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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]

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]

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]

This paper examines decision making under climate risks using farmers' decisions in sub-Saharan Africa, where climate risks are very high. Two risk measures are obtained from the Climate Research Unit's high-resolution climatology, diurnal temperature range (DTR) and coefficient of variation in precipitation (CVP), which are both averages of 30-yr climate data. Farm surveys of around 7600 households were matched cell by cell with the climate risk data. This paper finds that climate risks are indeed highest in the lowland arid zones in the Sahel. A spatial logit analysis shows that farmers in sub-Saharan Africa have adapted their agricultural systems to varying degrees of the CVP and the DTR. In the long term, if the CVP were to increase by 30%, an integrated system would increase by 7.0%. On the other hand, the two specialized systems fall: a crops-only system falls by 5.3% and a livestock-only system falls by 1.7%. When the DTR increases, farmers adapt by switching to a specialized livestock system. Under increased climate risks, this paper finds that farmers in the lowland savannahs and arid zones in the Sahel, where climate risks are high at present, will adapt by switching to an integrated system. Studies of climate risks, therefore, must account for behavioral responses of the individuals. These results can be utilized to help African farmers to adapt to increasing climate risks. [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]

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]

Billed as the creation and provision of timely, tailored information for decision making at all levels of society, climate services have garnered a great deal of attention in recent years. Despite this growing attention, strategies to design, diagnose, and evaluate climate services remain relatively ad hoc -- and while a general sense of what constitutes "good practice" in climate service provision is developing in some areas, and with respect to certain aspects of service provision, a great deal about the effective implementation of such service remains unknown. This paper reviews a sample of more than 100 climate service activities as a means to generate a snapshot of the state of the field in 2012. We find that a "typical climate service" at this time was provided by a national meteorological service operating on a national scale to provide seasonal climate information to agricultural decision makers online. Our analysis shows that the field of climate services was still emerging -- marked by contested definitions, an emphasis on capacity development, uneven progress toward co-production, uncertain funding streams, and a lack of evaluation activities -- and stands as a signpost against which the development of the field can be measured. The paper also reflects on the relative contribution of this sort of sampling activity in informing "good practice" and offers suggestions for how both sampling and case study efforts can be better designed to increase the potential for learning. The paper concludes with some observations on the relative contribution that broad- based analyses can play in informing this emerging field. [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]

Climate risk financing programs in agriculture have caught the attention of researchers and policy makers over the last decade. Weather index insurance has emerged as a promising market-based risk financing mechanism. However, to develop a suitable weather index insurance mechanism it is essential to incorporate the distribution of underlying weather and climate risks to a specific event model that can minimize intraseasonal basis risk. In this paper we investigate the erratic nature of rainfall patterns in Kenya using Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) rainfall data from 1983 to 2017. We find that the patterns of rainfall are fractional, both erratic and persistent, which is consistent with the Noah and Joseph effects that are well known in mathematics. The erratic nature of rainfall emerges from the breakdown of the convergence to a normal distribution. Instead we find that the distribution about the average is approximately lognormal, with an almost 50% higher chance of deficit rainfall below the mean than adequate rainfall above the mean. We find that the rainfall patterns obey the Hurst law and that the measured Hurst coefficients for seasonal rainfall pattern across all years range from a low of 0.137 to a high above 0.685. To incorporate the erratic and persistent nature of seasonal rainfall, we develop a new approach to weather index insurance based upon the accumulated rainfall in any 21-day period falling below 60% of the long-term average for that same 21-day period. We argue that this approach is more satisfactory to matching drought conditions within and between various phenological stages of growth. [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]

While climate services have the potential to reduce precipitation- and temperature-related risks to agrarian livelihoods, such outcomes are possible only when they deliver information that is salient, legitimate, and credible to end users. This is particularly true of climate services intended to address the needs of women in agrarian contexts. The design of such gender-sensitive services is hampered by oversimplified framings of women as a group in both the adaptation and climate services literatures. This paper demonstrates that even at the village level, women have different climate and weather information needs, and differing abilities to act on that information. Therefore, starting with preconceived connections between identities and vulnerability is likely to result in overgeneralizations that hinder the ability to address the climate-related development and adaptation needs of the most vulnerable. Instead, as is demonstrated in this paper, the design and implementation of effective gender-sensitive climate services must start with the relevant social differences that shape people's livelihoods decisions and outcomes, including but not limited to gender. [ABSTRACT FROM AUTHOR]

