early-maturing, middle-maturing, and late-maturing. We disentangle the time dimension distinguishing five phenological stages, as identified by the Growing Degree Days approach. Our panel regression models show that the starting, growing, and anthesis stages are sensitive to changes in minimum temperatures, regardless of wheat earliness. Raises in maximum temperatures during the starting stage are associated with increases in yields until a certain threshold above of which decrease; the opposite is true for increases in maximum temperatures in the maturity stage for latematuring varieties, and in the end stage for early-maturing varieties. Results imply that farmers and policymakers may adopt ex-ante and ex-post risk management strategies, i.e., choice of variety to avoid severe yield losses and incentives to crop insurance uptake, respectively. [ABSTRACT FROM AUTHOR]

In order to stress test loan portfolios for the impacts of climate change, historical events need to be analyzed to create templates to stress test for future events. Using the 2012 Midwestern US drought as an example, this work creates a stress-testing template for future droughts. The analysis connects weather and crop yield data to impacts on local macroeconomic conditions by comparing drought-impacted agricultural counties with nearby urban counties. After measuring the net macroeconomic impacts of the drought, this was used as an overlay with existing macroeconomic stress models to stress test a lender in a different part of the US for possible drought impacts. Having a library of such climate events would allow lenders to stress test their portfolios for a wide range of possible impacts. [ABSTRACT FROM AUTHOR]

The development of methodologies for predicting crop yield, in real-time and in response to different agro-climatic conditions, could help to improve the farm management decision process by providing an analysis of expected yields in relation to the costs of investment in particular practices. Based on the use of crop models, this paper compares the ability of two methodologies to predict wheat yield ( Triticum aestivum L.), one based on stochastically generated climatic data and the other on mean climate data. It was shown that the numerical experimental yield distribution could be considered as a log-normal distribution. This function is representative of the overall model behaviour. The lack of statistical differences between the numerical realisations and the logistic curve showed in turn that the Generalised Central Limit Theorem (GCLT) was applicable to our case study. In addition, the predictions obtained using both climatic inputs were found to be similar at the inter and intra-annual time-steps, with the root mean square and normalised deviation values below an acceptable level of 10% in 90% of the climatic situations. The predictive observed lead-times were also similar for both approaches. Given (i) the mathematical formulation of crop models, (ii) the applicability of the CLT and GLTC to the climatic inputs and model outputs, respectively, and (iii) the equivalence of the predictive abilities, it could be concluded that the two methodologies were equally valid in terms of yield prediction. These observations indicated that the Convergence in Law Theorem was applicable in this case study. For purely predictive purposes, the findings favoured an algorithm based on a mean climate approach, which needed far less time (by 300-fold) to run and converge on same predictive lead time than the stochastic approach. [ABSTRACT FROM AUTHOR]

All over the world, farming communities need to adapt to a changing climate. At the same time, they are confronted by the necessity to increase food availability for a growing population, but also to ensure a sustainable use of natural resources. In this process, agriculture is not just the fiend responsible for an increase in greenhouse gas emissions, nor the victim of rising temperatures and extreme weather events. Agriculture is also an ally in climate change mitigation and adaptation because the selection of ad hoc crop varieties and livestock can lower the environmental impact of farming and the implementation of better management practices can promote soil conservation. Whether agriculture will have a positive or a negative impact on climate change adaptation and mitigation will depend on the rural policies implemented, but it will also be contingent on an adequate understanding of the interconnections existing between weather, climate, and farming. This review explores such interconnections by focusing on the history of agricultural meteorology, which is the research field that studies the impact of weather and climate on crops, livestock, farming operations, and plant and animal pests and diseases. The article discusses stakeholders, institutions, and main developments in agricultural meteorology, and describes how the agenda of agricultural meteorology has shifted over time. At the beginning of the 20th century, research in agricultural meteorology focused on increasing the profitability of farming. In the 21st century, instead, the main goal of agricultural meteorology is to ensure food security and guarantee sustainability. This article is categorized under: Climate, History, Society, Culture > Disciplinary Perspectives [ABSTRACT FROM AUTHOR]

