Integrated crop–livestock systems directly link crop and livestock production together to generate positive economic and environmental outcomes. Some methods used in integrated systems, like winter grazing on cropland, could negatively affect soil properties and crop productivity. We compared soil compaction, corn (Zea mays L.) yield, and soil nutrient pools between an integrated crop–livestock system and continuous corn system to address this issue. The study was conducted near Pana, IL, between 2002 and 2006. Soil compaction was evaluated indirectly by measuring soil penetration resistance (PR) and surface CO2 effluxes. Total soil C, N, and microbial biomass C, were measured from 2002 to 2005. Soil PR and CO2 effluxes showed inconsistent trends related to soil compaction and cattle presence. Corn yield from 2004 to 2006 was higher (P = 0.01) in the integrated system (11.6 Mg ha−1) compared with continuous corn (10.6 Mg ha−1). Total soil C concentration increased significantly from 2002 to 2005 within components of the integrated system but remained unchanged in continuous corn. Microbial biomass C was also higher in the integrated system but only in 2005. The study determined that integration of crops with livestock had generally positive effects on crop yield and soil organic matter despite the potential for livestock to compact soil during winter grazing.

Huangzaosi, Qi319, and Ye478 are foundation inbred lines widely used in maize breeding in China. To elucidate genetic base of yield components and kernel-related traits in these elite lines, two F(2:3) populations derived from crosses Qi319 × Huangzaosi (Q/H, 230 families) and Ye478 × Huangzaosi (Y/H, 235 families), as well as their parents were evaluated in six environments including Henan, Beijing, and Xinjiang in 2007 and 2008. Correlation and hypergeometric probability function analyses showed the dependence of yield components on kernel-related traits. Three mapping procedures were used to identify quantitative trait loci (QTL) for each population: (1) analysis for each of the six environments, (2) joint analysis for each of the three locations across 2 years, and (3) joint analysis across all environments. For the eight traits measured, 90, 89, and 58 QTL for Q/H, and 72, 76, and 51 QTL for Y/H were detected by the three QTL mapping procedures, respectively. About 70% of the QTL from Q/H and 90% of the QTL from Y/H did not show significant QTL × environment interactions in the joint analysis across all environments. Most of the QTL for kernel traits exhibited high stability across 2 years at the same location, even across different locations. Seven major QTL detected under at least four environments were identified on chromosomes 1, 4, 6, 7, 9, and 10 in the populations. Moreover, QTL on chr. 1, chr. 4, and chr. 9 were detected in both populations. These chromosomal regions could be targets for marker-assisted selection, fine mapping, and map-based cloning in maize.

BackgroundLow-molecular-weight glutenin subunits (LMW-GS) strongly influence the bread-making quality of bread wheat. These proteins are encoded by a multi-gene family located at the Glu-A3, Glu-B3 and Glu-D3 loci on the short arms of homoeologous group 1 chromosomes, and show high allelic variation. To characterize the genetic and protein compositions of LMW-GS alleles, we investigated 16 Aroona near-isogenic lines (NILs) using SDS-PAGE, 2D-PAGE and the LMW-GS gene marker system. Moreover, the composition of glutenin macro-polymers, dough properties and pan bread quality parameters were determined for functional analysis of LMW-GS alleles in the NILs.ResultsUsing the LMW-GS gene marker system, 14–20 LMW-GS genes were identified in individual NILs. At the Glu-A3 locus, two m-type and 2–4 i-type genes were identified and their allelic variants showed high polymorphisms in length and nucleotide sequences. The Glu-A3d allele possessed three active genes, the highest number among Glu-A3 alleles. At the Glu-B3 locus, 2–3 m-type and 1–3 s-type genes were identified from individual NILs. Based on the different compositions of s-type genes, Glu-B3 alleles were divided into two groups, one containing Glu-B3a, B3b, B3f and B3g, and the other comprising Glu-B3c, B3d, B3h and B3i. Eight conserved genes were identified among Glu-D3 alleles, except for Glu-D3f. The protein products of the unique active genes in each NIL were detected using protein electrophoresis. Among Glu-3 alleles, the Glu-A3e genotype without i-type LMW-GS performed worst in almost all quality properties. Glu-B3b, B3g and B3i showed better quality parameters than the other Glu-B3 alleles, whereas the Glu-B3c allele containing s-type genes with low expression levels had an inferior effect on bread-making quality. Due to the conserved genes at Glu-D3 locus, Glu-D3 alleles showed no significant differences in effects on all quality parameters.ConclusionsThis work provided new insights into the composition and function of 18 LMW-GS alleles in bread wheat. The variation of i-type genes mainly contributed to the high diversity of Glu-A3 alleles, and the differences among Glu-B3 alleles were mainly derived from the high polymorphism of s-type genes. Among LMW-GS alleles, Glu-A3e and Glu-B3c represented inferior alleles for bread-making quality, whereas Glu-A3d, Glu-B3b, Glu-B3g and Glu-B3i were correlated with superior bread-making quality. Glu-D3 alleles played minor roles in determining quality variation in bread wheat. Thus, LMW-GS alleles not only affect dough extensibility but greatly contribute to the dough resistance, glutenin macro-polymers and bread quality.