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Food security is a key pillar of environmental security yet remains one of the world's greatest challenges. Its obverse, food insecurity, negatively impacts health and well-being, drives mass migration, and undermines national security and global sustainable development. Ensuring food security is a delicate balance of myriad concerns within the atmospheric and Earth sciences, agronomy and agriculture engineering, social sciences, economics, monitoring, and policymaking. A Food Security Presidential Session at the American Meteorological Society's (AMS) 2022 Annual Meeting brought together experts across disciplines to tackle issues at the nexus of weather, climate, and food security. The starkest takeaway was the realization that, despite its importance and clear roles for the atmospheric and climate sciences, food security has not been a focus for the AMS community. The aim of this paper is to build on the perspectives shared by this expert panel and to identify overlapping issues and key points of intersection between the food-security community and AMS. We examine 1) the interactions between weather, climate, and the food system and how they influence food security; 2) the time and spatial scales of food security decision support that match weather and climate phenomena; 3) the role of both providers and users of information as well as decision-makers in improving research to operations for food security; and 4) the opportunities for the AMS community to address food security. We conclude that, moving forward, the AMS community is well-positioned to scale up its engagement across the global food system to address existing scientific needs and technology gaps to improve global food security. SIGNIFICANCE STATEMENT: We examine how members of the AMS community can help ensure global food security, whether they are engaged in the physical and social sciences or the realms of policy and society. Inspired by the messages of panelists at a Presidential Forum on Food Security at the 2022 AMS Annual Meeting, we gather their perspectives and those of experts working in the various relevant fields and find that there are roles for everyone across the AMS—from providing forecasts of the hydrometeorological variables used in food security outlooks to converting data to knowledge and engaging with stakeholders and decision-makers. We make concrete suggestions to ensure the AMS and its members are fully engaged in feeding the world. [ABSTRACT FROM AUTHOR]

Accurate, timely, and accessible meteorological and soil moisture measurements are essential for a number of applications including weather forecasting, agricultural decision-making, and flood and drought prediction. Such data are becoming increasingly available globally, but the large number of networks and various data reporting formats often make utilization of such data difficult. The TexMesonet is a "network of networks" developed within the state of Texas to collect, process, and make public data collected from more than 1700 monitoring stations throughout the state. This paper describes the TexMesonet, with special attention paid to monitoring sites installed and managed by the Texas Water Development Board. It also provides a case study exemplifying how these data may be used and gives recommendations for future data applications. [ABSTRACT FROM AUTHOR]

Since 2017, the Northern Australia Climate Program (NACP) has assisted the pastoral grazing industry to better manage drought risk and climate variability. The NACP funding is sourced from the beef cattle industry, government, and academia, representing the program's broad range of aims and target beneficiaries. The program funds scientists in the United Kingdom and Australia, in addition to extension advisers called "Climate Mates" across a region that supports 15 million head of cattle. Many Climate Mates are employed in the cattle sector and have existing relationships in their communities and capacity to meaningfully engage with the program's intended beneficiaries—red meat producers. The NACP is a prime example of a successful end-to-end program, integrating climate model improvements (research) with tailored forecast products (development), through to direct stakeholder engagement (extension), on-ground application of technologies (adoption), and improvement in industry and community resilience (impact). The climate information needs of stakeholders also feed back to the research and development components, ensuring the scientific research directly addresses end-user requirements. For any scientific research program, ensuring that research output has measurable real-world impact represents a key challenge. This is more difficult in cases where the scientific research is several steps away from the customer's needs. This paper gives an overview of the NACP and research highlights, discussing how the end-to-end framework could be adapted and applied in other regions and industries. It seeks to provide a roadmap for other groups to follow to produce more targeted research with identifiable real-world benefits. [ABSTRACT FROM AUTHOR]