Abstract: Crop models often simulate drought impacts with full and no irrigation scenarios, while planners are more interested in whether the current available irrigation water can cope with the future more serious droughts. This paper addresses a key constraint common to modeling studies: the limited representation of actual irrigation water supply. We present a data-driven approach to identify a benchmark for agronomic drought risk levels as defined by water availability thresholds at the baseline climate (1980–2008) using reported crop yields, climate and irrigation statistics. Then, holding the current irrigation supplies, we adopted Bayesian formula to estimate magnitude of the future water availability and the associated probability of crops yields being decreased to rainfall-deficiency under climate conditions in 2030s (2020–2040) based on the RegCM3 climate model output driven by IPCC SRES A1B scenario. Results reveal that future drought stress would overwhelm the irrigation capacity of current supplies in northern and western China, while drought remains at baseline climate levels in the central, eastern and southern regions. The largest increases in the probability of projected drought risk were in northeast and southwest, ranging from 14% to 28% above baseline climate. Regional drought impacts for grain self sufficiency are discussed. [Copyright &y& Elsevier]

Abstract: Agro-meteorological metrics are indicators of weather determined environmental conditions on which agricultural management decisions are made. Metrics derived from an estimated future climate provide an opportunity to characterise the impacts of climate change on a wide range of agricultural systems, land use practices and ecosystem services. Such indications are vital for determining how changes in the biophysical environment can lead to land management and policy adaptations to achieve multiple objectives of financial viability, food security, biodiversity conservation and environmental sustainability. They provide valuable links between probable management adaptation responses and capacity for mitigating greenhouse gas emissions. However, there are large uncertainties associated with projected future climates, including the climate models’ spatial scale of representation and those at which agro-meteorological metrics are applied. This paper describes the estimation of agro-meteorological metrics derived from observed weather and downscaled Regional Climate Model projection data for 12 sites in Scotland. Results show that projected changes to seasonal rainfall distribution, growing season length, soil moisture deficits and accessibility will be substantially different from the present climate. Fundamentally, the metrics indicate a substantial shift in land management requirements and potential need for substantial changes in agricultural systems and land use that will have implications across a wide range of research disciplines. [Copyright &y& Elsevier]

Water resources management and agriculture planning models require a statistical synthesis of some rainfall features, in particular those representing dry atmospheric conditions. The bigger the basin, the more important these features become, as is the case of the La Plata Basin LPB.This paper focuses on the precipitation variability in the large LPB in South America, analysing the number of months per year with low rainfall and the sequences of months with low rainfall, their theoretical distributions and stability, which are needed as input for the models mentioned above.Long time series are used to analyse the lowfrequency variability and the relative importance of decadal variability. Changes are evident in the number of months per year with low rainfall, with a decrease of about 20 in the period after 1970.Theoretical distribution models binomial and geometric are fitted to these empirical distributions, and the regional variability of the fitting parameters is shown. In practically the entire region, the goodnessoffit of the two theoretical models considered is statistically satisfactory.The temporal variability of the parameters of the theoretical binomial p and geometric α distributions is analysed, in excluding subperiods of 10 and 5 years, respectively. The results show lowfrequency variability overlapped on a decadal variability, with low homogeneous regional behaviour.The distribution models have proven to be efficient for frequency adjustments of the rainfall properties studied. These results are an acceptable and necessary input to decision models in LPB. They also make it possible to infer effects of climate change. Copyright © 2008 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Abstract: There are clear indications that in the future the Alpine region will increasingly suffer from droughts. In this paper, a simple method is devised for determining probability of drought occurrence and exceedance of severity thresholds. A reconstruction of drought occurrence during the past 100 yr indicates that the method is able to detect major events and also to correctly gauge their relative severity. The procedure is used in conjunction with climate simulations for the European region valid for 2071–2100 to study the impact of climate change on the likelihood and severity of droughts. The climate scenario used for the analysis refers to a SRES A2 emission pathway and specifies in particular a decrease in the frequency of wet days of about 20% with respect to the growing season of summer crops (April to September). Under these conditions the frequency of droughts is shown to increase from about 15% to more than 50%. In addition, the results indicate an overall shift in the distribution toward higher severity scores. The average severity increases by a factor of two, but also at the upper end of the spectrum severity increases by more than 20%. It is argued that this will affect the perception of extreme droughts, i.e. of those events rarer than the 10th percentile of the distribution. If this scenario comes true, by the end of the 21st century droughts comparable in severity to the 2003 event would represent the norm rather than the exception. [Copyright &y& Elsevier]

Abstract PNNLs Agriculture and Land Use is used to demonstrate the impact of potential changes in climate on agricultural production and land use in the United States. AgLU simulates production of four crop types in several world regions, in 15-yr time steps from 1990 to 2095. Changes in yield of major field crops in the United States, for 12 climate scenarios, are obtained from simulations of the EPIC crop growth model. Results from the HUMUS model are used to constrain crop irrigation, and BIOME3 model is used to simulate productivity of unmanaged ecosystems. Assumptions about changes in agricultural productivity outside the United States are treated on a scenario basis, either responding in the same way as in the United States, or not responding to climate. [ABSTRACT FROM AUTHOR]