Producer organizations representing Canada's farm and livestock sectors are powerful change agents and advocates for their industries, particularly during challenging times such as climate- or weather-related hardships. Such organizations have a complex role: engaging with policy-makers, as well as their memberships and the public, to pursue the interests of their specific communities. This paper includes an examination of how farm producer organizations communicate about climate and weather to these various audiences, and the specific needs and recommendations they advance. Of particular interest are commodities related to pasture-based grazing, which is underrepresented in the climate adaptation literature. A collection of 95 publicly available documents is analyzed, representing a snapshot of climate- and weather-related public and policy engagement of Canadian and Albertan farm and livestock producer organizations from 2010 to 2015. Qualitative coding by scale, commodity, and audience revealed three significant patterns within this exploratory study. First, while national "umbrella" organizations speak climate to government, Alberta-based livestock/forage organizations speak to their members with a focus on weather. Second, while the two national umbrella organizations examined are politically divergent, they appear to be united on the topic of climate change. Third, common ground was also found between climate and weather discourses around on-farm management, specifically rotational grazing. These three patterns reveal a disjointed dialogue within the Canadian farm and livestock sectors on topics of climate adaptation and mitigation, as well as opportunities for future cooperation, and the need for further research on farm organization beliefs and their capacity to create/manage climate knowledge. [ABSTRACT FROM AUTHOR]

Weather derivatives are considered a promising agricultural risk management tool. Station-based meteorological indices typically provide the data underlying these instruments. However, the main shortcoming of these weather derivatives is an imperfect correlation between the weather index and the yield of the insured crop, called basis risk. This paper considers three available remotely sensed vegetation health (VH) indices, namely, the vegetation condition index (VCI), the temperature condition index (TCI), and the vegetation health index (VHI), as indices for weather derivatives in a German case study. We investigated the correlation and period of highest correlation with winter wheat yield. Moreover, we analyzed whether the use of remotely sensed VH indices for weather derivatives can reduce basis risk and thus improve the performance of weather derivatives. The two commonly used meteorological indices, precipitation and temperature sums, were employed as benchmarks. Quantile regression and index value simulation were used for the design and pricing of the weather derivatives. The analysis for the selected farms and corresponding counties in northeastern Germany revealed that, on average, the VHI resulted in the highest correlation with winter wheat yield, and VHI-based weather derivatives were also superior in terms of the hedging effectiveness. The total periods of the highest correlations ranged from the beginning of April to the end of July. VHI- and VCI-based weather derivatives led to statistically significant reductions of basis risk, compared to the benchmarks. Our results indicate that the VHI-based weather derivatives can be useful alternatives to meteorological indices, especially in regions with sparser weather station networks. [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]

Farmers stand to be greatly affected by changes in the climate, necessitating adaptive responses, yet little is documented on how U.S. Midwestern farmers understand and perceive climate change adaptation. Eight focus groups with 53 Michigan farmers were conducted in 2011-12 to better understand the following: 1) what do farmers think about the relationship between climate change and agriculture, 2) what differentiates normal weather-related management from climate change adaptation actions, and 3) how do farmers understand the term 'climate change adaptation.' Farmers expressed skepticism at global climate change yet conveyed specific details about the local changes in climate they are experiencing on their farms. They were not able to clearly define the term 'climate change adaptation' but did note specific adaptive actions they have already implemented. The farmers explained that nonclimate factors were of more concern to them when making management decisions, and they showed reactive (not proactive) actions toward adaptation. Farmers noted that any action they take has to address their specific situation, suggesting that generalized adaptation actions and language might not resonate with them. Building on quantitative surveys conducted by others, the findings in this paper contribute to ongoing efforts to more effectively assess farmers' perceptions related to climate change adaptation and to use that understanding to promote education, outreach, research, and public policies to more proactively address the consequences posed by climate change. [ABSTRACT FROM AUTHOR]

Exposure to weather extremes, such as heatwaves, can cause discomfort, harm, or death in grazing cattle in pastures. While the Australian Bureau of Meteorology issues sheep graziers alerts when there is an exposure risk to chill for livestock, there is no equivalent alert for heat stress for Australian cattle. Before any such alert system can be developed, a robust assessment and comparison of relevant cattle thermal stress indices is required. This study evaluates and compares the multiyear climatology of three cattle thermal heat stress indices across Australia in the warm season months (October–March). The same indices are then used to assess historical Australian heat events where cattle died from heat exposure. These events are based off official records and survey responses from northern Australian graziers. In the seven historical heat events studied, high relative humidity combined with low wind speeds, or high solar exposure combined with high surface temperatures, exacerbated the impact of heat stress on cattle. In the two historic events where multiple compounding weather factors combined (e.g., high humidity, low winds, and high solar exposure), the cattle mortality levels were significantly high. These events were characterized by rainy conditions followed by a rapid warming, meaning cattle were likely unable to acclimatize to such dramatic temperature changes. This study highlights the need for using more than one thermal stress index when verifying cattle heat stress events and, importantly, calls for further research on standardizing the risk classifications of these thermal indices for cattle in Australia's variable climate. Significance Statement: Cattle across Australia's northern tropical and semiarid regions often experience extreme hot and humid conditions in the summer months, which increases the risk of heat stress. This is the first study of its kind to evaluate observations of cattle heat stress across Australia using indices that describe the combined effects of solar exposure, wind speed, relative humidity, and surface temperatures. These cattle heat stress indices can be used to evaluate historical cattle mortality events in feedlots and in grazed pastures. This study lays the groundwork for the development of Australian-wide cattle heat stress forecast products on the 7-day to multiweek time scales. [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]

