PhenologyMMS: A program to simulate crop phenological responses to water stress

Abstract

Crop phenology is fundamental for understanding crop growth and development, and increasingly influences many agricultural management practices. Water deficits are one environmental factor that can influence crop phenology through shortening or lengthening the developmental phase, yet the phenological responses to water deficits have rarely been quantified. The objective of this paper is to provide an overview of a decision support technology software tool, PhenologyMMS Vl.2, developed to simulate the phenology of various crops for varying levels of soil water. The program is intended to be simple to use, requires minimal information for calibration, and can be incorporated into other crop simulation models. It consists of a Java interface connected to FORTRAN science modules to simulate phenological responses. The complete developmental sequence of the shoot apex correlated with phenological events, and the response to soil water availability for winter and spring wheat (Triticum aestivum L.), winter and spring barley (Hordeum vulgare L.), corn (Zea mays L.), sorghum (Sorghum bicolor L.), proso millet (Panicum milaceum L.), hay/foxtail millet [Setaria italica (L.) P. Beauv.]. and sunflower (Helianthus annus L.) were created based on experimental data and the literature. Model evaluation consisted of testing algorithms using "generic" default phenology parameters for wheat (i.e., no calibration for specific cultivars was used) for a variety of field experiments to predict developmental events. Results demonstrated that the program has general applicability for predicting crop phenology and can aid in crop management.

Early evidence for the use of wheat and barley as staple crops on the margins of the Tibetan Plateau

“…In high-altitude and highlatitude environments, having sufficient available units of heat is a key factor in determining the distribution of plant species. In agronomy, heat traditionally is measured as GDD, the cumulative heat requirements of a plant (54,55). In these environments, GDD is more useful than length of the growing season for determining the distribution of food crops.…”

Simulating the Probability of Grain Sorghum Maturity before the First Frost in Northeastern Colorado

“…The other set is for extremely dry, but not lethal, conditions indicative of dryland conditions with below average rainfall in semiarid production regions, and denoted as GS (for GDD stressed). For each location, all combinations of seven planting dates (1,8,15,22,29 May and 5, 12 June), three maturity classes (early, medium, and late), and four general categories of soil water in the seedbed at planting (optimum, medium, dry, and planted in dust) were simulated for each year of historical weather data using both GN and GS parameter values. The probability of reaching maturity at a location for each combination of GN/GS, planting date, maturity class, and soil water at planting scenario was calculated by determining for each year whether the simulated maturity date was before, or after, the first frost date for the year.…”

“…Simulations to estimate the probability of reaching maturity before the first frost date were run for each location changing the phenological parameters (dryland, GS; irrigated, GN), maturity class (early, medium, and late), planting date (1,8,15,22,29 May and 5, 12 June), and seedbed water conditions at planting (optimum, medium, dry, and planted in dust). The probability of reaching maturity at a location for each combination of GN/GS, planting date, maturity class, and soil water at planting scenario was calculated by determining whether the simulated maturity date for each year was before, or after, the first frost date for the year.…”

“…Simulations were run for each location changing the phenological parameters (dryland, GS; irrigated, GN), maturity class (early, medium, and late), planting date (1,8,15,22,29 May and 5, 12 June), and seedbed water conditions at planting (optimum, medium, dry, and planted in dust) to estimate mean maturity date. The mean simulated maturity date differed among locations, phenological parameters, planting dates, maturity class, and seedbed water conditions (full data not shown).…”

“…The objectives of PhenologyMMS are to (1) provide a relatively easy tool to producers, consultants, and scientists to predict crop developmental stages and provide information about crop phenology; and (2) develop seedling emergence and crop phenology science simulation components that could be inserted into other crop simulation models. Additional details on PhenologyMMS not covered below can be found in [19,[28][29][30], and PhenologyMMS can be downloaded at ARS Agricultural Software Download and Applications website [31].…”