Simulating bimodal tall fescue growth with a degree‐day‐based process‐oriented plant model

Plant growth simulation models have a temperature response function driving development, with a base temperature and an optimum temperature defined. Such models function well when plant development rate shows a continuous change throughout the growing season. This approach becomes more complex as it is extended to cool‐season perennial grasses with a dormant period and bimodal growth curves. The objective of this study was to develop such a bimodal growth model for tall fescue (Schedonorus arundinaceus (Schreb.) Dumort) in the Midwest USA based on multiyear measurement trials. Functions for bimodal growth were incorporated into the ALMANAC model and applied to tall fescue using published tall fescue yields for a variety of sites and soils. Fields of cultivars “Kentucky 31” and “BarOptima Plus E34” were divided into paddocks and sampled weekly for dry‐matter accumulation. These biomass estimates were used to derive weekly growth values by differences between sequential weekly samplings. The measured values were compared to a single tall fescue simulation each year on one soil. Using these results, the ALMANAC model was modified and tested against mean reported tall fescue yields for 11 sites, with one to three soils per site. When we introduced midsummer dormancy into ALMANAC, we assumed dormancy began on the longest day of the year and lasted until the photoperiod was 0.68 hr shorter than the longest. ALMANAC simulated previously reported tall fescue yields well across the range of sites. Thus, ALMANAC shows great promise to simulate bimodal growth in this common cool‐season grass.     

基于ALMANAC的山西省冬小麦模拟

为了准确的监测山西省冬小麦动态长势和预测产量,本研究使用ALMANAC作物生长模型对山西省洪洞县高、中、低产田的冬小麦产量进行了模拟。收集了模型需要的作物属性、土壤、气象及田间管理措施等众多参数并根据实际情况对参数进行了调整,结果表明：冬小麦模拟产量的相对误差(RE)为-7.8%~5.7%,叶面积指数的RE为-12.5%~13.6%,水地最大叶面积指数最大;与背景态相比生育期提前,叶面积指数水地变化不大,旱地低较多,温度主要是对生育期的影响,而水分则对叶面积指数产生较大影响。冬小麦的产量和叶面积指数的动态变化能够被ALMANAC模型较好地模拟;而且模型能够模拟不同水分条件下冬小麦的叶面积指数及气候变化对冬小麦影响。     

ALMANAC 模型对黄土高原玉米、谷子和糜子产量的模拟

用ALMANAC作物模型对黄土高原安塞县大南沟小流域2000～2004年玉米、谷子和糜子的产量进行模拟.结果显示,ALMANAC模型能够很好地模拟该地区的粮食产量,模拟值与实测值回归方程的决定系数大于0.72,回归方程接近1∶1线.玉米、谷子和糜子模拟产量与实测产量之间的平均相对误差分别为-2.29%、-2.32%和8.34%,模拟产量与实测产量之间的标准化均方根差分别为15.7%、11.5%和15.8%.梯田、阴坡和阳坡之间的模拟误差有所差异,玉米梯田产量的模拟值比实测值偏高,阴坡和阳坡产量的模拟值偏低;谷子梯田和阴坡产量的模拟值偏低,阳坡产量的模拟值偏高;糜子梯田和阳坡产量的模拟值偏高.梯田模拟值与实测值之间的一致性较好,其次为阴坡,再次为阳坡.     

Potential impact of future climate change on sugarcane under dryland conditions in Mexico

Assessments of impacts of future climate change on widely grown sugarcane varieties can guide decision‐making and help ensure the economic stability of numerous rural households. This study assessed the potential impact of future climatic change on sugarcane grown under dryland conditions in Mexico and identified key climate factors influencing yield. The Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model was used to simulate sugarcane growth and yield under current and future climate conditions. Management, soil and climate data from farm sites in Jalisco (Pacific Mexico) and San Luis Potosi (Northeastern Mexico) were used to simulate baseline yields. Baseline climate was developed with 30‐year historical data from weather stations close to the sites. Future climate for three decadal periods (2021–2050) was constructed by adding forecasted climate values from downscaled outputs of global circulation models to baseline values. Climate change impacts were assessed by comparing baseline yields with those in future decades under the A2 scenario. Results indicate positive impacts of future climate change on sugarcane yields in the two regions, with increases of 1%–13% (0.6–8.0 Mg/ha). As seen in the multiple correlation analysis, evapotranspiration explains 77% of the future sugarcane yield in the Pacific Region, while evapotranspiration and number of water and temperature stress days account for 97% of the future yield in the Northeastern Region. The midsummer drought (canicula) in the Pacific Region is expected to be more intense and will reduce above‐ground biomass by 5%–13% (0.5–1.7 Mg/ha) in July–August. Harvest may be advanced by 1–2 months in the two regions to achieve increases in yield and avoid early flowering that could cause sucrose loss of 0.49 Mg ha^?1 month^?1. Integrating the simulation of pest and diseases under climate change in crop modelling may help fine‐tune yield forecasting.