A simple—yet powerful—indicator for monitoring agricultural drought is the water requirement satisfaction index (WRSI). In data-sparse, food-insecure areas, the WRSI is used to guide billions of dollars of aid every year. The WRSI uses precipitation (PPT) and reference evapotranspiration (RefET) data to estimate water availability relative to water demand experienced over the course of a growing season. If the season is in progress, to-date conditions can be combined with climatological averages to provide insight into potential end-of-season (EOS) crop performance. However, if the average is misrepresented, these forecasts can hinder early warning and delay precious humanitarian aid. While many agencies use arithmetic average climatologies as proxies for "average conditions," little published research evaluates their effectiveness in crop-water balance models. Here, we use WRSI hindcasts of three African regions' growing seasons, from 1981 to 2019, to assess the adequacy of the arithmetic mean climatological forecast—the Extended WRSI. We find that the Extended WRSI is positively biased, overestimating the actual EOS WRSI by 2%–23% in East, West, and southern Africa. The presented alternative combines to-date conditions with data from previous seasons to produce a series of historically realistic conclusions to the current season. The mean of these scenarios is the WRSI Outlook. In comparison with the Extended WRSI, which creates a single forecast scenario using average inputs that are not covarying, the WRSI Outlook employs an ensemble of scenarios, which more adequately capture the historical distribution of distribution of rainfall events along with the covariability between climate variables. More specifically, the impact of dry spells in individual years is included in the WRSI Outlook in a way that is smoothed over in the Extended WRSI. We find that the WRSI Outlook has a near-zero bias score and generally has a lower RMSE. In total, this paper highlights the inadequacies of the arithmetic mean climatological forecast and presents a less biased and more accurate scenario-based approach. To this end, the WRSI Outlook can improve our ability to identify agricultural drought and the concomitant need for humanitarian aid. [ABSTRACT FROM AUTHOR]

The catastrophic derecho that occurred on 10 August 2020 across the midwestern United States caused billions of dollars of damage to both urban and rural infrastructure as well as agricultural crops, most notably across the state of Iowa. This paper documents the complex evolution of the derecho through the use of low-Earth-orbit passive-microwave imager and GOES-16 satellite-derived products complemented by products derived from NEXRAD weather radar observations. Additional satellite sensors including optical imagers and synthetic aperture radar (SAR) were used to observe impacts to the power grid and agriculture in Iowa. SAR improved the identification and quantification of damaged corn and soybeans, as compared to true-color composites and normalized difference vegetation index (NDVI). A statistical approach to identify damaged corn and soybean crops from SAR was created with estimates of 1.97 million acres of damaged corn and 1.40 million acres of damaged soybeans in the state of Iowa. The damage estimates generated by this study were comparable to estimates produced by others after the derecho, including two commercial agricultural companies. [ABSTRACT FROM AUTHOR]

Agriculture is a highly weather-dependent activity, and climatic conditions impact both directly crop growth and indirectly diseases and pest developments causing yield losses. Weather forecasts are now a major component of various decision-support systems that assist farmers to optimize the positioning of crop protection treatments. However, properly accounting for weather uncertainty in these systems still remains a challenge. In this paper, three global and regional ensemble prediction systems (EPSs), covering different spatiotemporal scales, are coupled to a temperature-driven developmental model for grapevine moths in order to provide probabilistic forecasts of treatment dates. It is first shown that a parametric postprocessing of the EPSs significantly improves the prediction of treatment dates. Anticipating the need for phytosanitary treatments also requires seamless weather forecasts from the next hour to subseasonal time scales. An approach is presented to design seamless ensemble forecasts from the combination of the three EPSs used. The proposed method is able to leverage the increased performance of high-resolution EPS at short ranges, while ensuring a smooth transition toward larger-scale EPSs for longer ranges. The added value of this seamless integration on agronomic predictions is, however, difficult to assess with the current experimental setup. Additional simulations over a larger number of locations and years may be required. [ABSTRACT FROM AUTHOR]

On the Canadian Prairies, agricultural practices result in millions of hectares of standing crop stubble that gradually emerges during snowmelt. The importance of stubble in trapping wind-blown snow and retaining winter snowfall has been well demonstrated. However, stubble is not explicitly accounted for in hydrological or energy balance snowmelt models. This paper relates measurable stubble parameters (height, width, areal density, and albedo) to the snowpack energy balance and snowmelt with the new, physically based Stubble–Snow–Atmosphere Model (SSAM). Novel process representations of SSAM quantify the attenuation of shortwave radiation by exposed stubble, the sky and vegetation view factors needed to solve longwave radiation terms, and a resistance scheme for stubble–snow–atmosphere fluxes to solve for surface temperatures and turbulent fluxes. SSAM results were compared to observations of radiometric snow-surface temperature, stubble temperature, snow-surface solar irradiance, areal-average turbulent fluxes, and snow water equivalent from two intensive field campaigns during snowmelt in 2015 and 2016 over wheat and canola stubble in Saskatchewan, Canada. Uncalibrated SSAM simulations compared well with these observations, providing confidence in the model structure and parameterization. A sensitivity analysis conducted using SSAM revealed compensatory relationships in energy balance terms that result in a small increase in net snowpack energy as stubble exposure increases. [ABSTRACT FROM AUTHOR]

