We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts; (2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end‐of‐season yields (relative root mean square error [RRMSE] of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50–64%. Model initial conditions and management information accounted for one‐fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2 = 0.88), root depth (R2 = 0.83), biomass production (R2 = 0.93), grain yield (R2 = 0.90), plant N uptake (R2 = 0.87), soil moisture (R2 = 0.42), soil temperature (R2 = 0.93), soil nitrate (R2 = 0.77), and water table depth (R2 = 0.41). We concluded that model set‐up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment.
Application of crop growth models (CGMs) in plant breeding is limited by the large number of candidate cultivars that breeders work with and the large number of CGM parameters that affect cultivar performance. The objectives of this study were to (1) calibrate 15 publicly available maize hybrids in Agricultural Production Systems sIMulator and quantify prediction accuracy in modeling physiological trait differences (yield, biomass, phenology, etc.) among genotypes; (2) better understand minimum phenotypic data requirements for CGM cultivar calibration to inform breeding efforts; and (3) quantify simulated genotype by environment interactions (G × E) across years for five traits. We calibrated hybrids with two years of multi‐trait, temporal field measurements. The
- NSF-PAR ID:
- 10408571
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Crop Science
- Volume:
- 63
- Issue:
- 2
- ISSN:
- 0011-183X
- Page Range / eLocation ID:
- p. 511-534
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
more » « less
Summary Hybrid breeding is the main strategy for improving productivity in many crops, especially in rice and maize. Genomic hybrid breeding is a technology that uses whole‐genome markers to predict future hybrids. Predicted superior hybrids are then field evaluated and released as new hybrid cultivars after their superior performances are confirmed. This will increase the opportunity of selecting true superior hybrids with minimum costs. Here, we used genomic best linear unbiased prediction to perform hybrid performance prediction using an existing rice population of 1495 hybrids. Replicated 10‐fold cross‐validations showed that the prediction abilities on ten agronomic traits ranged from 0.35 to 0.92. Using the 1495 rice hybrids as a training sample, we predicted six agronomic traits of 100 hybrids derived from half diallel crosses involving 21 parents that are different from the parents of the hybrids in the training sample. The prediction abilities were relatively high, varying from 0.54 (yield) to 0.92 (grain length). We concluded that the current population of 1495 hybrids can be used to predict hybrids from seemingly unrelated parents. Eventually, we used this training population to predict all potential hybrids of cytoplasm male sterile lines from 3000 rice varieties from the 3K Rice Genome Project. Using a breeding index combining 10 traits, we identified the top and bottom 200 predicted hybrids. SNP genotypes of the training population and parameters estimated from this training population are available for general uses and further validation in genomic hybrid prediction of all potential hybrids generated from all varieties of rice.
-
Dreisigacker, Susanne (Ed.)Abstract Over the past century of maize (Zea mays L.) breeding, grain yield progress has been the result of improvements in several other intrinsic physiological and morphological traits. In this study, we describe (i) the contribution of kernel weight (KW) to yield genetic gain across multiple agronomic settings and breeding programs, and (ii) the physiological bases for improvements in KW for US hybrids. A global-scale literature review concludes that rates of KW improvement in US hybrids were similar to those of other commercial breeding programs but extended over a longer period of time. There is room for a continued increase of kernel size in maize for most of the genetic materials analysed, but the trade-off between kernel number and KW poses a challenge for future yield progress. Through phenotypic characterization of Pioneer Hi-Bred ERA hybrids in the USA, we determine that improvements in KW have been predominantly related to an extended kernel-filling duration. Likewise, crop improvement has conferred on modern hybrids greater KW plasticity, expressed as a better ability to respond to changes in assimilate availability. Our analysis of past trends and current state of development helps to identify candidate targets for future improvements in maize.more » « less
-
more » « less
Abstract We review approaches to maize breeding for improved drought tolerance during flowering and grain filling in the central and western US corn belt and place our findings in the context of results from public breeding. Here we show that after two decades of dedicated breeding efforts, the rate of crop improvement under drought increased from 6.2 g m−2 year−1 to 7.5 g m−2 year−1, closing the genetic gain gap with respect to the 8.6 g m−2 year–1 observed under water-sufficient conditions. The improvement relative to the long-term genetic gain was possible by harnessing favourable alleles for physiological traits available in the reference population of genotypes. Experimentation in managed stress environments that maximized the genetic correlation with target environments was key for breeders to identify and select for these alleles. We also show that the embedding of physiological understanding within genomic selection methods via crop growth models can hasten genetic gain under drought. We estimate a prediction accuracy differential (Δr) above current prediction approaches of ~30% (Δr=0.11, r=0.38), which increases with increasing complexity of the trait environment system as estimated by Shannon information theory. We propose this framework to inform breeding strategies for drought stress across geographies and crops.
-
more » « less
Summary Predictive relationships between plant traits and environmental factors can be derived at global and regional scales, informing efforts to reorient ecological models around functional traits. However, in a changing climate, the environmental variables used as predictors in such relationships are far from stationary. This could yield errors in trait–environment model predictions if timescale is not accounted for.
Here, the timescale dependence of trait–environment relationships is investigated by regressing
in situ trait measurements of specific leaf area, leaf nitrogen content, and wood density on local climate characteristics summarized across several increasingly long timescales.We identify contrasting responses of leaf and wood traits to climate timescale. Leaf traits are best predicted by recent climate timescales, while wood density is a longer term memory trait. The use of sub‐optimal climate timescales reduces the accuracy of the resulting trait–environment relationships.
This study concludes that plant traits respond to climate conditions on the timescale of tissue lifespans rather than long‐term climate normals, even at large spatial scales where multiple ecological and physiological mechanisms drive trait change. Thus, determining trait–environment relationships with temporally relevant climate variables may be critical for predicting trait change in a nonstationary climate system.
