Quantifying nitrous oxide (N2O) fluxes, a potent greenhouse gas, from soils is necessary to improve our knowledge of terrestrial N2O losses. Developing universal sampling frequencies for calculating annual N2O fluxes is difficult, as fluxes are renowned for their high temporal variability. We demonstrate daily sampling was largely required to achieve annual N2O fluxes within 10% of the ‘best’ estimate for 28 annual datasets collected from three continents—Australia, Europe and Asia. Decreasing the regularity of measurements either under- or overestimated annual N2O fluxes, with a maximum overestimation of 935%. Measurement frequency was lowered using a sampling strategy based on environmental factors known to affect temporal variability, but still required sampling more than once a week. Consequently, uncertainty in current global terrestrial N2O budgets associated with the upscaling of field-based datasets can be decreased significantly using adequate sampling frequencies.
HighlightExposed to terminal drought, the soil water content at which chickpea seed set ceased coincided with that at which stomatal conductance began to decrease and pod abscisic acid concentration increased.
T he main objective of a plant breeding program is selection of breeding lines which represent the greatest improvement relative to established varieties in terms of one or more traits. Therefore the effi ciency of a breeding program is measured through changes in yield performance over time (genetic gain). In statistical context it is expressed as potential genetic gain, measuring the average diff erence in performance among the lines entering the breeding program and those fi nally selected. The main concern in the selection process is the eff ect of V×E interactions, and the degree of uncertainty in identifi cation of varieties with broad or specifi c adaptation to the target environments. Effi cient analysis of multi-environment trials (METs) reduces the uncertainty and helps in understanding the V×E interactions.The linear mixed model approach to the analysis of plant breeding experiments, METs in particular, has become popular and widely used. These comprise: variance component models (Patterson et al.ABSTRACT Genetic gain is used as a long-term measure of the effi ciency of a breeding program. A spatial linear mixed model that includes a multiplicative mixed model (MMM) for the variety by environment (V×E) effect has been used for the analysis of 39 trials of 25 historical lupin varieties for the period of 1997 to 2006. The 25 varieties were produced by the Australian breeding effort from 1967 to 2007 and are a result from fi ve cycles of breeding. Genetic gain was assessed on the basis of the overall performance of the varieties across all environments based on the MMM results. The genetic gain from the fi rst early fl owering variety, Unicrop, to the highest yielding variety, Mandelup, represents a yield gain of 81% over 31 yr. The varieties' yield stability across the environments and their broad or specifi c adaptations are discussed.Abbreviations: AMMI, additive main eff ects and multiplicative interaction; AR1 × AR1, fi rst order autoregressive by fi rst order autoregressive; BLUE, best linear unbiased estimate; BLUP, best linear unbiased predictor; FA, factor analytic; GGE, genotype main eff ects and genotype × environment interaction; MET, multi-environment trials; MMM, multiplicative mixed model; PCA, principal components analysis; REML, residual maximum likelihood; REMLRT, residual maximum likelihood ratio test; V×E, variety by environment.
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