Using Stage‐Dependent Temperature Parameters to Improve Phenological Model Prediction Accuracy in Rice Models

Abstract

Crop phenology models that use constant temperature parameters across developmental stages may be less accurate and have temperature‐dependent systematic prediction error (bias). Using the DD10 model, we evaluated default and optimized (DD_Opt) temperature parameters using data from seven California rice (Oryza sativa L.) cultivars grown in six locations over 3 yr (2012–2014). Furthermore, we evaluated the effect of using stage‐dependent temperature parameters on model performance using two‐ and three‐stage optimization approaches. Optimized temperature parameters, or DD_Opt (RMSE: 2.3–5.4 d), performed better than DD10 (RMSE: 2.9–7.3 d). A temperature sensitivity analysis indicated that the time from planting to panicle initiation was most sensitive to temperature (every 1°C increase decreased days to panicle initiation by 1.8 d) while time from heading to R7 (marked by the appearance of one yellow hull on the main stem panicle) was not affected by temperature. Optimized temperature parameters varied between stages, with base temperature decreasing and optimum temperature increasing with plant development. Compared to the DD_Opt, two‐stage optimization (planting–heading and heading–R7) reduced the RMSE by 0.8 d and the systematic error by 0.6 d °C−1. Three‐stage optimization (planting–panicle initiation, panicle initiation–heading, and heading–R7) further reduced RMSE by 1.1 d and systematic error by 1.4 d °C−1 for preheading. These results demonstrate the importance of using stage‐dependent parameters to improve accuracy of phenological models, which may be important when models are used to study the crop response to climate change, field management options, ecosystem productivity, breeding, and yield gap analysis.

Point stresses during reproductive stage rather than warming seasonal temperature determine yield in temperate rice

“…For each plot, the date when 50% of the plants reached the heading stage (referred to as “50% heading” hereafter), plant height, percent lodging, and moisture content at harvest were recorded. As the observed date of panicle initiation was not observed, it was estimated using a thermal time model calibrated for CA rice cultivars by Sharifi, Hijmans, Hill, and Linquist (), who found little difference among cultivars in thermal time from planting to panicle initiation. For the purposes of defining crop stages, the vegetative stage was assumed to last from the date of planting to panicle initiation, the sensitive boot stage from 7 days after panicle initiation until 7 days prior to 50% heading, the flowering stage from 7 days prior to 50% heading to 7 days after 50% heading, and grain‐fill from the date of 50% heading to 30 days following.…”

Sustainable and technological strategies for basic cereal crops in the face of climate change: A literature review

“…Sanad et al (2016) evaluated spring wheat in the drought, concluding greater phenological resistance during emergence and sensitivity in anthesis, under controlled conditions. Sharifi et al (2016) evaluated the effect of the temperature depending on the stage and found out that the time from the rice planting to the initiation of the panicle is more sensitive. Ihsan et al (2016) analyzed the phenological development of wheat crops, their growth, yield and water use efficiency in arid soil in Western Arabia, observing the relationship between growth and yield at the beginning of the reproduction phase and also observed greater sensitivity to drought and to the dates of planting during anthesis.…”

Impact of High-Cadence Earth Observation in Maize Crop Phenology Classification

“…Understanding changes in crop phenology and their response to climatic conditions is critical for production-beneficial management practices [2]. While the literature describes several initiatives to address crop phenology via earth observations (remote sensing) [3][4][5][6], or mathematical models supported on weather information [7,8], the metrics reported from those studies are not easily translated into effective agronomic management strategies. For instance, Land Surface Phenology (LSP) determines the change in green vegetation condition by identifying changes in the vegetation seasonal pattern using remote sensing technologies [3], but these changes do not directly reflect the true phenological stage of a distinctive crop [9].…”