首页> 外文会议>第三届智能化农业信息技术国际学术会议(The 3rd International Symposium on Intelligent Information Technology in Agriculture)(ISIITA) >PREDICTING THE POTENTIAL PRODUCTION OF VARIOUS RICE GENOTYPES USING DSSAT CERES-RICE MODEL AND ESTIMATING THE YIELD GAP UNDER VARIOUS AGRO-ECOLOGICAL REGIONS OF TAMIL NADU
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PREDICTING THE POTENTIAL PRODUCTION OF VARIOUS RICE GENOTYPES USING DSSAT CERES-RICE MODEL AND ESTIMATING THE YIELD GAP UNDER VARIOUS AGRO-ECOLOGICAL REGIONS OF TAMIL NADU

机译:利用DSSAT水稻模型预测水稻不同基因型的潜在产量,以及泰米尔纳德邦不同农业生态区的产量差距估算。

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The potential yield of various rice genotypes (i.e) IR 64, White Ponni, ADT38, ADT 39, CO 47 , ADT 45 and Basmati 370 under different agro-ecological regions of Tamil Nadu was predicted using CERES- Rice model. The historical weather data of ten locations covering various agro ecological regions of Tamil Nadu were collected and the monthly average were calculated using the weatherman module of DSSAT (v 3.5). Based on the monthly average values, the weather data for all the locations were simulated. The potential production of the genotypes as influenced by weather parameters was predicted by setting the model inputs to unlimited water and N supply and the date of planting as per the crop production guide. The predicted potential yield was compared with actual yields obtained n the respective regions. The simulated results showed that among the cultivars, IR 64 recorded the maximum phonological growth and yield components like number of grains; grain weight; panicle number and grain yield irrespective of the locations. The predicted potential production of all the cultivars showed that there exists a huge gap between the actual yield and predicted yield. This confirms that there exists further scope for increasing the productivity.
机译:使用CERES-Rice模型预测了泰米尔纳德邦不同农业生态区域下各种水稻基因型(即IR 64,White Ponni,ADT38,ADT 39,CO 47,ADT 45和Basmati 370)的潜在产量。收集了涵盖泰米尔纳德邦各个农业生态区的十个地点的历史气象数据,并使用DSSAT的气象员模块计算了月平均值(v 3.5)。根据月平均值,模拟了所有位置的天气数据。根据作物生产指南,通过将模型输入设置为无限的水和氮供应以及播种日期来预测受天气参数影响的基因型的潜在产量。将预测的潜在产量与在各个区域获得的实际产量进行比较。模拟结果表明,在品种中,IR 64记录了最大的语音增长和产量构成,如籽粒数;谷物重量穗数和籽粒产量,与位置无关。预测的所有品种潜在产量表明,实际产量与预期产量之间存在巨大差距。这证实存在进一步提高生产率的余地。

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