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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]

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]

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]

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]

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]

The southern Great Plains (SGP) is defined by hydrometeorological swings between dry and wet extremes. These swings exacerbate the climatological gradients of moisture (from east to west) and temperature (from south to north), which can impact the agricultural production of the region. Thus, it is key to understand extremes to sustainably maintain agricultural success in the region. This study investigates the wet extremes, or extreme precipitation events, that have become more prominent in the last two decades. Data from 108 U.S. Historical Climatology Network stations were analyzed for the 1950-2020 period to detect changes in the frequency and magnitude of extreme precipitation events. Results show that changes in the magnitude of extreme precipitation are isolated and scattered across the SGP, with only the winter season showing regional shifts in extreme precipitation magnitude. Changes in the frequency of extreme precipitation events were noted across the entire SGP, although the changes in frequency are more notable in the eastern SGP than in the western SGP. Analysis shows that the increased number of events detected is driven more, but not exclusively, by the increasing spatial extent of individual extreme precipitation events than by an increased number of events. Overall, these results depict the changing nature of extreme precipitation within the SGP and differences in extreme precipitation between the eastern and western SGP. [ABSTRACT FROM AUTHOR]

This study used corn insurance data as a proxy for agricultural loss to better inform producers and decision-makers about resilience and mitigation. Building on previous research examining crop losses based on weather and climate perils, updates to the peril climatology, identification of peril hotspots, and the quantification of annual trends using inflation-adjusted indemnities for corn were performed over the period 1989–2020. Normalization techniques in loss cost and acreage loss at county-level spatial resolution were also calculated. Indemnity data showed drought and excess moisture as the two costliest and most frequent perils for corn in the United States, although changes in the socioeconomic landscape and frequency of extreme weather events in the recent decade have led to significant increases in corn indemnities for drought, heat, excess moisture, flood, hail, excess wind, and cold wet weather. Normalized losses also displayed significant trends but were dependent on the cause of loss and amount of spatial aggregation. Perhaps most notable were the documented robust increases in corn losses associated with excess moisture, especially considering future projections for increased mid and end-of-century extreme precipitation. Subtle decreasing trends in drought, hail, freeze/frost, and flood loss cost over the study period indicates hedging taking place to protect against these perils, especially in corn acreage outside the Corn Belt in high-risk production zones. The use of crop insurance as a proxy for agricultural loss highlights the importance for quantifying spatiotemporal trends by informing targeted adaption to certain hazards and operational management decisions. Significance Statement: This study quantified the climatology and trends of weather and climate perils affecting corn in the United States. Robust increases in losses were noted with perils causing excess moisture, which is cause for further concern given projected increases in extreme rainfall under a warming climate. [ABSTRACT FROM AUTHOR]

Agricultural production in the U.S. Midwest is vulnerable to drought, and specialty crop producers are an underserved audience for monitoring information and decision-support tools. We investigate the decision-making needs of apple, grape, and cranberry growers using a participatory process to develop crop-specific decision calendars. The process highlights growers’ decisions and information needs during the winter dormant, growing, harvest, and postharvest seasons. Apple, grape, and cranberry growers tend to be concerned with the effects of short-term drought on current crop quality and quantity, while also considering the long-term drought effect on the health of perennial plants and future years’ production. We find gaps in drought information particularly for tactical and strategic decision-making. We describe the use of decision calendars to identify points of entry for existing drought monitoring resources and tools, and to highlight where additional research and tool development is needed. SIGNIFICANCE STATEMENT: While drought causes agricultural losses in the U.S. Midwest, more is known about the impacts and decision-support needs of commodity row crop growers in the region than those of perennial specialty crop growers. We find opportunities for climate information providers to tailor drought information delivery to perennial fruit growers according to the season, the parameters that are relevant to their decisions, and the timeframe of information needed for operational, tactical, and strategic decision-making. [ABSTRACT FROM AUTHOR]

Accurate prediction of winter wheat (Triticum aestivum L.) heading date is important for determining the potential incidence of diseases and abiotic stresses such as freeze or heat events. Wheat phenological modeling requires cultivar- and crop-zone-specific vernalization and photoperiod knowledge. Previous models applied in Kansas showed that the uncertainties of predicting heading date were large and could be improved. In this study, a modification to the Scientific Impact Assessment and Modeling Platform for Advanced Crop and Ecosystem Management (SIMPLACE) model was developed and implemented to improve the accuracy of winter wheat heading date estimation. The cultivar- and crop-zone-specific model parameters were calculated using a Markov chain Monte Carlo simulation. The modified models were calibrated by using the longest observation site to cover all cultivars planted in each crop zone. Model performance was then evaluated for seven winter wheat cultivars at eight experiment sites distributed across four crop zones in Kansas. Our modified model (MS) had a root-mean-square error (RMSE) between predicted and observed heading date of 4 days, which reflects an improved accuracy by 5–8 days on average compared to the Agricultural Production Systems Simulator (APSIM) or the original SIMPLACE models. There was a clear correlation between the uncertainty of the modeled heading date and the sowing date in previous models. Our modified model demonstrates that integrating nonlinear temperature response functions, temperature stress factors, and sowing date information improved prediction of the heading date in winter wheat across Kansas. [ABSTRACT FROM AUTHOR]

