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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]

Grapevines are grown on a range of soils and in different climates. Depending on the desired final product and method of harvesting, the trellis system, and hence vineyard architecture, varies dramatically. Consequently, the crop coefficients used to estimate vineyard evapotranspiration from reference evapotranspiration are less universal than for other crops. Evapotranspiration measurements are often limited because of a lack of fetch and unavailability of vine parameters required as input. In this paper, a new procedure based on surface renewal (SR) analysis was derived to estimate sensible heat flux over the grapevines. A two-dimensional sonic anemometer operating close to the canopy can provide the inputs required to estimate the sensible heat flux and the work of expansion of the air parcels under constant pressure. Regardless of the stability conditions, the SR estimates were comparable to those determined using the eddy covariance (EC) method providing a root-mean-square error of 14 W m−2 for all data. Thus, the procedure is low cost and makes SR a simpler method than EC to estimate latent heat flux as the residual of the energy balance equation in moderately tall and heterogeneous crops. The experiment was carried out at a site influenced by regional advection of sensible heat flux. [ABSTRACT FROM AUTHOR]

This paper examines to what extent crops and their environment should be viewed as a coupled system. Crop impact assessments currently use climate model output offline to drive process-based crop models. However, in regions where local climate is sensitive to land surface conditions more consistent assessments may be produced with the crop model embedded within the land surface scheme of the climate model. Using a recently developed coupled crop–climate model, the sensitivity of local climate, in particular climate variability, to climatically forced variations in crop growth throughout the tropics is examined by comparing climates simulated with dynamic and prescribed seasonal growth of croplands. Interannual variations in land surface properties associated with variations in crop growth and development were found to have significant impacts on near-surface fluxes and climate; for example, growing season temperature variability was increased by up to 40% by the inclusion of dynamic crops. The impact was greatest in dry years where the response of crop growth to soil moisture deficits enhanced the associated warming via a reduction in evaporation. Parts of the Sahel, India, Brazil, and southern Africa were identified where local climate variability is sensitive to variations in crop growth, and where crop yield is sensitive to variations in surface temperature. Therefore, offline seasonal forecasting methodologies in these regions may underestimate crop yield variability. The inclusion of dynamic crops also altered the mean climate of the humid tropics, highlighting the importance of including dynamical vegetation within climate models. [ABSTRACT FROM AUTHOR]

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

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]

Forecasts play an important role in planting decisions for Andean peasant producers. Predictions of the upcoming cropping season determine when, where, and what farmers will plant. This research looks at the sources of forecast information used by farmers in three indigenous communities in the Bolivian and Peruvian Altiplano by examining networks used to access weather forecasts. The Altiplano is impacted by the F.NSO phenomenon and by frequent droughts and frosts so weather- and climate-related risks are the greatest threats to food security. While both Peru and Bolivia have forecasting systems that widely broadcast forecasts via mass media, farmers do not take them into account when they make production decisions. Instead, they rely on traditional forecasting techniques even though confidence in these indicators is declining. Even though traditional forecast indicators are understood by most producers, few make their own forecasts. Instead they depend upon a few local experts who appear to have little connection to scientific forecasts or agricultural extension agencies. The implications of these findings for improving forecast use are then discussed. [ABSTRACT FROM AUTHOR]

This work proposes a relatively simple methodology for creating ensembles of precipitation inputs that are consistent with the spatial and temporal scale necessary for regional crop modeling. A high-quality reference precipitation dataset [the European Land Data Assimilation System (ELDAS)] was used as a basis to define the uncertainty in an operational precipitation database [the Crop Growth Monitoring System (CGMS)]. The distributions of precipitation residuals (CGMS - ELDAS) were determined for classes of CGMS precipitation and transformed to a Gaussian distribution using normal score transformations. In cases of zero CGMS precipitation, the occurrence of rainfall was controlled by an indicator variable. The resulting normal-score-transformed precipitation residuals appeared to be approximately multivariate Gaussian and exhibited strong spatial correlation; however, temporal correlation was very weak. An ensemble of 100 precipitation realizations was created based on back-transformed spatially correlated Gaussian residuals and indicator realizations. Quantile–quantile plots of 100 realizations against the ELDAS reference data for selected sites revealed similar distributions (except for the 100th percentile, owing to some large residuals in the realizations). The semivariograms of realizations for sampled days showed considerable variability in the overall variance; the range of the spatial correlation was similar to that of the ELDAS reference dataset. The intermittency characteristics of wet and dry periods were reproduced well for most of the selected sites, but the method failed to reproduce the dry period statistics in semiarid areas (e.g., southern Spain). Finally, a case study demonstrates how rainfall ensembles can be used in operational crop modeling and crop yield forecasting. [ABSTRACT FROM AUTHOR]