Carbon flux phenology is widely used to understand carbon flux dynamics and surface exchange processes. Vegetation phenology has been widely evaluated by remote sensors; however, very few studies have evaluated the use of vegetation phenology for identifying carbon flux phenology. Currently available techniques to derive net ecosystem exchange (NEE) from a satellite image use a single generic modeling subgroup for agricultural crops. But, carbon flux phenological processes vary highly with crop types and land management practices; this paper reexamines this assumption. presented here are an evaluation of ground-truth remotely sensed vegetation indices with in situ NEE measurements and an identification of vegetation indices for estimating carbon flux phenology metrics by crop type. Results show that the performance of different vegetation indices as an indicator of phenology varies with crop type, particularly when identifying the start of a season and the peak of a season. Maize fields require vegetation indices that make use of the near-infrared and red reflectance bands, while soybean fields require those making use of the shortwave infrared (IR) and near-IR bands. In summary, the study identifies how to best utilize remote sensing technology as a crop-specific measurement tool. [ABSTRACT FROM AUTHOR]

Established as a social companion to the Oklahoma Mesonet, the Oklahoma Meso-Scale Integrated Socio-Geographic Network (M-SISNet) is a network of approximately 1500 'social monitoring stations' (geolocated households) across the state of Oklahoma that provide data on household perceptions and responses to signals that are sent from agricultural, hydrological, and meteorological systems. This paper outlines the purpose and nature of the M-SISNet, with specific focus on the sample frame, protocols for recruitment, retention, and survey implementation. It concludes with example survey questions, analyses, and directions for accessing the data. [ABSTRACT FROM AUTHOR]

Crop water stress monitoring by remote sensing has been the focus of numerous studies. In this paper, specifically red (630-690 nm) and shortwave infrared (SWIR; 1550-1750 nm) wavelength bands are identified to monitor farmland water stress, and a method [modified shortwave infrared perpendicular water stress index (MSPSI)] is developed that is based on the spectral space constructed by SWIR − Red ( Rd) and SWIR + Red ( Rs). The MSPSI stayed at mostly the same water stress level for full vegetation coverage cases with high vegetation water content and saturated bare soil as well as full vegetation coverage with extremely low vegetation water and dry bare soil in the Rs- Rd spectral feature space. This approach makes the water stress conditions between different covers comparable and the MSPSI applicable to farmland water stress monitoring in different vegetation covers throughout the growing season. To validate the proposed index, the MSPSI calculated from Thematic Mapper images and Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m reflectance products (from March to October) in the Ningxia Hui Autonomous Region was compared with the ground-measured soil moisture content at different depths. It is evident from the results that the MSPSI derived from satellite imageries is highly correlated with ground-measured soil moisture at different depths (7.6 and 10 cm), with coefficients of determination R2 of 0.666, 0.512, 0.576, 0.361, 0.383, 0.391, 0.357, 0.410, and 0.418. The paper concludes that MSPSI is a promising index for crop water stress monitoring throughout the growing season. [ABSTRACT FROM AUTHOR]

Advanced information in the form of seasonal climate forecasts has the potential to improve farmers’ decision making, leading to increases in farm profits. Interdisciplinary initiatives seeking to understand and exploit the potential benefits of seasonal forecasts for agriculture have produced a number of quantitative ex-ante assessments of the economic value of seasonal climate forecasts. The realism, robustness, and credibility of such assessments become increasingly important as efforts shift from basic research toward applied research and implementation. This paper surveys published evidence about the economic value of seasonal climate forecasts for agriculture, characterizing the agricultural systems, approaches followed, and scales of analysis. The climate forecast valuation literature has contributed insights into the influence of forecast characteristics, risk attitudes, insurance, policy, and the scale of adoption on the value of forecasts. Key innovations in the more recent literature include explicit treatment of the uncertainty of forecast value estimates, incorporation of elicited management responses into bioeconomic modeling, and treatment of environmental impacts, in addition to financial outcomes of forecast response. It is argued that the picture of the value of seasonal forecasts for agriculture is still incomplete and often biased, in part because of significant gaps in published valuation research. Key gaps include sampling of a narrow range of farming systems and locations, incorporation of an overly restricted set of potential management responses, failure to consider forecast responses that could lead to “regime shifts,” and failure to incorporate state-of-the-art developments in seasonal forecasting. This paper concludes with six recommendations to enhance the realism, robustness, and credibility of ex-ante valuation of seasonal climate forecasts. [ABSTRACT FROM AUTHOR]

