Rice (Oryza sativa L.) Growth Modeling Based on Growth Degree Day (GDD) and Artificial Intelligence Algorithms

Rice (Oryza sativa L.) growth prediction is key for precise rice production. However, the traditional linear rice growth forecasting model is ineffective under rapidly changing climate conditions. Here we show that growth rate (Gr) can be well-predicted by artificial intelligence (AI)-based artificial neural networks (ANN) and gene-expression programming (GEP), with accumulated air temperatures based on growth degree day (GDD). In total, 10,246 Gr from 95 cultivations were obtained with three cultivars, TK9, TNG71, and KH147, in Central and Southern Taiwan. The model performance was evaluated by the Pearson correlation coefficient (r), root mean square error (RMSE), and relative RMSE (r-RMSE) in the whole growth period (lifecycle), as well as the average and specific key stages (transplanting, 50% initial tillering, panicle initiation, 50% heading, and physiological maturity). The results in lifecycle Gr modeling showed that ANN and GEP models had comparable r (0.9893), but the GEP model had the lowest RMSE (3.83 days) and r-RMSE (7.24%). In stage average and specific key stages, each model has its own best-fit growth period. Overall, GEP model is recommended for rice growth prediction considering the model performance, applicability, and routine farming work. This study may lead to smart rice production due to the enhanced capacity to predict rice growth in the field.

Weather based prediction of Chickpea Helicoverpa armigera population in Bundelkhand agroclimatic zone of Madhya Pradesh

An attempt was made to find out the impact of rainfall, temperature and growing degree day (GDD) on the larval incidence and peak population of Helicoverpa armigera on chickpea and its growth in the Bundelkhand Agroclimatic zone of Madhya Pradesh. Besides, an attempt was also made to examine the association with weather variables of rising and falling phase of the larval population of Helicoverpa armigera. It was found that there was not any significant impact of monthly (September and October) rainfall on the larval population but the monthly rainfall of January and February significantly influenced the incidence of the pod borer and GDD plays a vital role in increasing and decreasing of its peak population. Minimum temperature and rainfall play a crucial role for larval incidence and its population growth. Growing degree day from 1st January to 15th February were presented in relation to the number of peak larval population in chickpea. The correlation of weather factors with larval population was also presented and different weather parameters were screened for its prediction and management. A multiple regression equation was also developed. It was found that if the cumulative growing degree day from 1st January to 15th February 350 degree day and weekly minimum temperature ranged from 6 to 12 C along with number of rainfall events 5 days, then number of larval population of H. armigera in chickpea is high and vice-versa. This study will be very useful not only for forecasting the peak larval population of H. armigera in chickpea but in formulating effective pest management strategies too.

A comparison of hydrological models with different level of complexity in Alpine regions in the context of climate change

This study compares the ability of two degree-day models (Poli-Hydro and a degree-day implementation of Alpine3D) and one full energy-balance melt model (Alpine3D) to predict the discharge on two partly glacierized Alpine catchments of different size and intensity of exploitation, under present conditions and climate change as projected at the end of the century. For present climate, the magnitude of snow melt predicted by Poli-Hydro is sensibly lower than the one predicted by the other melt schemes, and the melting season is delayed by one month. This difference can be explained by the combined effect of the reduced complexity of the melting scheme and the reduced computational temporal resolution. The degree-day implementation of Alpine3D reproduces a melt season closer to the one obtained with its full solver; in fact, the onset of the degree-day mode still depends upon the full energy-balance solver, thus not bringing any particular benefit in terms of inputs and computational load, unlike with Poli-Hydro. Under climate change conditions, Alpine3D is more sensitive than Poli-Hydro, reproducing discharge curves and volumes shifted by one month earlier as a consequence of the earlier onset of snow melt. Despite their benefits, the coarser temporal computational resolution and the fixed monthly degree-days of simpler melt models like Poli-Hydro make them controversial to use for climate change applications with respect to energy-balance ones. Nevertheless, under strong river regulation, the influence of calibration might even overshadow the benefits of a full energy-balance scheme.