A winter-season warming trend has been observed in eastern Asian countries during the last century. Significant effects on dormancy and the subsequent bud-burst of deciduous fruit trees are expected. However, phenological observations are scant in comparison with long-time climate records in the region. Chill-day accumulation, estimated from daily maximum and minimum temperature, is a reasonable proxy for dormancy depth of temperate-zone fruit trees. A selected chill-day model was parameterized for the Campbell Early grapevine, which is the major cultivar (grown virtually everywhere) in South Korea. To derive model parameters (threshold temperature for chilling and the chilling requirement for breaking dormancy), a controlled-environment experiment using field-sampled twigs of Campbell Early was conducted. The chill-day model to estimate bud-burst dates was adjusted by derived parameters and was applied using 1994–2004 daily temperature data obtained from the automated weather station in the vineyard at the National Horticultural Research Institute. The model gave consistently good performance in predicting bud-burst of Campbell Early (RMSE of 2.5 days). To simulate dormancy depth of Campbell Early at eight locations in South Korea for the last century, the model was applied using data obtained for each location from 1921 to 2004. Calculations showed that the chilling requirement for breaking endodormancy of Campbell Early can be satisfied by mid-January to late February in South Korea, and the date was delayed going either northward or southward from the Daegu–Jeonju line that crosses the middle of South Korea in the east–west direction. Maximum length of the cold tolerant period (the number of days between endodormancy release and the forced dormancy release) showed the same spatial pattern. Dormancy release for 1981–2004 advanced by as much as 15 days relative to that for 1921–50 at all locations except Jeju (located in the southernmost island with a subtropical climate), where an average 15-day delay was predicted. The cold-tolerant period diminished somewhat at six out of eight locations. As a result, bud-burst of Campbell Early in spring was advanced by 6–10 days at most locations, and interannual variation in bud-burst dates increased at all locations. The earlier bud-burst after the 1970s was due to 1) warming in winter that results in earlier dormancy release (Incheon, Mokpo, Gangneung, and Jeonju), 2) warming in early spring that enhances regrowth after breaking dormancy (Busan and Jeju), and 3) a combination of both (Seoul and Daegu). [ABSTRACT FROM AUTHOR]

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

A nonhomogeneous hidden Markov model (NHMM) is used to make stochastic simulations of March–August daily rainfall at 10 stations over the southeastern United States, 1923–98. Station-averaged observed daily rainfall amount is prescribed as an input to the NHMM, which is then used to disaggregate the rainfall in space. These rainfall simulations are then used as inputs to a Crop Estimation through Resource and Environment Synthesis (CERES) crop model for maize. Regionally averaged yields derived from the NHMM rainfall simulations are found to correlate very highly (r = 0.93) with those generated by the crop model using observed rainfall; stationwise correlations range between 0.44 and 0.74. Rainfall and crop simulations are then constructed under increasing degrees of temporal smoothing applied to the regional rainfall input to the NHMM, designed to exclude the submonthly weather details that would be unpredictable in seasonal climate forecasts. Regional yields are found to be remarkably insensitive to this temporal smoothing; even with 90-day low-pass-filtered inputs to the NHMM, resulting yields are still correlated at 0.85 with the baseline simulation, whereas stationwise correlations range between 0.18 and 0.68. From these findings, it is expected that regional maize yields over the southeastern United States will be largely insensitive to year-to-year details of subseasonal rainfall variability; they should be downscalable, in principle, using an NHMM from climate forecasts archived at daily resolution, with the important caveat that the latter need to be skillful enough at the 90-day time scale. As a by-product of the analysis, subseasonal-to-interdecadal summer rainfall variability over the southeastern United States is interpretable in terms of six discrete weather states indicative of a monsoonlike climate regime. Low-simulated-yield years are found to be associated with delayed summer rainfall onset. [ABSTRACT FROM AUTHOR]