This paper explores the link between the anomalous warming hole in the southeastern United States and a major land-use/land-cover (LULC) change in the region. Land surface and satellite observations were analyzed to estimate the net radiative forcing due to LULC change. Albedo and latent energy were specifically addressed for the dominant LULC change of agriculture to forests. It was assumed that in the energy-limited environment of the region, the partition of changes in available energy due to albedo will mostly impact the sensible heat. The results show that in the southeastern United States, for the period of 1920 to 1992, the changes in sensible (as a result of albedo) and latent energies are in direct competition with each other. In the spring and early summer months, the croplands are in peak production and the latent energy associated with their evapotranspiration (ET) is comparable to that of the forests so the decrease in radiation due to albedo dominates the signal. However, during the late summer and fall months, most major crops have matured, thus reducing their transpiration rate while forests (particularly evergreens) maintain their foliage and with their deep roots are able to continue to transpire as long as atmospheric conditions are favorable. This later influence of latent energy appears to more than offset the increased radiative forcing from the spring and early summer. Overall, a mean annual net radiative forcing resulting from a LULC change from cropland to forests was estimated to be −1.06 W m−2 and thus a probable contribution to the 'warming hole' over the Southeast during the majority of the twentieth century. [ABSTRACT FROM AUTHOR]

This paper presents a detailed analysis of how future climate change may affect water availability in a typical arid endorheic river basin, the Heihe River basin (HRB), in northwest China. The analysis is based on the improved Soil Water Assessment Tool (SWAT), which is calibrated and validated with historical streamflow data from the upper HRB and is used to predict future hydrological responses. Six general circulation models (GCMs), under two emission scenarios (RCP4.5 and RCP8.5), are downscaled to construct future climate change scenarios. The results suggest that the climate of the upper HRB will likely become warmer and wetter in the near future (2021-50), with the largest increase in precipitation occurring in the summer. Correspondingly, the basinwide evapotranspiration, snowmelt, and runoff are projected to increase over the same period. The mean temperature in the near future is projected to rise, relative to the recent 30 years (1981-2010), by 1.2°-1.7°C under scenario RCP4.5 and by 1.4°-2.1°C under scenario RCP8.5. The mean precipitation is projected to increase by 10.0%-16.6% under scenario RCP4.5, and by 10.5%-22.0% under scenario RCP8.5. The mean values of evapotranspiration, snowmelt, and runoff are expected to increase by 14.2%, 4.3%, and 11.4%, respectively, under scenario RCP4.5 and to increase by 18.7%, 5.8%, and 12.8%, respectively, under scenario RCP8.5. Though the model simulations forecast an increase in streamflows in the headwater region of the HRB, future water availability varies significantly over space and time. The findings of this study will help to frame more effective water management strategies for the HRB under changing climatic conditions. [ABSTRACT FROM AUTHOR]

Despite considerable interest in the application of land surface data assimilation systems (LDASs) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model, and a sequential data assimilation filter) against a series of linear models that perform the same function (i.e., have the same basic input/output structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moisture/NDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which nonlinearities and/or complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model, and an ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system. [ABSTRACT FROM AUTHOR]