The main goal of this study, conducted in an area comprising 221 municipalities, in which 90% of the deforestation in the Legal Amazon takes place, was to understand the role of the agrarian structure in the conversion of forest into pasture and agriculture fields. Linear regression results indicate that 54%–62% of the variation in deforestation occurred between 1997 and 2004, respectively, and are explained as a function of changes in the amount of appropriated land in 1995. Likewise, up to 80% of the deforestation can be well explained by the variation in land concentration. In fact, strong spatial correlations were found between deforestation hotspots and land appropriation and land concentration. On the other hand, these critical areas have insufficient governance, particularly at the federal level. As the results of this study clearly demonstrate, strong governance and institutional integration, with emphasis on the territorial ordainment, are mandatory in order to reduce the rapid pace of deforestation in the Brazilian Amazon. [ABSTRACT FROM AUTHOR]

A new, high-resolution (4 km), gridded land surface dataset produced with the Land Information System (LIS) is introduced, and the first set of synthesis of key hydroclimatic variables is reported. The dataset is produced over a 33-yr time period (1980-2012) for the U.S. Midwest with the intent to aid the agricultural community in understanding hydroclimatic impacts on crop production and decision-making in operational practices. While approximately 20 hydroclimatic variables are available through the LIS dataset, the focus here is on soil water content, soil temperature, and evapotranspiration. To assess the performance of the model, the LIS dataset is compared with in situ hydrometeorological observations across the study domain and with coarse-resolution reanalysis products [NARR, MERRA, and NLDAS-2 (phase 2 of the North American Land Data Assimilation System)]. In agricultural regions such as the U.S. Midwest, finescale hydroclimatic mapping that links the regional scale to the field scale is necessary. The new dataset provides this link as an intermediate-scale product that links point observations and coarse gridded datasets. In general, the LIS dataset compares well with in situ observations and coarser gridded products in terms of both temporal and spatial patterns, but cases of strong disagreement exist particularly in areas with sandy soils. The dataset is made available to the broader research community as an effort to fill the gap in spatial hydroclimatic data availability. [ABSTRACT FROM AUTHOR]

Fieldwork was conducted in 2009-11 with Native American agriculturalists and traditionalists in southwestern Oklahoma on the form and use of their weather and climate knowledge: how it is constituted, how it is used in agricultural decision-making, and the extent to which their own weather knowledge is valued in light of other forms of weather information available. Conversations rekindled memories of knowledge imparted and stories told by previous generations and illuminated observational signs some still rely upon. Conversations also revealed that while the agriculturalists and traditionalists are acculturated into contemporary society, they still hold tightly to culturally important ways of knowing about and being in the world and are keen observers of their local environments. Given the contemporary relevance of climate change and its impacts on Indigenous peoples, this paper focuses on their observations and thoughts about climate variability: Have you noticed changes? What impacts do these changes have on you, including both on your agricultural activities and your trusted observational signs? What do you think about the public discourse on climate change? According to most, a changing climate is a real phenomenon perceived at the local scale and is impacting their ability to observe relied-upon indicators, and has caused them to make changes in their agricultural endeavors. The knowledge professed by those in this study can be placed within the larger context of Indigenous environmental knowledge formation, as it follows closely with that discovered through archival research and that described by other inquiries from around the world. [ABSTRACT FROM AUTHOR]