Pesticide applications during surface inversions can lead to spray drift causing severe damage up to several kilometers off-target. Current regulations in Australia prohibit spray application of certain agricultural chemicals when hazardous surface inversions exist. This severely limits spray opportunities. Surface inversions can be classified as weakly or strongly stable. In the weakly stable case, moderate to strong turbulent mixing is not supportive of long-distance concentrated drift. In the very stable case, weak turbulent mixing can support the transport of high concentrations of fine material over long distances. A review of the literature and our analyses indicate that if the turbulence, as measured by the standard deviation of the vertical wind speed σw, is greater than about 0.2 m s−1 then turbulence-driven mixing and dispersion is moderate to strong and conversely if σw is less than about 0.2 m s−1 then turbulence-driven mixing and dispersion is weaker (an order of magnitude). The concept of maximum downward heat flux as a natural division between the regimes is applied within Monin–Obukhov stability theory, and it is shown that the observed mean σw of 0.2 m s−1 aligns with the ridge line of maximum heat flux in stable conditions. The level of turbulence in the weakly stable regime is comparable to the turbulence typically observed in near-neutral conditions that are recommended under current guidelines as suitable for spraying and is therefore seen as an acceptable prerequisite to avoid nondispersive spraying conditions. [ABSTRACT FROM AUTHOR]

Agricultural drought has traditionally been monitored using indices that are based on above-ground measures of temperature and precipitation that have lengthy historical records. However, the period-of-record length for soil moisture networks is becoming sufficient enough to standardize and evaluate soil moisture anomalies and percentiles that are spatially and temporally independent of local soil type, topography, and climatology. To explore these standardized measures in the context of drought, the U.S. Climate Reference Network hourly standardized soil moisture anomalies and percentiles were evaluated against changes in the U.S. Drought Monitor (USDM) status, with a focus on onset, worsening, and improving drought conditions. The purpose of this study was to explore time scales (i.e., 1-6 weeks) and soil moisture at individual (i.e., 5, 10, 20, 50, and 100 cm) and aggregated layer (i.e., top and column) depths to determine those that were more closely align with evolving drought conditions. Results indicated that the upper-level depths (5, 10, and 20 cm, and top layer aggregate) and shorter averaging periods were more responsive to changes in USDM drought status. This was particularly evident during the initial and latter stages of drought when USDM status changes were thought to be more aligned with soil moisture conditions. This result indicates that standardized measures of soil moisture can be useful in drought monitoring and forecasting applications during these critical stages of drought formation and amelioration. [ABSTRACT FROM AUTHOR]

The Northeast Farming Region (NFR) of China is a critically important area of maize cultivation accounting for ~30% of national production. It is predominantly rain fed, meaning that adverse climate conditions such as drought can significantly affect productivity. Forewarning of such events, to improve contingency planning, could therefore be highly beneficial to the agricultural sector. For this, an improved estimate of drought exposure, and the associated large-scale circulation patterns, is of critical importance. We address these important questions by employing a large ensemble of initialized climate model simulations. These simulations provide 80 times as many summers as the equivalent observational dataset and highlight several limitations of the recent observational record. For example, the chance of a drought greater in area than any current observed event is approximately 5% per year, suggesting the risk of a major drought is significantly underestimated if based solely on recent events. The combination of a weakened East Asian jet stream and intensified subpolar jet are found to be associated with severe NFR drought through enhanced upper-level convergence and anomalous descent, reducing moisture and suppressing precipitation. We identify a strong 500-hPa geopotential height anomaly dipole pattern as a useful metric to identify this mechanism for relevance to seasonal predictability. This work can inform policy planning and decision-making through an improved understanding of the near-term climate exposure and form the basis of new climate services. [ABSTRACT FROM AUTHOR]

