在不同环境条件下优质蛋白玉米品种的物候期模型(英文)

Modeling of Phenology for Quality Protein Maize Cultivars under Different Environments

传统农业研究方法的结果通常具有地域性 ,且周期长 ,且投入大 ,用作物生长模型模拟技术 ,是解决这一问题的理想方法。为了用 CERES玉米生长模型预测栽培管理措施对不同品种生长发育的影响 ,在泰国北部清迈大学农学院的多熟种植中心 (北纬 18°4 7′,东经 99°5 7′,海拔 30 0 m )进行品种×播种期的双因素试验 ,参试种为 :Across 876 3(QPM) ,Poza Rica876 3(QPM)和 Suwan1,3个播种期分别是 1994年 12月 2 0日、 1995年 1月 5日、 1995年 1月 2 0日 ,然后采用模型中遗传参数计算器 (GENCAL )计算出这 3个品种的遗传参数 ;在云南省农业科学院试验站 (北纬2 5°,东经 10 9°,海拔 190 0 m)进行一个品种×施氮量的双因素试验 ,试验有 5个施氮水平 :分别是 10 0、 14 5、 185、 2 30和 2 70 kg/ hm2 ;3个参试种分别为 :Across 876 3、 Poza Rica 876 3和普通玉米墨白 1号。采用泰国获得的玉米品种的遗传参数对云南试验中两个品种的生长发育过程进行预测 ,结果表明 CERES玉米模型可以较准确地预测不同品种生长发育。

Traditional agricultural research result is recognized as site specific, slow, and expensive. An alternative to solve this problem is to use modeling approach. A nitrogen×variety experiment was conducted in the Research Station of Yunnan Academy of Agricultural Sciences (25°N Lat., 109°E Long., 1900 msl.). There were five nitrogen levels, i.e. 100, 145, 185, 230, and 270 kg ha -1 . Three maize varieties, two quality protein maize (QPM), i.e., Across 8763, Poza Rica 8763, and one normal maize Mobei 1, were used. In order to simulate the effects of management practice on growth and development of different maize cultivars by using the Crop Environment Resource Synthesis (CERES) Maize model in Northern Thailand, one varieties×planting dates experiment was conducted in the Research Station of the Multiple Cropping Center, Faculty of Agriculture, Chiang Mai University (18°47′N Lat., 99°57′E Long, 300 msl.). The experiment was conducted to generate data set for use in genetic coefficients determination. There were three Planting date levels: December 20, 1994, January 5, 1995, and Januany 20, 1995. Three maize cultivars were used, Across 8763(QPM), Poza Rica 8763 (QPM), and Suwan 1. The Genotype Coefficients Calculator (GENCAL) was used to determine a set of genetic coefficients for the three cultivars. The CERES Maize model satisfactorily simulated effects of planting dates on growth and development of different maize cultivars. This set of genetic coefficients was then used to simulate effects of management practices in Yunnan. The CERES Maize model demonstrated acceptable ability to simulate phenology events, e.g, silking, and physiological maturity dates.