The rising emissions of reactive nitrogen (Nr) from transport, industrial, and agricultural sectors in India have resulted in its consequent interactions with the removal mechanism of the atmospheric dust. This study, therefore, reports the fluxes of reactive nitrogen along with other inorganic species through dustfall over six sites of Delhi-National Capital Region (NCR) characterized by the changing dynamics of its different land-use pattern. The highest Nr fluxes were observed at site SMA Industrial estate (SMA; NO3− = 16.45 ± 10.17 mg m−2 day−1, NH4+ = 16.33 ± 16.00 mg m−2 day−1) and lowest at site Chuchchakwas village (CV; NO3− = 1.24 ± 0.16 mg m−2 day−1, NH4+ = 0 mg m−2 day−1). Sites Mukherjee Nagar (MN), Peeragarhi Chowk (PC), Jawaharlal Nehru University (JNU), and Noida Phase II (N-II), on the other hand, showed 3.59 ± 1.00, 3.39 ± 0.61, 2.98 ± 0.84, and 3.36 ± 0.78 mg m−2 day−1 of NO3− fluxes and 0.30 ± 0.06, 0.22 ± 0.04, 0.21 ± 0.04, and 0.22 ± 0.05 mg m−2 day−1 of NH4+ fluxes, respectively. The fraction of the total ions in the water soluble extract of the dustfall was also noticed to be the highest at the SMA site (22.2%) and lowest at the CV site (1.5%) with MN, PC, JNU, and N-II showing 3.5%, 3.7%, 2.9%, and 3.9% of their respective contributions. Relative abundances of Ca2+ and SO42− in the dustfall substantiated the stoichiometric reactions involved in Nr scavenging. The role of Ca2+ in the spatiotemporal variability of Nr fluxes was established with the help of neutralization ratios and regression plots. Morphological and particle size analysis further confirmed the anthropogenic-induced crustal interferences in the summertime dustfall fluxes of Nr species. [ABSTRACT FROM AUTHOR]

Water scarcity threatens agriculture in California. During the last two decades, historically severe droughts have led to severe water shortages. Under projected changes in climate, droughts of greater severity and duration will exacerbate this situation. California produces 80% of the world's almonds, which require consistent water supplies for irrigation. Almonds are the most commonly grown crop in California, covering nearly 1.4 million acres over about 8,000 farms. In response to these challenges, almond growers are considering a myriad of management strategies to save water and mitigate climate change. The Tree-Crop Remote Sensing of Evapotranspiration Experiment (T-REX) aims to identify water and orchard management opportunities to maximize water use efficiency and carbon sequestration in almonds and other woody perennial tree crops. The project combines satellite, uncrewed aerial vehicles, and proximal sensing technologies to retrieve key variables used to model surface fluxes and biophysical properties. We aim to advance our understanding of water management and cultural practices on water-carbon relationships in tree-perennial agroecosystems. Through new methods, such as evapotranspiration-based irrigation scheduling, even a modest 10% decrease in almond orchard irrigation across the state equates to about a third of the water in Lake Oroville, California's second-largest reservoir, at average levels. From a carbon perspective, almond orchards could sequester 8% of the state's current greenhouse gas emissions by transitioning toward climate-smart practices. As such, the almond industry is uniquely positioned to curb water use and contribute to climate change mitigation while maintaining economic viability of almond production. An overview of initial results related to evapotranspiration observational and modeling uncertainty and carbon sequestration potential are presented in this article. [ABSTRACT FROM AUTHOR]

Phenological indicators (PI) are used to study changes to animal and plant behavior in response to seasonal cycles, and they can be useful to quantify the potential impacts of climate change on ecosystems. Here, multiple global climate models and emission scenarios are used to drive dynamically downscaled simulations using the WRF Model over the contiguous United States (CONUS). The wintertime dormancy of plants [chilling units (CU)], timing of spring onset [extended spring indices (SI)], and frequency of proceeding false springs are calculated from regional climate simulations covering historical (1995-2005) and future periods (2025-2100). Southern parts of the CONUS show projected CU decreases (inhibiting some plants from flowering or fruiting), while the northern CONUS experiences an increase (possibly causing plants to break dormancy too early, becoming vulnerable to disease or freezing). Spring advancement (earlier SI dates) is projected, with decadal trends ranging from approximately 1-4 days per decade over the CONUS, comparable to or exceeding those found in observational studies. Projected changes in risk of false spring (hard freezes following spring onset) vary across members of the ensemble and regions of the CONUS, but generally western parts of the CONUS are projected to experience increased risk of false springs. These projected changes to PI connote significant effects on cycles of plants, animals, and ecosystems, highlighting the importance of examining temperature changes during transitional seasons. SIGNIFICANCE STATEMENT: This study examines how phenological indicators, which track the life cycles of plants and animals, could change from 2025 to 2100 as simulated in a regional climate model over the contiguous United States. Chilling units quantify the presence of cooler weather that can benefit plants prior to their growing season. They are projected to decrease in the southern United States, possibly inhibiting agricultural production. Spring onset is projected to occur earlier in the year, advancing by 1-4 days on average over each future decade. Risk of false springs (damaging hard freezes after spring onset) increases in the western United States. Our findings highlight the need to understand effects of climate change during transitional seasons, which can impact agriculture and ecosystems. [ABSTRACT FROM AUTHOR]

