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The prediction of leaf appearance rate (LAR) is an important part of many crop simulation models. Most wheat simulations models assume that LAR is affected by temperature and photoperiod. This assumption ignores the fact that seed reserves contribute to a greater LAR of the first two leaves and that the LAR of subsequent leaves decreases as a result of an increase in the distance that each leaf primordium must extend before it appears. The objective of this study was to develop a generalized LAR chronology response function [f(C)] for wheat that takes into account seed reserves and the increasing distance from the meristem to the whorl for later appearing leaves. This chronology response function was then incorporated into an existing LAR model [Wang and Engel (WE) model; Wang and Engel, 1998, Agricultural Systems 58: 1-24]. This function varied from 0 to 1, being equal to 1 for the first two leaves due to seed reserves, and decreasing (taking the form of a power law) for subsequent leaves. Data from a growth chamber (two cultivars) and several field experiments (four cultivars, two years and eight sowing dates) at Lincoln, Nebraska, USA, were used as independent data to test three LAR models (Miglietta model, Miglietta, 1991, Climate Research 1: 145-150; WE model; and modified WE model). Predictions of the main stem Haun stage, both in the growth chamber and in the field, were greatly improved by incorporating f(C) into the Wang and Engel model. The root mean square error for the field data was 1.1, 0.7, and 0.3 leaves for the Miglietta model, the Wang and Engel model, and the modified Wang and Engel model, respectively.

Drought is an important factor limiting corn (Zea mays L.) yields in the Texas High Plains, and adoption of drought‐tolerant (DT) hybrids could be a management tool under water shortage. We conducted a 3‐yr field study to investigate yield, evapotranspiration (ET), and water use efficiency (WUE) in DT hybrids. One conventional (33D49) and 4 DT hybrids (P1151HR, P1324HR, P1498HR, and P1564HR) were grown at three water regimes (I100, I75, and I50, referring to 100, 75, and 50% ET requirement) and three planting densities (PD) (5.9, 7.4, and 8.4 plants m−2). Yield (13.56 Mg ha−1) and ET (719 mm) were the greatest at I100 but WUE (2.1 kg m−3) was the greatest at I75. Although DT hybrids did not always have greater yield and WUE than 33D49 at I100, hybrids P1151HR and P1564HR consistently had greater yield and WUE than 33D49 at I75 and I50. Compared to 33D49, P1151HR and P1564HR had 8.6 to 12.1% and 19.1% greater yield at I75 and I50, respectively. Correspondingly, WUE was 9.8 to 11.7% and 20.0% greater at I75 and I50, respectively. Greater PD resulted in greater yield and WUE at I100 and I75 but PD did not affect yield and WUE at I50. Yield and WUE in greater PD (8.4 plants m−2) were 6.3 to 8.3% greater than those in smaller PD (5.9 plants m−2). The results of this study demonstrated that proper selection of DT hybrids can increase corn yield and WUE under water‐limited conditions.

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Previous studies have shown the effect of a lead vehicle's speed, deceleration rate and headway distance on drivers' brake response times. However, how drivers perceive this information and use it to determine when to apply braking is still not quite clear. To better understand the underlying mechanisms, a driving simulator experiment was performed where each participant experienced nine deceleration scenarios. Previously reported effects of the lead vehicle's speed, deceleration rate and headway distance on brake response time were firstly verified in this paper, using a multilevel model. Then, as an alternative to measures of speed, deceleration rate and distance, two visual looming-based metrics (angular expansion rate θ˙ of the lead vehicle on the driver's retina, and inverse tau τ, the ratio between θ˙ and the optical size θ), considered to be more in line with typical human psycho-perceptual responses, were adopted to quantify situation urgency. These metrics were used in two previously proposed mechanistic models predicting brake onset: either when looming surpasses a threshold, or when the accumulated evidence (looming and other cues) reaches a threshold. Results showed that the looming threshold model did not capture the distribution of brake response time. However, regardless of looming metric, the accumulator models fitted the distribution of brake response times better than the pure threshold models. Accumulator models, including brake lights, provided a better model fit than looming-only versions. For all versions of the mechanistic models, models using τ as the measure of looming fitted better than those using θ˙, indicating that the visual cues drivers used during rear-end collision avoidance may be more close to τ.