Greenland Ice Sheet Surface Runoff Projections to 2200 Using Degree-Day Methods

Surface runoff from the Greenland ice sheet (GrIS) has dominated recent ice mass loss and is having significant impacts on sea-level rise under global warming. Here, we used two modified degree-day (DD) methods to estimate the runoff of the GrIS during 1950–2200 under the extensions of historical, RCP 4.5, and RCP 8.5 scenarios. Near-surface air temperature and snowfall were obtained from five Earth System Models. We applied new degree-day factors to best match the results of the surface energy and mass balance model, SEMIC, over the whole GrIS in a 21st century simulation. The relative misfits between tuned DD methods and SEMIC during 2050–2089 were 3% (RCP4.5) and 12% (RCP8.5), much smaller than the 30% difference between untuned DD methods and SEMIC. Equilibrium line altitude evolution, runoff-elevation feedback, and ice mask evolution were considered in the future simulations to 2200. The ensemble mean cumulative runoff increasing over the GrIS was equivalent to sea-level rises of 6 ± 2 cm (RCP4.5) and 9 ± 3 cm (RCP8.5) by 2100 relative to the period 1950–2005, and 13 ± 4 cm (RCP4.5) and 40 ± 5 cm (RCP8.5) by 2200. Runoff-elevation feedback produced runoff increases of 5 ± 2% (RCP4.5) and 6 ± 2% (RCP8.5) by 2100, and 12 ± 4% (RCP4.5) and 15 ± 5% (RCP8.5) by 2200. Two sensitivity experiments showed that increases of 150% or 200%, relative to the annual mean amount of snowfall in 2080–2100, in the post-2100 period would lead to 10% or 20% more runoff under RCP4.5 and 5% or 10% under RCP8.5 because faster ice margin retreat and ice sheet loss under RCP8.5 dominate snowfall increases and ice elevation feedbacks.

Assessing the ability of the growing degree-day metric to explain variation in size-at-age and duration-to-moult of lobsters and crabs

Environmental temperature directly controls the rate at which ectotherms grow and develop. The growing degree-day metric (GDD, °C∙d) scales time by temperature to create a thermal time scale relevant to ectothermic organisms. Here we assess the ability of GDD to model size-at-age and duration-to-moult in 15 datasets (9 size-at-age, 6 duration-to-moult) comprising 7 species of lobsters and crabs. We applied generalized linear models to assess the ability of GDD vs. “calendar” time to explain growth and development observations within and across trials. Best-fit models included GDD with fewer parameters in 6 of 9 size-at-age and 5 of 6 duration-to-moult datasets, and a better fit to the data in 6 of 9 size-at-age datasets. Our results show that the individual growth of lobster and crab species can be modelled using thermal time models. Such models can be used to identify thermal tolerance limits, predict growth under varying temperature conditions and disentangle temperature effects from those of other factors affecting individual growth and development, resulting in improved growth models for field conditions including fisheries management.

How did the characteristics of the growing season change during the past 100 years at a steep river basin in Japan?

The effects of climate change on plant phenological events such as flowering, leaf flush, and leaf fall may be greater in steep river basins than at the horizontal scale of countries and continents. This possibility is due to the effect of temperature on plant phenology and the difference between vertical and horizontal gradients in temperature sensitivities. We calculated the dates of the start (SGS) and end of the growing season (EGS) in a steep river basin located in a mountainous region of central Japan over a century timescale by using a degree-day phenological model based on long-term, continuous, in situ observations. We assessed the generality and representativeness of the modelled SGS and EGS dates by using phenological events, live camera images taken at multiple points in the basin, and satellite observations made at a fine spatial resolution. The sensitivity of the modelled SGS and EGS dates to elevation changed from 3.29 days (100 m)−1 (−5.48 days °C−1) and −2.89 days (100 m)−1 (4.81 days °C−1), respectively, in 1900 to 2.85 days (100 m)−1 (−4.75 days °C−1) and −2.84 day (100 m)−1 (4.73 day °C−1) in 2019. The long-term trend of the sensitivity of the modelled SGS date to elevation was −0.0037 day year−1 per 100 m, but the analogous trend in the case of the modelled EGS date was not significant. Despite the need for further studies to improve the generality and representativeness of the model, the development of degree-day phenology models in multiple, steep river basins will deepen our ecological understanding of the sensitivity of plant phenology to climate change.