Context. Computer-based crop simulation models are important tools for agricultural research and management. APSIM (Agricultural Production Systems sIMulator) is commonly used around the world but has not been widely validated in North America.

Aims. The objective of this work was to evaluate the reliability of APSIM for simulating wheat production in California, with the aim of providing guidance for future field research aimed at model calibration and validation.

Methods. Environmental and management data from state-wide wheat variety trials of common wheat (Triticum aestivum L.) were used to parameterise the APSIM-Wheat module (ver. 7.10 r4220). Simulated yield and protein data were compared with observed field trial results to test the reliability of APSIM simulations.

Key results. The most reliable simulation of grain yield had a root-mean-square error of 1040 kg/ha and normalised root-mean-square error of 16% relative to actual field data. Preliminary calibration of the model for Californian wheat varieties did not improve simulation accuracy or precision.

Conclusions. The accuracy or precision of the simulations was comparable to that of other tests of the APSIM-Wheat module in environments where it has not been previously calibrated but was considered too low to be reliable. The lack of reliability was due to the poor representation of local Californian wheat genotypes by existing APSIM cultivars, as well as possible lack of precision and accuracy of field data.

Implications. APSIM could be a valuable tool for wheat research and management in California; however, further research is needed to generate suitable field data for model calibration and validation.

Context. Micronutrient enrichment of pearl millet (Pennisetum glaucum (L.) R.Br.), an important food source in arid and semi-arid Asia and Africa, can be achieved by using stable genotypes with high iron and zinc content in breeding programs.

Aims. We aimed to identify stable expression of high grain iron and zinc content in pearl millet lines across environments.

Methods. In total, 29 genotypes comprising 25 recombinant inbred lines (RILs), two parental lines and two checks were grown and examined from 2014 to 2016 in diverse environments. Best performing genotypes were identified through genotype + genotype × environment interaction (GGE) biplot and additive main-effects and multiplicative interaction (AMMI) model analysis.

Key results. Analysis of variance showed highly significant (P < 0.01) variations. The GGE biplot accounted for 87.26% (principal component 1, PC1) and 9.64% (PC2) of variation for iron, and 87.04% (PC1) and 6.35% (PC2) for zinc. On the basis of Gollob’s F validation test, three interaction PCs were significant for both traits. After 1000 validations, the real root-mean-square predictive difference was computed for model diagnosis. The GGE biplot indicated two winning RILs (G4, G11) across environments, whereas AMMI model analysis determined 10 RILs for iron (G12, G23, G24, G7, G15, G13, G25, G11, G4, G22) for seven for zinc (G14, G15, G4, G7, G11, G4, G26) as best performers. The most stable RILs across environments were G12 for iron and G14 for zinc.

Conclusions. High iron and zinc lines with consistent performance across environments were identified and can be used in the development of biofortified hybrids.

Implications. The findings suggest that AMMI and GGE, as powerful and straightforward techniques, may be useful in selecting better performing genotypes.

Context. Path analysis (PA) is a widely used multivariate statistical technique. When performing PA, the effects of the parameters of the mathematical model relating to the experimental design are disregarded, working only with the average effects of the treatments.

Aims. We aimed to analyse the implications of statistical assumptions, and of removing mathematical model parameters, on the PA results in oat.

Methods. A field study was conducted in southern Brazil in five crop years. The experimental design employed was a two-factor 22 × 5 randomised complete block design, characterised by 22 cultivars and five fungicide applications, with three repetitions. Six explanatory variables were measured, panicle length, panicle dry mass, panicle spikelet number, panicle grain number, panicle grain dry mass, and harvest index, and the primary variable yield. Initially, normality and multicollinearity diagnoses were carried out and correlation coefficients were calculated. The PA was performed in three ways: traditional, with measures to address multicollinearity (ridge), and traditional with eliminating variables.

Key results and conclusions. The occurrence of multicollinearity resulted in obtaining path coefficients without biological application. Removing the model’s parameters modifies the path coefficients, with average changes of 10.5% and 13.3% in the direction, and 24.7% and 23.0% in the magnitude, of the direct and indirect effects, respectively.

Implications. This new approach makes it possible to remove the influences of treatments and experimental design from observations and, consequently, from path coefficients and their interpretations. Therefore, the researcher will reduce possible bias in the coefficient estimates, highlighting the real relationship between the variables, and making the results and interpretations more reliable.

Context. Maize (Zea mays L.) is one of the most economically important plants of the cereal family; it has value as human food, livestock feed, and as a component of industrial products.

Aims. This study focused on genetic diversity and existence of genetic divergence among promising maize inbred lines in Iran.

Methods. A commercial maize 600K SNP (single-nucleotide polymorphism) array was used to inspect genetic variability among 93 maize inbred lines.

Key results. The rate of transition mutation was twice as high as transversion mutation, and the density of detected SNPs was greater close to telomere regions of maize chromosomes. Considering the fluctuation of observed, expected and total heterozygosity and fixation index values across maize chromosomes, as well as polymorphism information content values, there is a high level of genetic variability among the studied maize panel. In addition, discriminant analysis of the principal components revealed four subpopulations in which the subpopulation ‘Line’ was distinct from other subpopulations and had no genomic overlap with them. Selection signature analysis revealed 177 regions harbouring 75 genes that differentiate among subgroups. Detected genes had a role in the mitogen-activated protein kinase signalling pathway, spliceosome, protein processing in endoplasmic reticulum, and hormone signal transduction.

Conclusions. We conclude that remarkable genetic diversity and differentiation exists among the studied maize subpopulations. The most differentiated SNPs among the subpopulations were associated with important biological processing genes and pathways.

Implications. The findings provide valuable insights for future maize breeding programs through exploitation of heterosis, as well as marker-assisted selection.

Context. Amylose is a type of resistant starch with numerous health benefits and industrial applications. Starch from maize (Zea mays L.) usually has an amylose content of ~25%.

Aims. The aim was to develop high-amylose maize genotypes suitable for human consumption and adapted to Indian conditions.

Methods. Marker-assisted backcross breeding was used to transfer the mutant ae1 allele from a high-amylose donor from the USA into the three parents (HKI 1344, HKI 1378, HKI 1348-6-2) of two high-yielding white maize hybrids (HM5 and HM12) grown in India.

Key results. In converted lines, amylose content was 40.40–58.10% of total kernel starch, compared with 22.25–26.39% in parents. The percentage increase in amylose content was 63.70–153.03%. There was a significant amount of background recovery in each backcross generation: 66.80–79% in BC₁F₁, 72.85–88.60% in BC₂F₁, and 84.45–93.70% in BC₂F₂. Overall, the total kernel starch content was reduced (by ~22%) in the ae1-introgressed families.

Conclusions. The converted lines developed in the study are enriched with kernel amylose while showing significant background recovery.

Implications. The high-amylose lines developed may be highly beneficial for diabetic patients and in the bioplastics industry, and should be suitable for growing under Indian conditions.