The impact of anthropogenic global warming on viticulture has been thoroughly studied. However, many of the climate projections are limited by the resolution of the models that cannot resolve mesoscale weather patterns, which heavily influence grape production. In this work, data were gathered from the National Center for Atmospheric Research wherein a high-spatiotemporal-resolution (4 km× 4 km, 1 h) Weather Research and Forecasting (WRF) Model was run from October 2000 to September 2013 over North America using observed data, and again using the atmospheric chemistry of CMIP5 ensemble mean of the RCP8.5 greenhouse gas emission scenario, creating a pseudo–global warming (PGW) model. Such models are capable of resolving the mesoscale influences that most climate models cannot. Contrasting the observed results to the PGW results allows users to compare "what happened" to "what could have happened." This analysis was applied to four cool-climate viticultural regions in the United States: two in Michigan, one in upstate New York, and one in Oregon. In the PGW run, hours exposed to extreme heat (>32°C) increase by orders of magnitude. Growing season degree-day (GDD) accumulations increase between 783 and 1057 base 10°C in comparing the models, while growing season average temperatures increase between 4.05° and 5.53°C. Precipitation patterns were also studied. The four regions would no longer classify as "cool climate" and would see growing seasons similar to some of the most productive warm-climate wine-producing regions. The authors consider the opportunities and challenges presented by the potential climate shift for cool-climate and warm-climate viticultural regions. [ABSTRACT FROM AUTHOR]

Statistically downscaled forecasts of October-December (OND) rainfall are evaluated over EastAfrica from two general circulation model (GCM) seasonal prediction systems. The method uses canonical correlation analysis to relate variability in predicted large-scale rainfall (characterizing, e.g., predicted ENSO and Indian Ocean dipole variability) to observed local variability over Kenya and Tanzania. Evaluation is performed for the period 1982-2011 and for the real-time forecast forOND2015, a season when a strong El Niño was active. The seasonal forecast systems used are the National Centers for Environmental Prediction Climate Forecast System, version 2 (CFSv2), and the Geophysical Fluid Dynamics Laboratory Forecast-Oriented Low Ocean Resolution (GFDL-FLOR) version of CM2.5. The Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) rainfall dataset--a blend of in situ station observations and satellite estimates--was used at 5 km 3 5 km resolution over Kenya and Tanzania as benchmark data for the downscaling. Results for the case-study forecast for OND 2015 show that downscaled output from both models adds realistic spatial detail relative to the coarser raw model output--albeit with some overestimation of rainfall that may have been derived from the downscaling procedure introducing a wet response to El Niño more typical of historical cases. Assessment of the downscaled forecasts over the 1982-2011 period shows positive long-term skill better than that documented in previous studies of unprocessed GCM forecasts for the region. Climate forecast downscaling is thus a key undertaking worldwide in the generation of more reliable products for sector specific application including agricultural planning and decision-making. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting (WRF) Model is evaluated as a regional climate model for the simulation of climate indices that are relevant to viticulture in Western Australia's wine regions at a 5-km resolution under current and future climate. WRF is driven with ERA-Interim reanalysis for the current climate and three global climate models (GCMs) for both current and future climate. The focus of the analysis is on a selection of climate indices that are commonly used in climate-viticulture research. Simulations of current climate are evaluated against an observational dataset to quantify model errors over the 1981-2010 period. Changes to the indices under future climate based on the SRES A2 emissions scenario are then assessed through an analysis of future (2030-59) minus present (1970-99) climate. Results show that when WRF is driven with ERA-Interim there is generally good agreement with observations for all of the indices although there is a noticeable negative bias for the simulation of precipitation. The results for the GCM-forced simulations were less consistent. Namely, while the GCM-forced simulations performed reasonably well for the temperature indices, all simulations performed inconsistently for the precipitation index. Climate projections showed significant warming for both of the temperature indices and indicated potential risks to Western Australia's wine growing regions under future climate, particularly in the north. There was disagreement between simulations with regard to the projections of the precipitation indices and hence greater uncertainty as to how these will be characterized under future climate. [ABSTRACT FROM AUTHOR]

In the summer of 2012, the central plains of the United States experienced one of its most severe droughts on record. This study examines the meteorological impacts of irrigation during this drought through observations and model simulations using the Community Land Model coupled to the Weather Research and Forecasting (WRF) Model. A simple parameterization of irrigation processes is added into the WRF Model. In addition to keeping soil moisture in irrigated areas at a minimum of 50% of soil moisture hold capacity, this irrigation scheme has the following new features: 1) accurate representation of the spatial distribution of irrigation area in the study domain by using a MODIS-based land surface classification with 250-m pixel size and 2) improved representation of the time series of leaf area index (LAI) values derived from crop modeling and satellite observations in both irrigated and nonirrigated areas. Several numerical sensitivity experiments are conducted. The WRF-simulated temperature field when including soil moisture and LAI modification within the model is shown to be most consistent with ground and satellite observations, all indicating a temperature decrease of 2-3 K in irrigated areas relative to the control run. Modification of LAI in irrigated and dryland areas led to smaller changes, with a 0.2-K temperature decrease in irrigated areas and up to a 0.5-K temperature increase in dryland areas. Furthermore, the increased soil moisture and modified LAI are shown to lead to statistically significant increases in surface divergence and surface pressure and to decreases in planetary boundary layer height over irrigated areas. [ABSTRACT FROM AUTHOR]

