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Accurate modelling of crop phenology is essential for evaluating how crops respond to future environmental and management changes. However, cultivar parameters are often estimated based on limited data and using a trial-and-error method, leading to uncertainties in simulated phenology and subsequent crop yield. In this paper we evaluated the ability of the APSIM-Canola model to simulate canola phenology and the impact of uncertainty in phenology modelling on simulated grain yield. We constrained the APSIM-Canola model to experimental data to derive the parameters controlling canola flowering and maturity dates using a Bayesian optimisation method, by minimising the RMSE between simulated and recorded pre- and post-flowering durations. The dataset covered observations for 10 cultivars, 35 site-years, with maximum of seven sowing dates each year from four canola growing regions in China. Our results demonstrated that multiple combinations of parameters could lead to the same simulation accuracy of canola phenology (equifinality) due to insufficient information/understanding to separate vernalisation and photoperiod sensitive phases. This could potentially lead to incorrect cultivar characterisation and wrong yield simulations. Our results further showed that the critical photoperiod below which canola phenological development slows down is likely to be 20 h instead of the 16.3 h currently used in the APSIM model. With this correction, the model was able to accurately simulate canola phenology across environments, and the impact of equifinality on simulated yield was small. Cultivar differences in terms of phenology could be accurately described by only three parameters in APSIM, i.e., vernalisation sensitivity, photoperiod sensitivity, and thermal time required for grain-filling period. [ABSTRACT FROM AUTHOR]

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

Abstract: This paper describes how spatial dependence can be incorporated into statistical models for crop yield along with the dangers of ignoring it. In particular, approaches that ignore this dependence suffer in their ability to capture (and predict) the underlying phenomena. By judiciously selecting biophysically based explanatory variables and using spatially-determined prior probability distributions, a Bayesian model for crop yield is created that not only allows for increased modelling flexibility but also for improved prediction over existing least-squares methods. The model is focused on providing efficient predictions which stabilize the effects of noisy data. Prior distributions are developed to accommodate the spatial non-stationarity arising from distinct between-region differences in agricultural policy and practice. In addition, a range of possible dimension–reduction schemes and basis expansions are examined in the pursuit of improved prediction. As a result, the model developed has improved prediction performance relative to existing models, and allows for straightforward interpretation of climatic effects on the model''s output. [Copyright &y& Elsevier]

Abstract: Climate change has already affected the phenology of several species. To be able to assess the impacts of climate change under various climate scenarios, we need superior models of the phenology of different species. Linear regression methods alone are of limited value for the analyses of natural indicators or phenological data because most time series of naturally occurring events in ecosystems do change in a nonlinear way. In this paper, we applied a Bayesian probability approach to investigate time series of the phenological phase of bud burst in Norway spruce (Picea abies (L.) Karst.) and mean monthly/weekly temperatures of corresponding climate stations in Germany. In these temperature and Norway spruce bud burst time series we detected years with the highest probability for discontinuities. We analysed rates of change and the relationship between temperature changes and bud burst of Norway spruce in the 51-year period 1953–2003. We used a Bayesian method for a coherence analysis between phenological onset dates and an effective temperature generated as a weighted average of monthly and weekly means from January to May. Weight coefficients were obtained from an optimization of the coherence factor by simulated annealing. In all investigated cases we found coherence factors that suggested a relationship between temperature and phenological time series. Norway spruce bud burst and mean temperature times series of April and May exhibited abrupt changes, particularly at the beginning of the 1980s. April and May temperature time series revealed an increased warming until 2003, and bud burst events advanced. Norway spruce bud burst, in particular, exhibited responses to temperatures of the previous (April) and current month (May). We suggest that besides commonly used sums of daily mean temperatures, forcing temperatures in phenology models should also include solutions where weighted effective temperatures in a sensitive time span are considered. [Copyright &y& Elsevier]

• Open-path infrared gas analyzer self-heating causes eddy-covariance flux errors • The common self-heating correction of Burba et al. (2008) has two errors • Large boundary layer thickness and surface weighting errors roughly cancel • A new formulation emphasizes the role of the spar in self-heating Across networks of cold weather sites, eddy covariance measurements of ecosystem fluxes commonly indicate carbon uptake throughout winter, a problem that has been linked to open-path infrared gas analyzer (IRGA) self-heating. Though there is no definitive consensus about this phenomenon, the de facto correction for the prevalent LI-7500 is the methodology of Burba et al. (2008) (B08), which predicts self-heating from IRGA surface temperatures and boundary layers. In this paper we examine the validity of this model compared to an updated correction by conducting field studies with the LI-7500 and an IRGA designed by the National Oceanic and Atmospheric Administration (NOAA). Both types of sensors were instrumented to measure sensible heat flux within the instrument path and temperatures of key instrument surfaces. We test hypotheses that (1) considerable IRGA self-heating occurs at a cold, windy, high elevation, mountainous field site, (2) unwarranted boundary layer adjustment terms defined in B08 reduce the bottom and top surface heat fluxes by an order of magnitude, and (3) the weighted sum of the surface heat fluxes should be proportional to their surface area contribution to the IRGA measurement volume. We detected more sensible heat flux within the LI7500 path than outside of it (statistical slopes between 0.95 to 1.3 and offsets between 0 and 35 W m−2) with the largest discrepancies occurring during the day and in winter. The NOAA IRGA has a similar structure to the LI-7500, but with flat surfaces that do not warrant the boundary layer adjustment terms in B08. Bayesian analyses indicated the bottom and top surface heat flux weightings from B08 are improbable and an order of magnitude too large. For the LI-7500, when the boundary layer adjustment terms were omitted the B08 weightings were also improbable; by including these terms and using the new weightings, self-heating estimates were reasonable only because the bottom and top sensible heat fluxes became negligible. We conclude that it is likely the B08 self-heating correction has two errors that roughly cancel out at our field site; this should not generally be expected. The new formulation emphasizes the role of the spar in self-heating, which should be taken into consideration for all cold-weather installations regardless of how the IRGA is mounted. [ABSTRACT FROM AUTHOR]