Since meteorological data have been regularly recorded in Hungary, 2010 has been the rainiest year, whereas 2011 and 2012 have been the driest years. Protected grasslands and their management by low-intensity livestock farming are seriously affected by climatic changes. This paper seeks to shed light on stakeholder perceptions of recent extreme weather events, in order to better understand how to adapt the management of vulnerable habitats to anticipated changes. The status of vulnerable habitats is strongly affected by land use change as well; therefore, this process requires stakeholder involvement. Opinions on what aspects are presumed to be affected most by changes in climate were gathered during semistructured interviews from farmers and conservationists in the Körös-Maros National Park in Hungary. Stakeholders revealed negative impacts resulting from extreme floods, droughts, and changes in rainfall and temperature, and they highlighted challenges relating to conflicting management objectives and perverse incentives arising from policy-level issues (such as regulatory structures and implementation). Subjects noted that years with high precipitation caused adverse conditions for livestock, late mowing due to inaccessible pastures, poor quality of hay, and undergrazing (but still compaction damage). Years with negative precipitation records show decreased quantity and quality of hay, loss of valuable forage, winter shortage of green fodder, land degradation, increased water demand, shifting grazing seasons, decreasing carrying capacity, overgrazing, heat stress, declines in physical activities, expansion of invasive alien species, and conflicts between economic interests and nature conservation objectives. In only a few cases was the awareness of these issues converted into practical adaptation or mitigation activities. [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]

Northeast China (NEC) is one of the major agricultural production areas in China, producing about 30% of China's total maize output. In the past five decades, maize yields in NEC increased rapidly. However, farmer yields still have potential to be increased. Therefore, it is important to quantify the impacts of agronomic factors, including soil physical properties, cultivar selections, and management practices on yield gaps of maize under the changing climate in NEC in order to provide reliable recommendations to narrow down the yield gaps. In this study, the Agricultural Production Systems Simulator (APSIM)-Maize model was used to separate the contributions of soil physical properties, cultivar selections, and management practices to maize yield gaps. The results indicate that approximately 5%, 12%, and 18% of potential yield loss of maize is attributable to soil physical properties, cultivar selection, and management practices. Simulation analyses showed that potential ascensions of yield of maize by improving soil physical properties PAY s, changing to cultivar with longer maturity PAY c, and improving management practices PAY m for the entire region were 0.6, 1.5, and 2.2 ton ha−1 or 9%, 23%, and 34% increases, respectively, in NEC. In addition, PAY c and PAY m varied considerably from location to location (0.4 to 2.2 and 0.9 to 4.5 ton ha−1 respectively), which may be associated with the spatial variation of growing season temperature and precipitation among climate zones in NEC. Therefore, changing to cultivars with longer growing season requirement and improving management practices are the top strategies for improving yield of maize in NEC, especially for the north and west areas. [ABSTRACT FROM AUTHOR]

The rapid expansion of irrigation since the 1950s has significantly depleted the Ogallala Aquifer. This study examines the warm-season climate impacts of irrigation over the Ogallala using high-resolution (6.33 km) simulations of a version of the Weather Research and Forecasting (WRF) Model that has been coupled to the Community Land Model with dynamic crop growth (WRF-CLM4crop). To examine how dynamic crops influence the simulated impact of irrigation, the authors compare simulations with dynamic crops to simulations with a fixed annual cycle of crop leaf area index (static crops). For each crop scheme, simulations were completed with and without irrigation for 9 years that represent the range of observed precipitation. Reduced temperature and precipitation biases occur with dynamic versus static crops. Fundamental differences in the precipitation response to irrigation occur with dynamic crops, as enhanced surface roughness weakens low-level winds, enabling more water from irrigation to remain over the region. Greater simulated rainfall increases (12.42 mm) occur with dynamic crops compared to static crops (9.08 mm), with the greatest differences during drought years (+20.1 vs +5.9 mm). Water use for irrigation significantly impacts precipitation with dynamic crops ( R2 = 0.29), but no relationship exists with static crops. Dynamic crop growth has the largest effect on the simulated impact of irrigation on precipitation during drought years, with little impact during nondrought years, highlighting the need to simulate the dynamic response of crops to environmental variability within Earth system models to improve prediction of the agroecosystem response to variations in climate. [ABSTRACT FROM AUTHOR]