Trend analysis and estimation of monthly and annual precipitation, reference evapotranspiration ET o, and rainfall deficit are essential for water-resources management and cropping-system design. Rainfall, ET o, and water-deficit patterns and trends at Macon in eastern Mississippi for a 120-yr period (1894-2014) were analyzed for annual, seasonal, and monthly periods. The analysis showed historical average annual rainfall, ET o, and dryness index (DI) in the location to be 1307 mm, 1210 mm, and 0.97, respectively. Monthly rainfall and ET o ranged from 72 to 118 mm and from 94 to 146 mm, respectively, between May and October, resulting in a monthly rain deficit from 22 to 62 mm. Annual rainfall showed an increasing trend of 1.17 mm yr−1 while annual ET o exhibited a decreasing trend of −0.51 mm yr−1, resulting in an annual DI reduction of 0.001 per year. Seasonal trends were found for rainfall in autumn (1.06 mm yr−1), ET o in summer (−0.29 mm yr−1) and autumn (−0.18 mm yr−1), and DI in autumn (−0.006). An autoregressive, integrated, and moving-average (ARIMA) approach was used to model monthly and annual rainfall, ET o, and DI and to predict those values in the future. Low values of the root-mean-square error (RMSE) and mean absolute error (with both statistics being normalized to the mean of the observed values), low values of average percent bias, and low values of the ratio of the RMSE to the standard deviation of observed data, along with values of 1.0 for Nash-Sutcliffe modeling efficiency and the index of agreement, all suggest that the performance of the models is acceptable. The ARIMA models forecast 1319 mm of mean annual rainfall, 1203 mm of mean annual ET o, and 0.82 of mean annual DI from 2015 to 2024. The results obtained from this research can guide development of water-management practices and cropping systems in the area that rely on this weather station. The approaches used and the models fitted in this study can serve as a demonstration of how a time series trend can be analyzed and a model fitted at other locations. [ABSTRACT FROM AUTHOR]

Climate change will inevitably continue for the next few decades and will have an impact on climate-sensitive agricultural production, emphasizing the need to design effective adaptive strategies to cope with climate risk or take advantage of potential climatic benefits. In this study, the latest version of the Crop Environment Resource Synthesis-Rice (CERES-Rice) model was applied to assess the impacts of climate change and carbon dioxide (CO2) fertilization on rice yield, as well as the effectiveness of two popularly adopted adaptive measures in Hunan Province, the main rice-production location in China. The simulation spanned 30 years of baseline (1981-2010) as well as three future periods (2011-40, 2041-70, and 2071-99) with climate data generated by five general circulation models under the newly developed representative concentration pathway (RCP) 4.5 and 8.5 scenarios. The simulation results showed that, in comparison with average rice yield during the baseline (1981-2010), the ensemble-average yield of all cultivars during the 2020s, 2050s, and 2080s would decrease under both RCPs without CO2 fertilization effects. The ensemble-average yield reduction during the 2080s was alleviated under both RCPs if CO2 fertilization effects were accounted for. Adaptation simulations indicated that two adaptive measures (switching cultivars and changing planting dates) could mitigate the adverse effect to different extents. The intermodel variability under both RCPs was generally small. These findings may provide useful insight into the potential impacts of climate change on rice yield and effective adaptive measures to mitigate the adverse effect of future climate change in Hunan Province. [ABSTRACT FROM AUTHOR]

The terral de Vicuña is a warm and dry wind that flows down the Elqui Valley in north-central Chile typically at dawn and early morning. Given that most terral episodes occur in austral winter when chill accumulation by deciduous fruit trees proceeds, negative effects on agriculture may be expected. During 11 (2004-14) winters a meteorological characterization of terral winds and the assessment of their impact on chill accumulation, by the modified Utah Model and the Dynamic Model, were performed. Within this period, 67 terral days (TD) were identified as those in which nighttime to early morning wind direction and speed, air temperature, and relative humidity reached defined thresholds on an hourly basis (terral hours). Most frequent TD featured 6-9 consecutive terral hours; duration is considered here as a proxy for their intensity. Synoptic-scale meteorological analysis shows that 65% of moderate and strong terral events develop as a cold, migratory anticyclone drifts poleward of the study area, coinciding with the onset of a midtropospheric ridge over central Chile, bringing southwest winds on top of the Andes (~500-hPa level). The remaining 35% are either associated with 500-hPa easterlies (foehn like), with prefrontal conditions ahead of a trough driving northwest 500-hPa winds, or with transitional 500-hPa westerlies. Assessments of chill accumulation during TD show that, although present average and cold winter conditions do not represent a major TD hazard to local agriculture, lower chill accumulation associated with anomalously high nocturnal temperatures could be significantly more important during present and future warmer winters. [ABSTRACT FROM AUTHOR]

