Given a limit to crop yield improvement, Waggoner (1994) observed that a logistic model was more suitable Soybean [Glycine max (L.) Merr.] yields in the USA have risen for crop yield vs. time data 22.6 kg ha Ϫ1 yr Ϫ1 from 1924 to 1997, but in the last quarter century (1972-1997) have risen 40% faster, 31.4 kg ha Ϫ1 yr Ϫ1 . This upwardtrend in on-farm yield is fueled by rapid producer adoption of technologies emerging from agricultural research. Published estimates of the The logistic response curve is sigmoid because the expoannual gain in yield attributable to genetic improvement averaged nential change in yield (Y) over time (T) is modulated about 15 kg ha Ϫ1 yr Ϫ1 prior to the 1980s, but is now averaging about by the degree to which present yield differs from its 30 kg ha Ϫ1 yr Ϫ1 in both the public and proprietary sectors. Periodic limit (K). The inflection point (Tm) separates the posiadvances in agronomic technology, and a relentless rise in atmospheric tive and negative exponential phases of the sigmoid.CO 2 (currently 1.5 LL Ϫ1 yr Ϫ1 ), also contribute to the upward trend Wagoner (1994) used average U.S. corn (Zea mays L.) in on-farm yield. In Nebraska, irrigated yield averages 800 kg ha Ϫ1 yields from 1940 to 1992, and set K to 21 Mg ha Ϫ1 (the more than rainfed yield, and is improving at a 40% faster annual rate highest grain yield recorded in U.S. corn production), (35.1 vs. 24.9 kg ha Ϫ1 ). About 36% of the annual variation in the to show that U.S. corn yield was rising at a logistic rate irrigated-rainfed yield difference is attributable to annual variation in absolute rainfed yield. Inadequate water obviously limits absolute of 3.6% yr Ϫ1 . crop yield, but also seems to be an obstacle in terms of the rate of Attaining the theoretical yield maximum on each U.S. yield improvement. Several physiological traits changed during six farm each year for each crop would unquestionably redecades of cultivar releases in Ontario that led to a genetic gain in yield quire optimization of every yield-affecting biotic or abiof about 0.5% yr Ϫ1 . Changes in some traits were obvious (improved otic factor in every production environment. The degree lodging), but more subtle in others (greater N 2 -fixation, greater stressto which such multiple-factor optimization is possible, tolerance). In terms of photosynthate supplied to sinks across a wide or economically feasible, will determine what fraction range of environments, recent cultivars seem to be superior to obsolete of the crop's yield potential that can actually be realized ones. To sustain and enhance soybean yield improvement in the future, on the average U.S. farm on a year-to-year basis. In any technological innovation must be continually injected into the agriculevent, mitigation of the yield-limiting factors will require tural enterprise.

Accurate prediction of phenological development in maize (Zea mays L.) is fundamental to determining crop adaptation and yield potential. A number of thermal functions are used in crop models, but their relative precision in predicting maize development has not been quantified. The objectives of this study were (i) to evaluate the precision of eight thermal functions, (ii) to assess the effects of source data on the ability to differentiate among thermal functions, and (iii) to attribute the precision of thermal functions to their response across various temperature ranges. Data sets used in this study represent >1000 distinct maize hybrids, >50 geographic locations, and multiple planting dates and years. Thermal functions and calendar days were evaluated and grouped based on their temperature response and derivation as empirical linear, empirical nonlinear, and process-based functions. Precision in predicting phase durations from planting to anthesis or silking and from silking to physiological maturity was evaluated. Large data sets enabled increased differentiation of thermal functions, even when smaller data sets contained orthogonal, multi-location and -year data. At the highest level of differentiation, precision of thermal functions was in the order calendar days < empirical linear < process based < empirical nonlinear. Precision was associated with relatively low temperature sensitivity across the 10 to 26°C range. In contrast to other thermal functions, process-based functions were derived using supra-optimal temperatures, and consequently, they may better represent the developmental response of maize to supra-optimal temperatures. Supra-optimal temperatures could be more prevalent under future climate-change scenarios, but data sets in this study contained few data in that range.