Unfavourable weather is a common cause for crop failures all over the world. Whilst extreme weather conditions may cause extreme impacts, crop failure commonly is induced by the occurrence of multiple and combined anomalous meteorological drivers. For these cases, the explanation of conditions leading to crop failure is complex, as the links connecting weather and crop yield can be multiple and non-linear. Furthermore, climate change is likely to perturb the meteorological conditions, possibly altering the occurrences of crop failures or leading to unprecedented drivers of extreme impacts. The goal of this study is to identify important meteorological drivers that cause crop failures and to explore changes in crop failures due to global warming. For that, we focus on a historical failure event, the extreme low soybean production during the 2012 season in the midwestern US. We first train a random forest model to identify the most relevant meteorological drivers of historical crop failures and to predict crop failure probabilities. Second, we explore the influence of global warming on crop failures and on the structure of compound drivers. We use large ensembles from the EC-Earth global climate model, corresponding to present-day, pre-industrial +2 and 3 ∘ C warming, respectively, to isolate the global warming component. Finally, we explore the meteorological conditions inductive for the 2012 crop failure and construct analogues of these failure conditions in future climate settings. We find that crop failures in the midwestern US are linked to low precipitation levels, and high temperature and diurnal temperature range (DTR) levels during July and August. Results suggest soybean failures are likely to increase with climate change. With more frequent warm years due to global warming, the joint hot–dry conditions leading to crop failures become mostly dependent on precipitation levels, reducing the importance of the relative compound contribution. While event analogues of the 2012 season are rare and not expected to increase, impact analogues show a significant increase in occurrence frequency under global warming, but for different combinations of the meteorological drivers than experienced in 2012. This has implications for assessment of the drivers of extreme impact events. [ABSTRACT FROM AUTHOR]

We study potential impacts of future climate change on U.S. agricultural productivity using county‐level yield and weather data from 1950 to 2015. To account for adaptation of production to different weather conditions, it is crucial to allow for both spatial and temporal variation in the production process mapping weather to crop yields. We present a new panel data estimation technique, called mean observation OLS (MO‐OLS) that allows for spatial and temporal heterogeneity in all regression parameters (intercepts and slopes). Both forms of heterogeneity are important: We find strong evidence that production function parameters adapt to local climate, and also that sensitivity of yield to high temperature declined from 1950–89. We use our estimates to project corn yields to 2100 using 19 climate models and three greenhouse gas emission scenarios. We predict unmitigated climate change will greatly reduce yield. Our mean prediction (over climate models) is that adaptation alone can mitigate 36% of the damage, while emissions reductions consistent with the Paris targets would mitigate 76%. [ABSTRACT FROM AUTHOR]

Keywords: Climate change; technical progress; crops yield; ARDL model; Pakistan EN Climate change technical progress crops yield ARDL model Pakistan INTRODUCTION Climate change has direct and disastrous effects on agriculture segment since it specifically relies upon weather variability (Meza and Silva, 2009). In the short-run, the temperature has a negative and significant effect on the wheat yield which means that a 1 percent rise in temperature will lead to a 1.02 percent decrease in wheat yield. Climate change, technical progress, crops yield, ARDL model, Pakistan. [Extracted from the article]

Actual evapotranspiration (AET), grain yield and water productivity (WPET) of rice and wheat were assessed in relation to changing climatic conditions over a period of 32-46 years for three locations (Ballowal Saunkhari, Ludhiana and Bathinda) in different agro-climatic regions of Punjab. A large variability in AET of rice and wheat were observed over the years with increasing trend at Ballowal Saunkhari and decreasing trend at Ludhiana and Bathinda. A linear increase in grain yield of rice and wheat was observed at all the three locations. This resulted a significant increasing trends in water productivity of both wheat and rice at all the stations. The water productivity of rice was negatively correlated with AET while water productivity of wheat had curvilinear relationship with AET. [ABSTRACT FROM AUTHOR]

Analyzes of the effects of climate change in agriculture is a priority in the strategic development actions in EU member countries and beyond. The interdisciplinary character of actions involves a comprehensive approach by identifying and linking activities to develop and implement measures intra and inter-sectoral those related to climate change in agriculture and related fields. Climate variability affects all sectors but agriculture remains the most vulnerable. Crop production varies year by year, it is significantly influenced by fluctuations in weather conditions and in particular the production of extreme weather events. In Romania, climate change has and will have a significant impact on the development of natural conditions, agriculture and biodiversity are the areas most vulnerable to climate change, given the dependence on climatic conditions and the negative ecological, economic and social. From an economic perspective, this phenomenon causes a reduction to total compromise agricultural production, with serious implications on food security of the population. [ABSTRACT FROM AUTHOR]