Soil temperature can exhibit considerable memory from weather and climate signals and is among the most important initial conditions in numerical weather and climate models. Consequently, a more accurate long-term land surface soil temperature dataset is needed to improve weather and climate simulation and prediction, and is also important for the simulation of agricultural crop yield and ecological processes. The North American Land Data Assimilation phase 2 (NLDAS-2) has generated 31 years (1979-2009) of simulated hourly soil temperature data with a spatial resolution of ⅛°. This dataset has not been comprehensively evaluated to date. Thus, the purpose of this paper is to assess Noah-simulated soil temperature for different soil depths and time scales. The authors used long-term (1979-2001) observed monthly mean soil temperatures from 137 cooperative stations over the United States to evaluate simulated soil temperature for three soil layers (0-10, 10-40, and 40-100 cm) for annual and monthly time scales. Short-term (1997-99) observed soil temperatures from 72 Oklahoma Mesonet stations were used to validate simulated soil temperatures for three soil layers and for daily and hourly time scales. The results showed that the Noah land surface model generally matches observed soil temperature well for different soil layers and time scales. At greater depths, the simulation skill (anomaly correlation) decreased for all time scales. The monthly mean diurnal cycle difference between simulated and observed soil temperature revealed large midnight biases in the cold season that are due to small downward longwave radiation and issues related to model parameters. [ABSTRACT FROM AUTHOR]

Impacts of climate change on agriculture are a major concern worldwide, but uncertainties of climate models and emission scenarios may hamper efforts to adapt to climate change. In this paper, a probabilistic approach is used to estimate the uncertainties and simulate impacts of global warming on wheat production and water use in the main wheat cultivation regions of China, with a global mean temperature (GMT) increase scale relative to 1961-90 values. From output of 20 climate scenarios of the Intergovernmental Panel on Climate Change Data Distribution Centre, median values of projected changes in monthly mean climate variables for representative stations are adapted. These are used to drive the Crop Environment Resource Synthesis (CERES)-Wheat model to simulate wheat production and water use under baseline and global warming scenarios, with and without consideration of carbon dioxide (CO2) fertilization effects. Results show that, because of temperature increase, projected wheat-growing periods for GMT changes of 1°, 2°, and 3°C would shorten, with averaged median values of 3.94%, 6.90%, and 9.67%, respectively. There is a high probability of decreasing (increasing) changes in yield and water-use efficiency under higher temperature scenarios without (with) consideration of CO2 fertilization effects. Elevated CO2 concentration generally compensates for the negative effects of warming temperatures on production. Moreover, positive effects of elevated CO2 concentration on grain yield increase with warming temperatures. The findings could be critical for climate-change-driven agricultural production that ensures global food security. [ABSTRACT FROM AUTHOR]

In many regions of the world, the extremes of winter cold are a major determinant of the geographic distribution of perennial plant species and of their successful cultivation. In the United States, the U.S. Department of Agriculture (USDA) Plant Hardiness Zone Map (PHZM) is the primary reference for defining geospatial patterns of extreme winter cold for the horticulture and nursery industries, home gardeners, agrometeorologists, and plant scientists. This paper describes the approaches followed for updating the USDA PHZM, the last version of which was published in 1990. The new PHZM depicts 1976-2005 mean annual extreme minimum temperature, in 2.8°C (5°F) half zones, for the conterminous United States, Alaska, Hawaii, and Puerto Rico. Station data were interpolated to a grid with the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) climate-mapping system. PRISM accounts for the effects of elevation, terrain-induced airmass blockage, coastal effects, temperature inversions, and cold-air pooling on extreme minimum temperature patterns. Climatologically aided interpolation was applied, based on the 1971-2000 mean minimum temperature of the coldest month as the predictor grid. Evaluation of a standard-deviation map and two 15-yr maps (1976-90 and 1991-2005 averaging periods) revealed substantial vertical and horizontal gradients in trend and variability, especially in complex terrain. The new PHZM is generally warmer by one 2.8°C (5°F) half zone than the previous PHZM throughout much of the United States, as a result of a more recent averaging period. Nonetheless, a more sophisticated interpolation technique, greater physiographic detail, and more comprehensive station data were the main causes of zonal changes in complex terrain, especially in the western United States. The updated PHZM can be accessed online (). [ABSTRACT FROM AUTHOR]