This study quantifies the impact of typhoons on rice production in the Philippines. To this end, satellite-derived reflectance data are used to detect the location of rice fields at 500-m resolution. Utilizing typhoon-track data within a wind field model and satellite-derived precipitation measures, fragility curves are then employed to proxy the damage of storms on rice production within each rice field. The results from a panel spatial regression model show that typhoons substantially reduced local provincial production in the quarter of the strike, having caused losses of up to 12.5 million tons since 2001. Using extreme value theory to predict future losses, the results suggest that a typhoon like the recent Haiyan, which is estimated to have caused losses of around 260 000 tons, has a return period of 13 years. This methodology can provide a relatively timely tool for rice damage assessments after tropical cyclones in the region. [ABSTRACT FROM AUTHOR]

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources-reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset-and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but undersensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves. [ABSTRACT FROM AUTHOR]

In this research, the linkage between late winter Bering Sea ice and maize/rice yields in northeastern China (NEC) is investigated. Results show that such ice influences NEC crop production and thus can be employed to predict harvest amounts. Further investigation reveals that positive anomalies of late winter sea ice cover can persist until spring and that spring sea ice can strengthen North Pacific Oscillation (NPO) positive-phase patterns, and vice versa. NPO significantly affects sea surface temperature (SST) over the North Pacific Ocean through sea-air interaction-in particular, in the Kuroshio region-that may persist until summer. In association with the positive SST anomalies, the polar vortex weakens and the western Pacific subtropical high strengthens, resulting in the convergence of southern and northern air masses over NEC. Moreover, both the southerly flow along the western flank of the western Pacific subtropical high and the easterly flow from the Japan Sea and the central Pacific region supply more water vapor transport; thus, an anomalous water vapor convergence center appears in NEC. With the anomalous updrafts, NEC exhibits positive precipitation anomalies. The greenhouse effect of water vapor results in an increase in minimum temperature, thereby leading to a decrease in diurnal temperature range (DTR). This increase in minimum temperature and decrease in DTR are primary factors favoring increases in rice and maize yields, respectively. [ABSTRACT FROM AUTHOR]

In sub-Saharan Africa, with its high rainfall variability and limited irrigation options, the crop planting date is a crucial tactical decision for farmers and therefore a major concern in agricultural decision making. To support decision making in rainfed agriculture, a new approach has been developed to optimize crop planting date. The General Large-Area Model for Annual Crops (GLAM) has been used for the first time to simulate maize yields in West Africa. It is used in combination with fuzzy logic rules to give more flexibility in crop planting date computation when compared with binary logic methods. A genetic algorithm is applied to calibrate the crop model and to optimize the planting dates at the end. The process for optimizing planting dates results in an ensemble of optimized planting rules. This principle of ensemble members leads to a time window of optimized planting dates for a single year and thereby potentially increases the willingness of farmers to adopt this approach. The optimized planting date (OPD) approach is compared with two well-established methods in sub-Saharan Africa. The results suggest earlier planting dates across Burkina Faso, ranging from 10 to 20 days for the northern and central part and less than 10 days for the southern part. With respect to the potential yields, the OPD approach indicates that an average increase in maize potential yield of around 20% could be obtained in water-limited regions in Burkina Faso. The implementation of the presented approach in agricultural decision support is expected to have the potential to improve agricultural risk management in these regions dominated by rainfed agriculture and characterized by high rainfall variability. [ABSTRACT FROM AUTHOR]

This study examines the frequency of daily rainfall totals greater than 2.54 cm (1 in.) averaged within a climate division (CD) associated with tropical systems that moved through the U.S. eastern Corn Belt region during the growing season. These occurrences are defined as 'events.' From 1913 to 2012, the tracks of 60 tropical systems moved over a study area that included 24 CDs-9 in Illinois, 9 in Indiana, and 6 in western Ohio. Of those 60 tropical systems, 37 were associated with events. Event risk varied through the growing season ranging from 3 events in June to 21 events in September. Decadal analysis showed an increase in the frequency of tropical systems and events during the last decade of the study (2003-12). Tropical systems were infrequent, and the timing of rainfall associated with the majority of events (i.e., September) was too late to impact corn and soybean development or yield in this region. Events had some impact on current and subsequent CD average soil moisture conditions; however, only 8 of the 37 events produced dramatic improvements in Palmer drought severity index (PDSI) values from categorical moderate to severe drought levels to near-normal conditions in the eastern Corn Belt. Those CDs that experienced a September or October event were associated with significantly higher PDSI values (+1.34) prior to the following summer than those that did not experience an event (+0.54). [ABSTRACT FROM AUTHOR]