Climate change has a significant impact on agrometeorological conditions. We analysed the winter wheat overwintering indices for the northern winter wheat production areas of China (NWPA), which include the percentage of extreme low‐temperature (daily minimum temperature ≤−22 °C) years, the first day of the overwintering period (FDO), sowing date (SD) and precipitation before winter (PBW). Then, we compared the differences of the four indices between the forecast period 2021–2050 and the baseline period 1961–1990 under the RCP4.5 scenario, which were based on the 0.5° × 0.5° grid daily meteorological data output provided by the Inter‐Sectoral Impact Model Intercomparison Project. The results showed that the occurrence of extreme cold winters decreases the NWPA, which will result in the northern boundary of winter wheat moving north by 1–2 latitudes and in an increase in the potentially safe overwintering areas by 8.5%. The FDO will be delayed in 2021–2050 by an average of 7.7 days. Climate warming will change the meteorological conditions for winter wheat overwintering in the Huang‐Huai winter wheat area, especially near the southern boundary of the winter wheat planting area, and the areas and years with no obvious overwintering periods will experience an increase by 50% and 56%, respectively. The SD will be postponed by 8.6–9.0 days. The PBW will decrease as the accumulated temperature increases, especially in the Huang‐Huai winter wheat area. In the future, the possible northern boundary for winter wheat planting may move northward, and the northernmost tip may reach 46°N. South of the Huang‐Huai region, winter wheat planting may change from a semi‐winter to a spring variety. These findings will provide a reference for generating wheat planting and management strategies in China. Climate change has significantly influenced the winter wheat planting in northern China. In 2021–2050, climate warming would decrease the occurrence of extreme cold winters in the regions near northern planting boundary, which would result in the possible northern boundary (red and green lines) moving northward, and the northernmost tip may reach 46°N. However, south of the Huang‐Huai region (black oval areas), wheat planting may change from a semi‐winter to a spring variety due to temperature increase and precipitation reduction. [ABSTRACT FROM AUTHOR]

Separating out the influence of climatic trend, fluctuations and extreme events on crop yield is of paramount importance to climate change adaptation, resilience, and mitigation. Previous studies lack systematic and explicit assessment of these three fundamental aspects of climate change on crop yield. This research attempts to separate out the impacts on rice yields of climatic trend (linear trend change related to mean value), fluctuations (variability surpassing the "fluctuation threshold" which defined as one standard deviation (1 SD) of the residual between the original data series and the linear trend value for each climatic variable), and extreme events (identified by absolute criterion for each kind of extreme events related to crop yield). The main idea of the research method was to construct climate scenarios combined with crop system simulation model. Comparable climate scenarios were designed to express the impact of each climate change component and, were input to the crop system model (CERES-Rice), which calculated the related simulated yield gap to quantify the percentage impacts of climatic trend, fluctuations, and extreme events. Six Agro-Meteorological Stations (AMS) in Hunan province were selected to study the quantitatively impact of climatic trend, fluctuations and extreme events involving climatic variables (air temperature, precipitation, and sunshine duration) on early rice yield during 1981-2012. The results showed that extreme events were found to have the greatest impact on early rice yield (−2.59 to −15.89%). Followed by climatic fluctuations with a range of −2.60 to −4.46%, and then the climatic trend (4.91-2.12%). Furthermore, the influence of climatic trend on early rice yield presented "trade-offs" among various climate variables and AMS. Climatic trend and extreme events associated with air temperature showed larger effects on early rice yield than other climatic variables, particularly for high-temperature events (−2.11 to −12.99%). Finally, the methodology use to separate out the influences of the climatic trend, fluctuations, and extreme events on crop yield was proved to be feasible and robust. Designing different climate scenarios and feeding them into a crop system model is a potential way to evaluate the quantitative impact of each climate variable. [ABSTRACT FROM AUTHOR]