Despite tremendous efforts to improve weather and climate predictions and to inform farmers about the use of such weather products, farmers’ attitudes toward forecast use remain poor and farmer use of forecasts has not increased. This paper describes features of a new conceptual model for facilitating farmers’ use of weather products and offers preliminary evidence for its effectiveness based on a test-of-concept prototype. The prototype system provides farmers with contextualized information, the opportunity to use that information in relevant farming contexts, and collaborative interaction with other users. In addition, scaffolding and feedback are incorporated in the model to enhance learning and motivation. Surveys before and after use of the prototype system, and focus-group discussion after system use, were conducted to obtain evaluations from 15 farmers in southeastern Nebraska. Farmers’ evaluations of the system were moderately positive and indicated greater intentions to use the products in the future than they had in the past. However, farmers only slightly increased their positive expectancies of various general categories of weather and climate products, supporting the difficulties associated with changing overall attitudes when attempting to transfer scientific improvements into practical uses. It is suggested that multiple exposures to such a system and more individualized and personally relevant use opportunities may further enhance the power of the proposed model. [ABSTRACT FROM AUTHOR]

There are a number of stochastic models that simulate weather data required for various water resources applications in hydrology, agriculture, ecosystem, and climate change studies. However, many of them ignore the dependence between station locations exhibited by the observed meteorological time series. This paper proposes a multisite generation approach of daily precipitation data based on the concept of spatial autocorrelation. This theory refers to spatial dependence between observations with respect to their geographical adjacency. In hydrometeorology, spatial autocorrelation can be computed to describe daily dependence between the weather stations through the use of a spatial weight matrix, which defines the degree of significance of the weather stations surrounding each observation. The methodology is based on the use of the spatial moving average process to generate spatially autocorrelated random numbers that will be used in a stochastic weather generator. The resulting precipitation processes satisfy the daily spatial autocorrelations computed using the observed data. Monthly relationships between the spatial moving average coefficients and daily spatial autocorrelations of the precipitation processes have been developed to find the spatial moving average coefficients that reproduce the observed daily spatial autocorrelations in the synthetic precipitation processes. To assess the effectiveness of the proposed methodology, seven stations in the Peribonca River basin in the Canadian province of Quebec were used. The daily spatial autocorrelations of both precipitation occurrences and amounts were adequately reproduced, as well as the total monthly precipitations, the number of rainy days per month, and the daily precipitation variance. Using appropriate weight matrices, the proposed multisite approach permits one not only to reproduce the spatial autocorrelation of precipitation between the set of stations, but also the interstation correlation of precipitation between each pair of stations. [ABSTRACT FROM AUTHOR]

Real-time rainfall monitoring in Africa is of great practical importance for operational applications in hydrology and agriculture. Satellite data have been used in this context for many years because of the lack of surface observations. This paper describes an improved artificial neural network algorithm for operational applications. The algorithm combines numerical weather model information with the satellite data. Using this algorithm, daily rainfall estimates were derived for 4 yr of the Ethiopian and Zambian main rainy seasons and were compared with two other algorithms—a multiple linear regression making use of the same information as that of the neural network and a satellite-only method. All algorithms were validated against rain gauge data. Overall, the neural network performs best, but the extent to which it does so depends on the calibration/validation protocol. The advantages of the neural network are most evident when calibration data are numerous and close in space and time to the validation data. This result emphasizes the importance of a real-time calibration system. [ABSTRACT FROM AUTHOR]

This paper analyzes the long-term (1901–2002) temporal trends in the agroclimate of Alberta, Canada, and explores the spatial variations of the agroclimatic resources and the potential crop-growing area in Alberta. Nine agroclimatic parameters are investigated: May–August precipitation (PCPN), the start of growing season (SGS), the end of the growing season (EGS), the length of the growing season (LGS), the date of the last spring frost (LSF), the date of the first fall frost (FFF), the length of the frost-free period (FFP), growing degree-days (GDDs), and corn heat units (CHUs). The temporal trends in the agroclimatic parameters are analyzed by using linear regression. The significance tests of the trends are made by using Kendall’s tau method. The results support the following conclusions. 1) The Alberta PCPN has increased 14% from 1901 to 2002, and the increment is the largest in the north and the northwest of Alberta, then diminishes (or even becomes negative over two small areas) in central and southern Alberta, and finally becomes large again in the southeast corner of the province. 2) No significant long-term trends are found for the SGS, EGS, and LGS. 3) An earlier LSF, a later FFF, and a longer FFP are obvious all over the province. 4) The area with sufficient CHU for corn production, calculated according to the 1973–2002 normal, has extended to the north by about 200–300 km, when compared with the 1913–32 normal, and by about 50–100 km, when compared with the 1943–72 normal; this expansion implies that the potential exists to grow crops and raise livestock in more regions of Alberta than was possible in the past. The annual total precipitation follows a similar increasing trend to that of the May–August precipitation, and the percentile analysis of precipitation attributes the increase to low-intensity events. The changes of the agroclimatic parameters imply that Alberta agriculture has benefited from the last century’s climate change. [ABSTRACT FROM AUTHOR]