This study compares different methods of predicting crop-related climate in the Ethiopian highlands for the period 1979-2009. A target index (ETH4) is developed as an average of four variables in the June-September season-rainfall, rainfall minus evaporation, estimated latent heat flux, and vegetation, following correlation with crop yields at Melkassa, Ethiopia (8.4°N, 39.3°E, 1550 m elevation). Predictors are drawn from gridded near-global fields of surface temperature, surface air pressure, and 200-hPa zonal wind in the preceding December-March season. Prediction algorithms are formulated by stepwise multivariate regression. The first set of predictors derive from objective principal component (PC) time scores with tropical loading patterns, and the second set is based on key areas determined from correlation with the target index. The second PC of upper zonal wind reveals a tropical-subtropical dipole that is correlated with ETH4 at two-season lead time (correlation coefficient r = −0.53). Point-to-field regression maps show high-latitude signals in surface temperature (positive in North America and negative in Eurasia) and air pressure (negative in the North Pacific Ocean and positive in the South Pacific). Upper zonal winds are most strongly related with ETH4 over the tropical Pacific and Africa at two-season lead time. The multivariate algorithm that is based on PC predictors has an adjusted r2 fit of 0.23, and the algorithm using key-area predictors achieves r2 = 0.37. In comparison, numerical model forecasts reach r2 = 0.33 for ECMWF simulations but are low for other models. The statistical results are specific to the ETH4 index, which is a climate proxy for crop yields in the Ethiopian highlands. [ABSTRACT FROM AUTHOR]

The impact of climate change on rice productivity in China remains highly uncertain because of uncertainties from climate change scenarios, parameterizations of biophysical processes, and extreme temperature stress in crop models. Here, the Model to Capture the Crop-Weather Relationship over a Large Area (MCWLA)-Rice crop model was developed by parameterizing the process-based general crop model MCWLA for rice crop. Bayesian probability inversion and a Markov chain Monte Carlo technique were then applied to MCWLA-Rice to analyze uncertainties in parameter estimations and to optimize parameters. Ensemble hindcasts showed that MCWLA-Rice could capture the interannual variability of the detrended historical yield series fairly well, especially over a large area. A superensemble-based probabilistic projection system (SuperEPPS) coupled to MCWLA-Rice was developed and applied to project the probabilistic changes of rice productivity and water use in eastern China under scenarios of future climate change. Results showed that across most cells in the study region, relative to 1961-90 levels, the rice yield would change on average by 7.5%-17.5% (from −10.4% to 3.0%), 0.0%-25.0% (from −26.7% to 2.1%), and from −10.0% to 25.0% (from −39.2% to −6.4%) during the 2020s, 2050s, and 2080s, respectively, in response to climate change, with (without) consideration of CO2 fertilization effects. The rice photosynthesis rate, biomass, and yield would increase as a result of increases in mean temperature, solar radiation, and CO2 concentration, although the rice development rate could accelerate particularly after the heading stage. Meanwhile, the risk of high-temperature stress on rice productivity would also increase notably with climate change. The effects of extreme temperature stress on rice productivity were explicitly parameterized and addressed in the study. [ABSTRACT FROM AUTHOR]

Predictive skills of retrospective seasonal climate forecasts (hindcasts) tailored to Philippine rice production data at national, regional, and provincial levels are investigated using precipitation hindcasts from one uncoupled general circulation model (GCM) and two coupled GCMs, as well as using antecedent observations of tropical Pacific sea surface temperatures, warm water volumes (WWV), and zonal winds (ZW). Contrasting cross-validated predictive skills are found between the 'dry' January-June and 'rainy' July-December crop-production seasons. For the dry season, both irrigated and rain-fed rice production are shown to depend strongly on rainfall in the previous October-December. Furthermore, rice-crop hindcasts based on the two coupled GCMs, or on the observed WWV and ZW, are each able to account for more than half of the total variance of the dry-season national detrended rice production with about a 6-month lead time prior to the beginning of the harvest season. At regional and provincial levels, predictive skills are generally low. The relationships are found to be more complex for rainy-season rice production. Area harvested correlates positively with rainfall during the preceding dry season, whereas the yield has positive and negative correlations with rainfall in June-September and in October-December of the harvested year, respectively. Tropical cyclone activity is also shown to be a contributing factor in the latter 3-month season. Hindcasts based on the WWV and ZW are able to account for almost half of the variance of the detrended rice production data in Luzon with a few months' lead time prior to the beginning of the rainy season. [ABSTRACT FROM AUTHOR]