‘Nurturance’ and ‘trust’ are two important aspects that have not been considered seriously in Indonesian state programmes that relate to transferring knowledge and technology to local people. As a result, their effectiveness in producing lifelong learning among farmers remains unknown. This article examines the inclusion of these two aspects in a climate-sensitive extension approach to interdisciplinary and trans-disciplinary collaboration between scientists and farmers. The arena of this collaboration, ‘Science Field Shops’ (SFSs, Warung Ilmiah Lapangan), is based on a dialogical knowledge exchange between all parties involved in assisting farmers to improve their capacity to anticipate the consequences of increasing climate variability. Farmers are being engaged as researchers and active learners in developing their own agrometeorological learning. This article examines the extent to which ‘nurturance’ and ‘trust’ have been influential in establishing and institutionalising the SFSs in different farming communities in Indonesia. In contrast, nurturance and trust are barely to be found in any government agricultural programme. This article also discusses the challenges the scientists faced in their efforts to build collaboration with government agencies. [ABSTRACT FROM AUTHOR]

In this study, a microwave-based multisatellite merged product released from the European Space Agency's Climate Change Initiative (ESA CCI) and two model-based simulations from the Community Land Model 4.5 (CLM4.5) and Global Land Data Assimilation System (GLDAS) were used to investigate interannual variations and trends of soil moisture in China between 1979 and 2010. They were also evaluated using in situ observations from the nationwide agrometeorological network. These three datasets show consistent drying trends for surface soil moisture in northeastern and central China, as well the eastern portion of Inner Mongolia, and wetting trends in the Tibetan Plateau, which are also identified by in situ observations. Trends in the root-zone soil moisture are in line with those of surface soil moisture seen in the CLM4.5 and GLDAS simulations obtained from most areas in China (78%-88%), except for northwestern China and southwest of the Tibetan Plateau. Moreover, the drying trend intensifies with increasing soil depth. Taking the in situ measurements as reference, it is found that ESA CCI has better accuracy in identifying the significant drying trends while CLM4.5 and GLDAS capture wetting trends better. Compared to temperature, precipitation is the primary factor responsible for these trends, which controls the direction of soil moisture changes, while increasing temperatures can also enhance soil drying during periods of decreased precipitation. [ABSTRACT FROM AUTHOR]

Gansu Province is a high-incidence area for many meteorological disasters. In this research, using affected and covered risk indices for the main agro-meteorological disasters (drought, flood, wind hail, and frost), the variability characteristics of agro-meteorological disasters were analyzed and the risks calculated based on the information diffusion method against the background of climate change in Gansu Province. The results showed that of affected (crop yield reduced by >10%) and covered (crop yield reduced by >30%) areas for drought and frost tended to increase between 1978 and 2014, whereas those for flood and wind hail tended to decrease. The affected and covered risk indices for drought increased, those for flood and wind hail showed a slight decreasing trend, the frost affected risk index decreased, and the frost covered risk index increased. Comparing the four kinds of risk for agro-meteorological disasters, the estimated risk decreased as the affected and covered risk indices increased. The risk of drought was more serious than those for other agro-meteorological disasters for the same affected risk index, followed by frost, flood, and wind hail. The most serious disaster agro-meteorological covered risk was wind hail, followed by drought, flood, and frost. After a rapid change in temperature in 1994, the estimated affected risk values of drought and frost increased on average by 17.56 and 96.83%, respectively, whereas the estimated risks of flood and wind hail decreased on average by 15.59 and 5.90%, respectively. After the temperature jump, the estimated covered risk of drought increased on average by 19.57%, whereas those of flood, wind hail, and frost decreased on average by 3.02, 1.78, and 26.10%, respectively. These new scientific discoveries are significant for agro-meteorological disaster prevention in Gansu Province. [ABSTRACT FROM AUTHOR]

In third world countries like Indonesia, higher education is expected to contribute to the development of applied sciences that tackle pressing problems. Adaptation to and mitigation of climate change are such fields. Applied sciences work better in problem solving when approached in an interdisciplinary way. Winarto and Stigter have decades of experience in applied agricultural sciences but from different main fields: anthropology and physics/(micro)meteorology/(micro)climatology. They provide a welcome complementarity in their dialogue-based interactions with farmers on how climate change has necessitated livelihood/resilience changes for them. They developed and established climatological services for farmers, consisting of seven initial climate services for agriculture, bringing farmers closer to their ecosystems that are currently threatened by climate change. They established what they call 'agrometeorological learning,' which is about changes of beliefs, attitudes, behaviors, and goals of farmers due to new meteorological and climatologic knowledge they acquire, which considerably help increase farmers resiliencies. [ABSTRACT FROM AUTHOR]