Detailed parameter sensitivity, model validation, and regional calibration of the Hybrid-Maize crop model were undertaken for the purpose of regional agroclimatic assessments. The model was run at both field scale and county scale. The county-scale study was based on 30-yr daily weather data and corn yield data from the National Agricultural Statistics Service survey for 24 locations across the Corn Belt of the United States. The field-scale study was based on AmeriFlux sites at Bondville, Illinois, and Mead, Nebraska. By using the one-at-a-time and interaction-explicit factorial design approaches for sensitivity analysis, the study found that the five most sensitive parameters of the model were potential number of kernels per ear, potential kernel filling rate, initial light use efficiency, upper temperature cutoff for growing degree-days' accumulation, and the grain growth respiration coefficient. Model validation results show that the Hybrid-Maize model performed satisfactorily for field-scale simulations with a mean absolute error (MAE) of 10 bu acre−1 despite the difficulties of obtaining hybrid-specific information. At the county scale, the simulated results, assuming optimal crop management, overpredicted the yields but captured the variability well. A simple regional adjustment factor of 0.6 rescaled the potential yield to actual yield well. These results highlight the uncertainties that exist in applying crop models at regional scales because of the limitations in accessing crop-specific information. This study also provides confidence that uncertainties can potentially be eliminated via simple adjustment factor and that a simple crop model can be adequately useful for regional-scale agroclimatic studies. [ABSTRACT FROM AUTHOR]

More severe droughts in the United States will bring great challenges to irrigation water supply. Here, the authors assessed the potential adaptive effects of irrigation infrastructure under present and more extensive droughts. Based on data over 1985-2005, this study established a statistical model that suggests around 4.4% more irrigation was applied in response to a one-unit reduction in the Palmer drought severity index (PDSI), and approximately 5.0% of irrigation water application could be saved for each 10% decrease in the areas supplied by surface irrigation infrastructure. Based on the results, the model-projected irrigation infrastructure has played a greater role in changes in irrigation than drought in most areas under the current climate except some southwestern counties. However, under the predicted future more severe drought in 2080-99 under the representative concentration pathways 4.5 scenario, the model projected that the drought will require 0%-20% greater irrigation amounts assuming the current irrigation efficiency. Under the predicted drought scenario, irrigation depth can be maintained at or below the baseline level in the western United States only when better irrigation infrastructure replaced 40% of the current surface irrigation infrastructure areas. In the northeast United States, limited changes in irrigation depth were predicted under different irrigation infrastructure scenarios because the percentage of surface irrigation area is already low under the baseline climate, and thus there is limited opportunity to adapt to future drought with advanced irrigation infrastructure. These results indicate that other effective solutions are required to complement these measures and aid U.S. agriculture in the future, more extensive drought. [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO) and Arctic Oscillation (AO) climatology (1980-2010) is developed and analyzed across the U.S. Corn Belt using state climate division weather and historic corn yield data using analysis of variance (ANOVA) and correlation analysis. Findings provide insight to agroclimatic conditions under different ENSO and AO episodes and are analyzed with a perspective for potential impacts to agricultural production and planning, with findings being developed into a web-based tool for the U.S. Corn Belt. This study is unique in that it utilizes the oceanic Niño index and explores two teleconnection patterns that influence weather across different spatiotemporal scales. It is found that the AO has a more frequent weak to moderate correlation to historic yields than ENSO when correlated by average subgrowing season index values. Yield anomaly and ENSO and AO episode analysis affirms the overall positive impact of El Niño events on yields compared to La Niña events, with neutral ENSO events in between as found in previous studies. Yields when binned by the AO episode present more uncertainty. While significant temperature and precipitation impacts from ENSO and AO are felt outside of the primary growing season, correlation between threshold variables of episode-specific temperature and precipitation and historic yields suggests that relationships between ENSO and AO and yield are present during specific months of the growing season, particularly August. Overall, spatial climatic variability resulting from ENSO and AO episodes contributes to yield potential at regional to subregional scales, making generalization of impacts difficult and highlighting a continued need for finescale resolution analysis of ENSO and AO signal impacts on corn production. [ABSTRACT FROM AUTHOR]

We analyze a provincial-scale dataset of winter wheat yield, together with finescale daily weather outcomes from 1979 to 2011, to assess the responses of winter wheat yield in China to temperature fluctuations. Contrary to the majority of the previous literature, we find that winter wheat yield in China responded positively to higher nighttime temperature Tmin, with the positive Tmin effects most significant in the northern China winter wheat region. Consistent with the previous studies, winter wheat yield in China exhibited negative responses to higher daytime temperature Tmax. As a result of these opposing temperature effects on yield, the net economic impact of weather variations on China's winter wheat sector is uncertain and is sensitive to specifications and data. Average winter wheat yield is projected to decline by 5.3%–7.0% by 2050 under the global climate model HadGEM2-ES and by 2.0%–3.4% under the NorESM1-M model. [ABSTRACT FROM AUTHOR]