The United States is currently responsible for 40%–45% of the world’s corn supply and 70% of total global exports [the U.S. Department of Agriculture–National Agricultural Statistics Service (USDA–NASS)]. Therefore, analyses of the spatial and temporal patterns of historical U.S. corn yields might provide insight into future crop-production potential and food security. In this study, county-level maize yield data from 1910 to 2001 were used to characterize the spatial heterogeneity of yield growth rates and interannual yield variability across the U.S. Corn Belt. Widespread decadal-scale changes in corn yield variability and yield growth rates have occurred since the 1930s across the Corn Belt, but the response has varied substantially with geographic location. Northern portions of the Great Plains have experienced consistently high interannual corn yield variability, averaging 30%–40% relative to the mean. Increasing usage of irrigation in Nebraska, Kansas, and Texas, since the 1950s, has helped boost yields by 75%–90% over rain-fed corn, creating a yield gap of 2–4 T ha-1 between irrigated and nonirrigated corn that could potentially be exploited in other regions. Furthermore, irrigation has reduced interannual variability by a factor of 3 in these same regions. A small region from eastern Iowa into northern Illinois and southern Wisconsin has experienced minimal interannual yield variability, averaging only 6%–10% relative to mean yields. This paper shows that the choice of time period used for statistical analysis impacted conclusions drawn about twentieth-century trends in corn yield variability. Widespread increases in yield variability were apparent from 1950 onward, but were not significant over the entire 1930–2001 period. There is also evidence that yield variability decreased from the early 1990s to 2001. Corn yield growth rates peaked at an annual-average rate of 3%–5% in the 1960s (124.5 kg ha-1 yr-1), but have steadily declined to a relative rate of 0.78% yr-1 (49.2 kg ha-1 yr-1) during the 1990s. A general inverse relationship between increasing corn yield and decreasing yield growth rates was noted after county-level yields reached 4 T ha-1, suggesting that widespread, significant increases in corn yield are not likely to take place in the future, particularly on irrigated land, without a second agricultural revolution. [ABSTRACT FROM AUTHOR]

During the twentieth century, the natural landscape of the Florida peninsula was transformed extensively by agriculture, urbanization, and the diversion of surface water features. The purpose of this paper is to present a numerical modeling study in which the possible impacts of this transformation on the warm season climate of the region were investigated. For three separate July–August periods (1973, 1989, and 1994), a pair of simulations was performed with the Regional Atmospheric Modeling System. Within each pair, the simulations differed only in the specification of land-cover class. The two different classes were specified using highly detailed datasets that were constructed to represent pre-1900 natural land cover and 1993 land-use patterns, thus capturing the landscape transformation within each pair of simulations. When the pre-1900 natural cover was replaced with the 1993 land-use dataset, the simulated spatial patterns of the surface sensible and latent heat flux were altered significantly, resulting in changes in the structure and strength of climatologically persistent, surface-forced mesoscale circulations—particularly the afternoon sea-breeze fronts. This mechanism was associated with marked changes in the spatial distribution of convective rainfall totals over the peninsula. When averaged over the model domain, this redistribution was reflected as an overall decrease in the 2-month precipitation total. In addition, the domain average of the diurnal cycle of 2-m temperature was amplified, with a noted increase in the daytime maximum. These results were consistent among all three simulated periods, and largely unchanged when subjected to a number of model sensitivity factors. Furthermore, the model results are in reasonable agreement with an analysis of observational data that indicates decreasing regional precipitation and increasing daytime maximum temperature during the twentieth century. These results could have important implications for water resource and land-use management issues in south Florida, including efforts to restore and preserve the natural hydroclimate of the Everglades ecosystem. This study also provides more evidence for the need to consider anthropogenic land-cover change when evaluating climate trends. [ABSTRACT FROM AUTHOR]