Science Field Shops (SFSs) are an example of a transdisciplinary educational commitment where farmers, scientists, and extension staff exchange knowledge on agrometeorology in dialogue form to better respond to climate change. How can scientists, farmers, and extension staff build up this transdisciplinary collaboration? How has the agrometeorological learning environment been institutionalized in several places in Indonesia? An interdisciplinary collaboration between agrometeorology and anthropology serves as basis for developing seven climate services that are provided in the SFSs. Through Knowledge Transfer and Communication Technologies, farmers have become active learners, researchers, and decision makers of their own responses to the consequences of climate change. Although such an approach proves efficient in improving the farmers' knowledge and anticipation capability, the transdisciplinary collaboration with state authority needs to be overhauled to improve the process. [ABSTRACT FROM AUTHOR]

Understanding changes in monsoon variability over a decade requires thorough knowledge of the seasonal and inter-annual variability in surface energy flux and its forcing parameters (land surface and meteorology) in response to climate change. In the present study, the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua climate model gridded global products (0.05° × 0.05° spatial resolution) of land surface temperature (LST;Ts), normalized difference vegetation index (NDVI), and surface albedo (α) were used to generate seasonal (June–September) and inter-annual (2003–2012) variation in surface energy flux and its forcing parameters over different agro-climatic regions (ACRs) of India. Energy fluxes were retrieved using a single-source surface energy balance model (here vegetation and soil is considered as a single unit). Energy flux observations over different ACRs allowed comparison of the seasonal transition of latent heat flux (LE), net radiation (Rn), soil heat flux (G), available energy (Q = Rn – G), and evaporative fraction (EF) as terrestrial links to the atmosphere. The seasonal and inter-annual variation in EF was investigated by plotting against the soil moisture information retrieved from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) global monthly data product (1° × 1° spatial resolution). Decadal and seasonal analysis showed that energy fluxes vary widely in time and space due to variability in surface radiation parameters (Ts,α), vegetation cover, soil moisture, and air temperature (Ta), which influence the seasonal transition of monsoon through LE and EF. Among the ACRs, LE and EF were found lowest in the Western Dry Region (WDR) and highest in the Western Himalayan Region (WHR). The spatiotemporal depiction of MODIS LE and MODIS EF over a span of 10 years can identify the hotspots and monsoon intensity over different ACRs. Climatic parameters that are susceptible to changes resulting from climate change are thoroughly studied in the present analysis. [ABSTRACT FROM AUTHOR]

Solar radiation is an important variable for studies related to solar energy applications, meteorology, climatology, hydrology, and agricultural meteorology. However, solar radiation is not routinely measured at meteorological stations; therefore, it is often required to estimate it using other techniques such as retrieving from satellite data or estimating using other geophysical variables. Over the years, many models have been developed to estimate solar radiation from other geophysical variables such as temperature, rainfall, and sunshine duration. The aim of this study was to evaluate six of these models using data measured at four independent worldwide networks. The dataset included 13 stations from Australia, 25 stations from Germany, 12 stations from Saudi Arabia, and 48 stations from the USA. The models require either sunshine duration hours (Ångstrom) or daily range of air temperature (Bristow and Campbell, Donatelli and Bellocchi, Donatelli and Campbell, Hargreaves, and Hargreaves and Samani) as input. According to the statistical parameters, Ångstrom and Bristow and Campbell indicated a better performance than the other models. The bias and root mean square error for the Ångstrom model were less than 0.25 MJ m day and 2.25 MJ m day, respectively, and the correlation coefficient was always greater than 95 %. Statistical analysis using Student's t test indicated that the residuals for Ångstrom, Bristow and Campbell, Hargreaves, and Hargreaves and Samani are not statistically significant at the 5 % level. In other words, the estimated values by these models are statistically consistent with the measured data. Overall, given the simplicity and performance, the Ångstrom model is the best choice for estimating solar radiation when sunshine duration measurements are available; otherwise, Bristow and Campbell can be used to estimate solar radiation using daily range of air temperature. [ABSTRACT FROM AUTHOR]

Agro-meteorological (AM) disasters have been reported to be more frequent with climate change. In this study, the spatial and temporal changes in the frequency of major AM disasters affecting maize production are investigated by analyzing observed records at 224 national AM stations in China from 1991 to 2009. To investigate the temporal changes of the disasters with climate warming, the whole study period was separated into a cool period (1991-2000) and a warmer one (2000-2009). We found drought was the most common disaster with a frequency of 49 %, followed by chilling injures (19.46 %). The frequency of disasters affecting maize increased significantly during the reproductive growth period than the vegetative growth period. Spatial patterns of major disaster frequency were characterized by region-specific, with more cold stress in northeastern China, drought in northern and western China, and rainstorms in southwestern China. Our study highlighted that the frequency and scope both increased significantly, implying a potential increasing risk for maize production. It is critical and important for local farmers and governors to take reasonable and timely adaptation measures based on the latest disasters' features to mitigate the potential yield loss from AM disasters in order to ensure food security in China. [ABSTRACT FROM AUTHOR]