This research focuses on the adaptability of olive-growing systems to climate change in the Sierra Mágina region of Andalusia. The authors combined a retrospective and prospective analysis, an interdisciplinary approach, collaboration among climatologists, geographers, and sociologists, and the participation of local farmers and stakeholders, all contributing their own knowledge. This paper assesses the adaptability of olive-growing systems to climate irregularities over the past 50 yr. First, a climatic study shows that rainfall decreased by 18% during the period 1955-2009. Water resource availability has declined 2 or 3 times more than rainfall, in part because of the expansion of irrigation, which ameliorated the effects of droughts and increased profitability. Second, relationships between rainfall and both irrigated and rainfed olive yields are assessed. These show that the cumulative rainfall of the 2 yr preceding the crop explains 41% of the variability of irrigated olive tree yields and 46% of rainfed yields; this result was unexpected for irrigated yields. Third, this study examines the perceptions of climate variability of 15 farmers, the views of 16 local stakeholders [developers, olive oil professionals, local authorities, a conservationist, and a representative of a local nongovernmental organization (NGO)]. The perceptions of the farmers are interpreted with respect to their socioeconomic status. All farmer and stakeholder interviewees know to a certain extent the climatic influence on olive yields, and most of them acknowledge the recent climatic changes. These findings will be valuable for future assessments of the adaptability of the agricultural and social systems to climate change. [ABSTRACT FROM AUTHOR]

The devastating civil war that began in Syria in March 2011 is the result of complex interrelated factors. The focus of the conflict is regime change, but the triggers include a broad set of religious and sociopolitical factors, the erosion of the economic health of the country, a wave of political reform sweeping over the Middle East and North Africa (MENA) and Levant region, and challenges associated with climate variability and change and the availability and use of freshwater. As described here, water and climatic conditions have played a direct role in the deterioration of Syria's economic conditions. There is a long history of conflicts over water in these regions because of the natural water scarcity, the early development of irrigated agriculture, and complex religious and ethnic diversity. In recent years, there has been an increase in incidences of water-related violence around the world at the subnational level attributable to the role that water plays in development disputes and economic activities. Because conflicts are rarely, if ever, attributable to single causes, conflict analysis and concomitant efforts at reducing the risks of conflict must consider a multitude of complex relationships and contributing factors. This paper assesses the complicated connections between water and conflict in Syria, looks more broadly at future climate-related risks for water systems, and offers some water management strategies for reducing those risks. [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]

India is the greatest groundwater consumer in the world, with estimated annual withdrawals exceeding 230 km³. More than 60% of irrigated agriculture, 85% of drinking water supplies, and 50% of urban and industrial water needs are dependent on sustainable groundwater management. Regardless, groundwater overextraction is a growing problem in many regions. Predictions of groundwater resource availability in India are problematic in part because of a limited number of monitoring sites and insufficient data quality and quantity. Regional groundwater assessments are further complicated because of sporadic and low-frequency data. To help overcome these issues and more accurately quantify groundwater resource availability, scientists have begun using satellite-derived remote sensing data. In this study, the authors used seasonal and annual hydrologic signals obtained by NASA Gravity Recovery and Climate Experiment (GRACE) satellites and simulated soil moisture variations from land data assimilation systems to show groundwater depletion trends in the northwest state of Gujarat (surface area of 196 030 km²), India. Results were evaluated using direct measurement data from 935 wells. Remote sensing generated results compared favorably with well data (e.g., r² 5 0.89 for Gandhinagar, a representative highly urbanized district in Gujarat: confidence interval (CI) 5 0.05 and P50.002). Results show that remote sensing is an effective tool to compliment and interpolate observed regional groundwater well data and improve groundwater storage estimations in Gujarat, India. Properly implemented, the method will supply reliable science-based information to enhance management of groundwater resources in India and other geographic locations. [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]

Modern agriculture alters natural biological and geophysical processes, with magnitudes proportional to its spatial extent. Cultivation is also the main cause of artificially enhanced reactive nitrogen (Nr) availability in natural ecosystems. Sustainable food production should thus minimize Nr use while maximizing human-destined caloric output per acre. The authors demonstrate that it is possible to design realistic, nutritionally sound plant-based diets that require a quarter to a half of the Nr and a quarter to a third of the land the mean American diet''s animal-based portion does. Broad application of these findings (e.g., by incorporating environmental considerations into official dietary recommendations) would reduce food production''s environmental impacts dramatically. [ABSTRACT FROM AUTHOR]

In September 1950, U.S. Senator Robert S. Kerr (D-Oklahoma) wrote to Indian leaders across the United States in order to ''make some determination with regard to whether or not we are going to have an early winter and whether or not we may expect a hard winter.'' Even though he had access to U.S. Weather Bureau predictions and other scientific data, Kerr and his administrative assistant, Ben Dwight, a member of the Choctaw Nation and its onetime Principal Chief, wrote that they ''would like to know what some of the Indians in the various sections of the nation think about our coming winter probabilities.'' Kerr and Dwight indicated they had a ''high regard for the old Indian ways of determining such things--because they are practical and have always been able to make some very accurate predictions.'' From 33 letters sent to tribes in 1950 (including 9 to tribes in Oklahoma) 3 responses were known to have been received; a follow-up letter-writing campaign in October 1951 was more fruitful, producing 8 known responses. This paper examines the tribal responses and explores the life and possible motivations of Senator Kerr, an influential man on the U.S. political stage during 1949--63, in seeking this information. This research is part of a broader field investigation that seeks to understand how Native Americans in Oklahoma conceptualize weather and climate, including traditional ways, and how their knowledge is helping to inform new efforts to farm sustainably and create food sovereignty. [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]