Air temperature influences agricultural practices and production outcomes, making detailed quantifications of temperature changes necessary for potential positive and negative effects on agricultural management practices to be exploited or mitigated. Temperature trends of long-term data for five agricultural locations, ranging from the subhumid eastern to the semiarid western parts of Nebraska, were studied to determine local temperature changes and their potential effects on agricultural practices. The study quantified trends in annual and monthly average maximum and minimum air temperature ( Tmax and Tmin), daily temperature range (DTR), total growing degree-days, extreme temperatures, growing-season dates and lengths, and temperature distributions for five heavily agricultural areas of Nebraska: Alliance, Central City, Culbertson, Fremont, and Hastings. July and August were the months with the greatest decreases in Tmax for the central part of Nebraska-Culbertson, Hastings, and Central City. Alliance, Culbertson, and Fremont had year-round decreases in DTR. Central City and Hastings experienced growing-season decreases in DTR. Increases in growing-season length occurred at rates of 14.3, 16.7, and 11.9 days century−1 for Alliance, Central City, and Fremont, respectively. At Hastings, moderately earlier last spring frost (LS) at a rate of 6.6 days century−1 was offset by an earlier (2.7 days century−1) first fall frost (FF), resulting in only a 3.8 days century−1 longer growing season. There were only slight changes in LS and FF dates of around 2 days earlier and 1 day later per century, respectively, for Culbertson. [ABSTRACT FROM AUTHOR]

Global solar radiation Rg is an important input for crop models to simulate crop responses. Because the scarcity of long and continuous records of Rg is a serious limitation in many countries, Rg is estimated using models. For crop-model application, empirical Rg models that use commonly measured meteorological variables, such as temperature and precipitation, are generally preferred. Although a large number of models of this kind exist, few have been evaluated for conditions in the United States. This study evaluated the performances of 16 empirical, temperature- and/or precipitation-based Rg models for the southeastern United States. By taking into account spatial distribution and data availability, 30 locations in the region were selected and their daily weather data spanning eight years obtained. One-half of the data was used for calibrating the models, and the other half was used for evaluation. For each model, location-specific parameter values were estimated through regressions. Models were evaluated for each location using the root-mean-square error and the modeling efficiency as goodness-of-fit measures. Among the models that use temperature or precipitation as the input variable, the Mavromatis model showed the best performance. The piecewise linear regression-based Wu et al. model (WP) performed best not only among the models that use both temperature and precipitation but also among the 16 models evaluated, mainly because it has separate relationships for low and high radiation levels. The modeling efficiency of WP was from ~5% to more than 100% greater than those of the other models, depending on models and locations. [ABSTRACT FROM AUTHOR]

Theoretical predictions for dispersion of heavy particles above an area source are used to formulate a new framework to interpret measurements of spore concentration above an infected field. Experimental measurements of mean spore concentration above an infected wheat field are used to validate theoretical predictions. The framework is then used to estimate total spore flux from the infected field and deposition patterns downwind. Results suggest that for the present case, consisting of a very low open canopy and friction velocity between 0.2 and 0.5 m s−1, the properties of the spore plume above the source field are mostly determined by the source strength (i.e., spore release rate) and are approximately independent of turbulence properties. Turbulence conditions have a strong effect on the distance downwind from the source traveled by spores, however, and are therefore critical in the spread of the disease. In addition, effects of spore clumping on dispersal are explored, illustrating the strong effect of clumping on reducing spore dispersal distance. [ABSTRACT FROM AUTHOR]

In the context of climate change and its impact on sectors like agriculture and health, it is important to examine the changes in the characteristics of temperature extremes of different intensities and duration. In this study, an India Meteorological Department gridded temperature dataset is used to examine the changes in the frequency of occurrence of extreme temperatures over India and its seven homogeneous regions during the period 1969-2005. Results indicate a significant decrease in the frequency of occurrence of cold nights in the winter months in India and in its homogeneous regions in the north except in the western Himalaya. Southern regions show a drastic decrease in the frequency of cold nights relative to the period 1969-75. A significant increasing trend in the number of warm days in summer is noticed only in the interior peninsula. In the entire country and on the east coast and west coast, the maximum number of warm days in summer has been noticed only during the last decade, 1996-2005. Further, in the whole country the maximum number of intense warm days and nights in summer has been observed in the last decade. A significant increase in the number of cold days in winter is observed in the north-central and northeast regions. Changes in the frequency of warm and cold exceedances indicate maximum warming in the west coast as compared with all other regions. In sum, such spatial and temporal changes in the characteristics of all categories of temperature extremes broadly suggest warming trends in large parts of India. [ABSTRACT FROM AUTHOR]