The climatic potential of maize under dryland farming in Lesotho, southern Africa, was investigated using five suitability indices comprising probability of accumulating heat units of greater than 1,500 growing degree days, probability of a frost-free growing season, probability of seasonal rainfall of more than 500 mm, probability of drought during the flowering to grain-filling stages and the slope of an area. A geographic information system layer was prepared for each of these parameters and the layers overlaid using different weights for each of the climatic suitability indices to obtain an agroclimatic maize suitability map for Lesotho. This analysis yielded different suitability classes. This variability points to prevalence of climatic constraints that need to be acknowledged when attempting to identify management strategies that can optimize the rain-fed maize production in climatically variable environments. [ABSTRACT FROM AUTHOR]

Enriching farmers' knowledge of the risks and consequences of climate change is the most promising strategy to better assist them. Nevertheless, we have to bear in mind that people filter and absorb scientific knowledge through pre-existing cultural models and aspirations for desired outcomes. The severe pest/disease outbreaks during the La-Niña periods of 2009 and 2010/2011, and the unpreparedness of farmers in many places in Java, was a timely opportunity for many parties to reflect seriously on the deficiencies in our approaches and facilitations. Our inter-disciplinary collaboration proved successful in strengthening and enriching farmers' knowledge by bringing agrometeorological thinking and knowledge, based on scientific ideas, premises, and methods, to local people who had their own "ethnoscience." This benefits farmers over an extended period and until the public extension intermediaries have been sufficiently trained. Our suggestions are: assisting farmers to discover their own vulnerability issues through continuous dialogues and knowledge exchange in "Science/Climate Field Shops," and the measurement of rainfall and the observation of weather and climate implications for fields and crops in a standardized way as the basis of an improved Climate Field School. To that end the training of public extension intermediaries is necessary. [ABSTRACT FROM AUTHOR]

The Cameroonian agricultural sector, a critical part of the local ecosystem, is potentially vulnerable to climate change, thus raising concerns about food security in the country''s future. Adaptations policies may be able to mitigate some of this vulnerability. This article addresses the issue of selected adaptation options within the context of Cameroonian food production. A methodology is applied where transient diagnostics of two atmosphere-ocean general circulation models, the NASA/Goddard Institute GISS and the British HadCM3, are coupled to a cropping system simulation model (CropSyst). This methodology simulates current and future (2020, 2080) crop yields for selected key crops such as bambara nut, groundnut, maize, sorghum, and soybean, in eight agricultural regions of Cameroon. Our results show that for the future, substantial yield increases are estimated for bambara groundnut, soybean and groundnut, while little or no change or even decreases for maize and sorghum yields, varying according to the climate scenario and the agricultural region investigated. Taking the ?no regrets? principle into consideration, we also explore the advantages of specific adaptation strategies specifically for three crops, maize, sorghum and bambara groundnut, under GISS A2 and B2 marker scenarios only. Here, changing sowing dates may be ineffective in counteracting adverse climatic effects because of the narrow rainfall band that strictly determines the timing of farm operations in Cameroon. In contrast, the possibility of developing later maturing new cultivars proved to be very effective in offsetting adverse impacts, giving the highest increases in productivity under different scenario projections without management changes. For example, under climate change scenario GISS A2 2080, a 14.6% reduction in maize yield was converted to a 32.1% increase; a 39.9% decrease in sorghum yield was converted to a 17.6% increase, and for bambara groundnut, yields were almost trebled due to increase length of growing period and the positive effects of higher CO2concentrations. These results better inform wider studies and development strategies on sustainable agriculture in the area by providing an indication as to the potential direction in shifts in production capabilities. Our approach highlights the benefit of using models as tools to investigate potential climate change impacts, where results can supplement existing knowledge. The findings also provide useful guidance and motivation to public authorities and development agencies interested in food security issues in Cameroon and elsewhere. [ABSTRACT FROM AUTHOR]

