The environment randomly influences nitrogen (N) response, demand, and optimum N rates. Field experiments were conducted at Lake Carl Blackwell (LCB) and Efaw Agronomy Research Station (Efaw) from 2015 to 2018 in Oklahoma, USA. Fourteen site years of data were used from two different trials, namely Regional Corn (Regional) and Optimum N rate (Optimum N). Three algorithms developed by Oklahoma State University (OSU) to predict yield potential were tested on both trials. Furthermore, three new models for predicting potential yield using optical crop sensors and climatological data were developed for maize in rain-fed conditions. The models were trained/built using Regional and were then validated/tested on the Optimum N trial. Out of three models, one model was developed using all of the Regional trial (combined model), and the other two were prepared from each location LCB and Efaw model. Of the three current algorithms; one worked best at predicting final grain yield at LCB location only. The coefficient of determination R² = 0.15 and 0.16 between actual grain yield and predicted grain yield was observed for Regional and Optimum N rate trials, respectively. The results further indicated that the new models were better at predicting final grain yield except for Efaw model (R² = 0.04) when tested on optimum N trial. Grain yield prediction for the combined model had an R² = 0.31. The best yield prediction was obtained at LCB with an R² = 0.52. Including climatological data significantly improved the ability to predict final grain yield along with using mid-season sensor data.

环境会随机影响氮（N）的响应，需求和最佳N速率。2015年至2018年，在美国俄克拉荷马州的卡尔布莱克韦尔湖（LCB）和埃法农学研究站（Efaw）进行了田间试验。从两个不同的试验中使用了十四个站点年的数据，分别是区域玉米（区域玉米）和最佳氮素含量（最佳氮素）。两项试验均测试了俄克拉荷马州立大学（OSU）开发的三种预测产量潜力的算法。此外，针对雨养条件下的玉米，开发了三种使用光学作物传感器和气候数据预测潜在单产的新模型。使用区域训练/建立模型，然后在Optimum N试验中进行验证/测试。在三种模型中，一种模型是使用所有区域试验（组合模型）开发的，其他两个则分别从每个位置的LCB和Efaw模型准备。当前三种算法中；仅在预测LCB位置的最终谷物产量时，最有效的一种方法。测定系数R 在区域和最佳氮肥施用量试验中，分别观察到实际谷物产量与预测谷物产量之间的² = 0.15和0.16。结果进一步表明， 当在最佳氮试验中进行测试时，除了Efaw模型（R ² = 0.04）以外，新模型在预测最终谷物产量方面更好。组合模型的谷物产量预测 值为R ² = 0.31。在LCB处获得最佳的产量预测，R ²  = 0.52。包括气候数据以及使用季中传感器数据，显着提高了预测最终谷物收成的能力。