Seasonal prediction of growing-season start of warm-season crops (GSSWC) is an important task for the agriculture sector to identify risks and opportunities in advance. On the basis of observational daily surface air temperature at 210 stations across Canada, this study found that the GSSWC in most Canadian areas begins during May-June and exhibits significant year-to-year variations that are dominated by two distinct leading empirical orthogonal function modes. The first mode accounts for 20.2%% of the total GSSWC variances and features a monosign pattern with the maximum anomalies in central-southern Canada. It indicates that warm-season crops in most Canadian areas usually experience a consistent early or late growing-season start and those in central-southern Canada have the most pronounced interannual variations. The second mode explains 10.8%% of the total variances and bears a zonal seesaw pattern in general, accompanied by prominent anomalies covering the west coast of Canada and anomalies with a reverse sign prevailing in central-eastern Canada. Therefore, a strong second-mode year represents an early GSSWC in western Canada and a late GSSWC in the rest of the regions. The predictability sources for the two distinct leading modes show considerable differences. The first mode is closely linked with the North American continental-scale snow cover anomalies and sea surface temperature anomalies (SSTAs) in the North Pacific and Indian Oceans in the prior April. For the second mode, the preceding April snow cover anomalies over western North America and SSTAs in the equatorial-eastern Pacific, North Pacific, and equatorial Indian Oceans provide precursory conditions. These snow cover anomalies and SSTAs sustain from April through May-June, influence the large-scale atmospheric circulation anomalies during the crops' growing-start season, and contribute to the occurrence of the two leading modes of the GSSWC across Canada. On the basis of these predictors of snow cover anomalies and SSTAs in the prior April, an empirical model is established for predicting the two principal components (PCs) of the GSSWC across Canada. Hindcasting is performed for the 1972-2007 period with a leaving-nine-out cross-validation strategy and shows a statistically significant prediction skill. The correlation coefficient between the observation and the hindcast is 0.54 for PC1 and 0.48 for PC2, both exceeding the 95%% confidence level. Because all of these predictors can be readily monitored in real time, this empirical model provides a new prediction tool for agrometeorological events across Canada. [ABSTRACT FROM AUTHOR]

To understand agro-climatic suitability for spring soybean growth in north China, an integrated crop-response-function method was developed. This method includes crop-response functions for temperature, precipitation, and sunshine and is assessed by a weighting method based on the coefficient of determination. The results show that the most suitable area (S1) for spring soybean growth occupied approximately 21.35%% of the total area of north China. Among three types of spring soybeans of early maturity, middle maturity, and late maturity, middle maturity was the most suitable variety to grow in the study area, covering nearly 1.133 ×× 106 km2 or about 99.75%% of the total area of S1. As a result of this study, the authors suggest that breeders pay more attention to middle-maturity cultivars in north China. The findings from this study may provide useful information for policy makers issuing guidelines for agricultural production. [ABSTRACT FROM AUTHOR]

A set of agroclimatic indices representing Canadian climatic conditions for field crop production are analyzed for long-term trends during 1895–2007. The indices are categorized for three crop types: cool season, warm season, and overwintering. Results indicate a significant lengthening of the growing season due to a significantly earlier start and a significantly later end of the growing season. Significant positive trends are also observed for effective growing degree-days and crop heat units at most locations across the country. The occurrence of extremely low temperatures has become less frequent during the nongrowing season, implying a more favorable climate for overwinter survival. In addition, the total numbers of cool days, frost days, and killing-frost days within a growing season have a decreasing trend. This means that crops may also be less vulnerable to cold stress and injury during the growing season. Extreme daily precipitation amounts and 10-day precipitation totals during the growing season have been increasing. Significant trends associated with increased availability of water during the growing season are identified by the standardized precipitation index and seasonal water deficits. The benefit of the increased precipitation may have been offset by an upward trend in evaporative demand; however, this would depend on the amount of growth and productivity resulting from increased actual evapotranspiration. [ABSTRACT FROM AUTHOR]

Daily rainfall during the April–October growing season in a major cropping region of southeastern Australia has been related to particular types of synoptic weather systems over a period of 33 yr. The analysis reveals that cutoff lows were responsible for at least 50% of all growing-season rainfall and accounted for 80% of daily rainfall events exceeding 25 mm per station. The proportion of rainfall contributed by cutoff lows varies throughout the growing season. It is highest in austral autumn and spring (55% and 57%, respectively) and falls to a minimum in July (42%). By way of contrast, the total contribution of all types of frontal systems to growing-season rainfall is about 32%, although the monthly value reaches a maximum of 41% in July when mean cutoff rainfall reaches a minimum. Rainfall associated with fronts is strongly concentrated in the lower range of daily falls (less than 10 mm per station). Frontal rainfall is found to be more consistent from year to year than is cutoff rainfall. The number of cutoff lows per season is highly variable, and there is a significant correlation between the number of cutoff days and atmospheric blocking in the region south of Australia in each month of the growing season. The mean amount of rainfall per cutoff day is also variable and has declined by approximately 0.8 mm over the analysis period. An understanding of the mechanisms controlling year-to-year variability of cutoff rainfall is therefore an important step in improving seasonal forecasts for agriculture in southeastern Australia. [ABSTRACT FROM AUTHOR]