A regional atmospheric model with 60-km resolution is asynchronously coupled with a potential vegetation model to study the implications of twenty-first-century climate change for the tropical and subtropical climate and vegetation of South America. The coupled model produces an accurate simulation of the present day climate and vegetation. Future climate is simulated by increasing atmospheric CO2 levels to 757 ppmv and imposing lateral and surface boundary conditions derived from a GCM simulation for 2081–2100 from the Canadian Climate Center GCM. The coupled regional model simulation projects a 70% reduction in the extent of the Amazon rain forest by the end of the twenty-first century and a large eastward expansion of the caatinga vegetation that is prominent in the Nordeste region of Brazil today. These changes in vegetation are related to reductions in annual mean rainfall and a modification of the seasonal cycle that are associated with a weakening of tropical circulation systems. [ABSTRACT FROM AUTHOR]

It is well established that crop production is inherently vulnerable to variations in the weather and climate. More recently the influence of vegetation on the state of the atmosphere has been recognized. The seasonal growth of crops can influence the atmosphere and have local impacts on the weather, which in turn affects the rate of seasonal crop growth and development. Considering the coupled nature of the crop–climate system, and the fact that a significant proportion of land is devoted to the cultivation of crops, important interactions may be missed when studying crops and the climate system in isolation, particularly in the context of land use and climate change. To represent the two-way interactions between seasonal crop growth and atmospheric variability, we integrate a crop model developed specifically to operate at large spatial scales (General Large Area Model for annual crops) into the land surface component of a global climate model (GCM; HadAM3). In the new coupled crop–climate model, the simulated environment (atmosphere and soil states) influences growth and development of the crop, while simultaneously the temporal variations in crop leaf area and height across its growing season alter the characteristics of the land surface that are important determinants of surface fluxes of heat and moisture, as well as other aspects of the land-surface hydrological cycle. The coupled model realistically simulates the seasonal growth of a summer annual crop in response to the GCM's simulated weather and climate. The model also reproduces the observed relationship between seasonal rainfall and crop yield. The integration of a large-scale single crop model into a GCM, as described here, represents a first step towards the development of fully coupled crop and climate models. Future development priorities and challenges related to coupling crop and climate models are discussed. [ABSTRACT FROM AUTHOR]

Abstract Climate variability and change affects individuals and societies. Within agricultural systems, seasonal climate forecasting can increase preparedness and lead to better social, economic and environmental outcomes. However, climate forecasting is not the panacea to all our problems in agriculture. Instead, it is one of many risk management tools that sometimes play an important role in decision-making. Understanding when, where and how to use this tool is a complex and multi-dimensional problem. To do this effectively, we suggest a participatory, cross-disciplinary research approach that brings together institutions (partnerships), disciplines (e.g., climate science, agricultural systems science, rural sociology and many other disciplines) and people (scientist, policy makers and direct beneficiaries) as equal partners to reap the benefits from climate knowledge. Climate science can provide insights into climatic processes, agricultural systems science can translate these insights into management options and rural sociology can help determine the options that are most feasible or desirable from a socio-economic perspective. Any scientific breakthroughs in climate forecasting capabilities are much more likely to have an immediate and positive impact if they are conducted and delivered within such a framework. While knowledge and understanding of the socio-economic circumstances is important and must be taken into account, the general approach of integrated systems science is generic and applicable in developed as well as in developing countries. Examples of decisions aided by simulation output ranges from tactical crop management options, commodity marketing to policy decisions about future land use. We also highlight the need to better understand temporal- and spatial-scale variability and argue that only a probabilistic approach to outcome dissemination should be considered. We demonstrated how knowledge of climatic variability (CV), can lead to better decisions in agriculture, regardless of geographical location and socio-economic conditions. [ABSTRACT FROM AUTHOR]

Abstract Agricultural meteorologists are concerned with many operational aspects of the effects of climate on crop production, livestock, and natural resource management. For them to continue to make a contribution to the economy of a country they must continually sharpen their skills and remain updated on the latest available information. Training should include a variety of skills, including transferable skills (e.g. communication, numeracy), professional skills (including cognitive skills) and information technology skills. Problem-based learning can be used to promote critical thinking, decision making and analytical skills. More use should be made of computer-aided learning for agricultural meteorologists in-service training. In particular, the Internet or CDs could be used to disseminate specific recently developed techniques and applications to improve the understanding of the variability in climate and its effect on agricultural production and natural resource management. Examples that can address the vulnerability of farmers include cropclimate matching, the use of indices, crop modelling and risk assessment together with seasonal outlooks. A strategy needs to be formulated to address these needs and implement changes in the education and training of agricultural meteorologists. These training needs must be constantly updated to meet the changing demands of new technology to cope with climate change and climate variability. [ABSTRACT FROM AUTHOR]