Agriculture has had a tremendous impact on soil nutrients around the world. In some regions, soil nutrients are depleted because of low initial soil fertility or excessive nutrient removals through intense land use relative to nutrient additions. In other regions, application of chemical fertilizers and manure has led to an accumulation of nutrients and subsequent water quality problems. Understanding the current level and spatial patterns of fertilizer and manure inputs would greatly improve the ability to identify areas that might be sensitive to aquatic eutrophication or to nutrient depletion. The authors calculated spatially explicit fertilizer inputs of nitrogen (N) and phosphorus (P) by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. They also calculated spatially explicit manure inputs of N and P by fusing global maps of animal density and international data on manure production and nutrient content. Significantly higher application rates were found for both fertilizers and manures in the Northern Hemisphere, with maxima centered on areas with intensive cropland and high densities of livestock. Furthermore, nutrient use is confined to a few major hot spots, with approximately 10% of the treated land receiving over 50% of the use of both fertilizers and manures. The authors’ new spatial disaggregation of the rich International Fertilizer Industry Association (IFA) fertilizer-use dataset will provide new and interesting avenues to explore the impact of anthropogenic activity on ecosystems at the global scale and may also have implications for policies designed to improve soil quality or reduce nutrient runoff. [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]

Climate, vegetation cover, and management create finescale heterogeneity in unirrigated agricultural regions, with important but not well-quantified consequences for spatial and temporal variations in surface CO2, water, and heat fluxes. Eddy covariance fluxes were measured in seven agricultural fields—comprising winter wheat, pasture, and sorghum—in the U.S. Southern Great Plains (SGP) during the 2001–03 growing seasons. Land cover was the dominant source of variation in surface fluxes, with 50%–100% differences between fields planted in winter–spring versus fields planted in summer. Interannual variation was driven mainly by precipitation, which varied more than twofold between years. Peak aboveground biomass and growing season net ecosystem exchange (NEE) of CO2 increased in rough proportion to precipitation. Based on a partitioning of gross fluxes with a regression model, ecosystem respiration increased linearly with gross primary production, but with an offset that increased near the time of seed production. Because the regression model was designed for well-watered periods, it successfully retrieved NEE and ecosystem parameters during the peak growing season and identified periods of moisture limitation during the summer. In summary, the effects of crop type, land management, and water limitation on carbon, water, and energy fluxes were large. Capturing the controlling factors in landscape-scale models will be necessary to estimate the ecological feedbacks to climate and other environmental impacts associated with changing human needs for agricultural production of food, fiber, and energy. [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]

Croplands cover large areas of the globe and contribute significantly to the global carbon cycle. However, like other ecosystems, limited information exists on spatially explicit, ground-based estimates of carbon fluxes. In this study, county-level cropland area and harvest information reported in the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) from 1972 to 2001 was utilized to calculate the temporal behavior of net primary production (NPP) for croplands across the United States. Production data for individual crops were converted to estimates of NPP using crop-specific factors. Because NASS does not include all crops of interest during all years, only a crop type in a county estimate was included if the entire time series was complete. Incomplete reporting occurred primarily with hay. Trends in crop area, NPP, and total production (area times NPP) exhibited significant spatial variation. The largest increases in production occurred in the Midwest, Great Plains, and Mississippi River Valley regions. Cropland area exhibited a range of trends from large percent increases in counties across the Great Plains and the West to decreases across the South. Generally, NPP increased in counties throughout the United States and for the country as a whole. It was estimated that total coterminous cropland production increased during 1972–2001 from 0.37 to 0.53 Pg C yr-1, a 40% increase over 1972 values. Since total cropland area changed little during the 30-yr period, production increases were driven primarily by gains in NPP. [ABSTRACT FROM AUTHOR]

Relationships between cropland change and presumed determinants were analyzed at scales ranging from 30 to 5100 m using logistic regression. The plot of the odds ratio across the spatial scales indicated that both biophysical and social variables were important in explaining cropland change. In the first period (1984–92), biophysical factors were the dominant factors, while market-related variables were more dominant between 1992 and 1999. Response to changing economic opportunities was the underlying cause of this trend. Policies that would make commercialization of agriculture viable are required in the Volta basin of Ghana. [ABSTRACT FROM AUTHOR]