Genetic improvement of short‐season soybean [Glycine max (L.) Merr.] cultivars has resulted in a 0.5% annual gain in yield. Although yield is the product of dry matter (DM) accumulation and partitioning, the relative contributions of these two components of yield to genetic improvement has not been documented. Furthermore, the mechanism by which higher DM accumulation or harvest index (HI) is accomplished in the newer cultivars is unclear. The objective of the current study was to characterize DM accumulation and partitioning in cultivars which differ in yield potential, and determine the role of these traits in yield improvement. Two older (low yield potential) and two newer (higher yield potential) soybean cultivars of similar maturity were grown in side‐by‐side trials in 1996 and 1997. Plant samples were taken during each growing season and separated into leaves, stems + petioles, roots, and seeds. Dry matter accumulation and leaf area indices were measured. Seed yield of the new cultivars was 30% greater than their older counterparts. Increased DM accumulation contributed 78% and increased HI contributed 22% towards the genetic gain in yield. Total plant dry weight increased to a maximum around R4/R5 and subsequently declined during the seed‐filling period (SFP) as pod development increased and leaf senescence began. This decline in dry weight during the SFP was greater for the old than for the new cultivars. The newer cultivars maintained leaf area further into the SFP than the old cultivars enabling continued dry matter accumulation. The results of this experiment indicate that genetic yield improvement in the short‐season soybean cultivars examined was mainly associated with longer leaf area duration and the subsequently greater DM accumulation.

Soybean rust (SBR; caused by Phakopsora pachyrhizi Syd. and P. Syd.) leads to premature leaf loss and yield reduction. The objectives of this study were to assess effects of SBR infection on soybean [Glycine max (L.) Merrill] yield and to identify causes for the yield reduction. Experiments were conducted in the 2005–2006 and 2006–2007 growing seasons at Londrina, Brazil. The five treatments were SBR infection beginning at either (i) the R2 or (ii) R5 growth stages; nondiseased defoliation treatments to mimic the leaf loss when SBR started at either (iii) the R2 or (iv) R5 growth stages; and (v) a disease‐free, nondefoliated control. The control and defoliation treatments were protected against SBR by fungicide applications. Disease severity, lesion area, and leaf area were monitored from R2 to R7. Biomass and seed yield were measured at maturity. Mean SBR‐induced yield reductions were 67% when infection started at R2 and 37% when infection started at R5. Leaf loss alone reduced yield significantly in only one year and only when defoliation treatments were begun at R2 (31% in 2005–2006). Soybean rust–induced yield loss was attributable to (i) premature leaf loss, (ii) reduction in canopy green leaf area due to SBR lesions, (iii) reduction in dry matter accumulation per unit absorbed radiation by the nonlesion green leaf area, and (iv) reduction in harvest index. The response of harvest index was attributable to reduced seed set and seed mass resulting likely from SBR‐induced reductions in rate of dry matter accumulation.

Predictions of crop yield under future climate change are predicated on historical yield trends 1-3 , hence it is important to identify the contributors to historical yield gains and their potential for continued increase. The large gains in maize yield in the US Corn Belt have been attributed to agricultural technologies 4 , ignoring the potential contribution of solar brightening (decadal-scale increases in incident solar radiation) reported for much of the globe since the mid-1980s. In this study, using a novel biophysical/empirical approach, we show that solar brightening contributed approximately 27% of the US Corn Belt yield trend from 1984 to 2013. Accumulated solar brightening during the post-flowering phase of development of maize increased during the past 3 decades, causing the yield increase that previously had been attributed to agricultural technology. Several factors are believed to cause solar brightening, but their relative importance and future outlook are unknown 5-9 , making prediction of continued solar brightening and its future contribution to yield gain uncertain. Consequently, results of this study call into question the implicit use of historical yield trends in predicting yields under future climate change scenarios.