Root plasticity improves maize nitrogen use when nitrogen is limiting - an analysis using 3D plant modelling.

Plant phenotypic plasticity plays an important role in nitrogen (N) acquisition and use under nitrogen-limited conditions. However, this role has never been quantified as a function of N availability, leaving it unclear whether plastic responses should be considered as potential targets for selection. A combined modelling and experimentation approach was adopted to quantify the role of plasticity on N uptake and plant yield. Based on a greenhouse experiment we considered plasticity in two maize traits: root-to-leaf biomass allocation ratio and emergence rate of axial roots. In a simulation experiment we individually enabled or disabled both plastic responses for maize stands grown across six N levels. Both plastic responses contributed to maintaining a higher N uptake and plant productivity as N-availability declined, compared to stands in which plastic responses were disabled. We conclude that plastic responses quantified in this study may be a potential target trait in breeding programs for greater N uptake across N levels while it may only be important for the internal use of N under N-limited conditions in maize. Given the complexity of breeding for plastic responses, an a priori model analysis is useful to identify which plastic traits to target for enhanced plant performance.

Genomic basis determining root system architecture in maize.

KEY MESSAGE: A total of 389 and 344 QTLs were identified by GWAS and QTL mapping explaining accumulatively 32.2-65.0% and 23.7-63.4% of phenotypic variation for 14 shoot-borne root traits using more than 1300 individuals across multiple field trails. Efficient nutrient and water acquisition from soils depends on the root system architecture (RSA). However, the genetic determinants underlying RSA in maize remain largely unexplored. In this study, we conducted a comprehensive genetic analysis for 14 shoot-borne root traits using 513 inbred lines and 800 individuals from four recombinant inbred line (RIL) populations at the mature stage across multiple field trails. Our analysis revealed substantial phenotypic variation for these 14 root traits, with a total of 389 and 344 QTLs identified through genome-wide association analysis (GWAS) and linkage analysis, respectively. These QTLs collectively explained 32.2-65.0% and 23.7-63.4% of the trait variation within each population. Several a priori candidate genes involved in auxin and cytokinin signaling pathways, such as IAA26, ARF2, LBD37 and CKX3, were found to co-localize with these loci. In addition, a total of 69 transcription factors (TFs) from 27 TF families (MYB, NAC, bZIP, bHLH and WRKY) were found for shoot-borne root traits. A total of 19 genes including PIN3, LBD15, IAA32, IAA38 and ARR12 and 19 GWAS signals were overlapped with selective sweeps. Further, significant additive effects were found for root traits, and pyramiding the favorable alleles could enhance maize root development. These findings could contribute to understand the genetic basis of root development and evolution, and provided an important genetic resource for the genetic improvement of root traits in maize.

Carbon footprint of farming practices in farmland ecosystems on the North and Northeast China plains.

Fast development of farming practices in China is projected to result in additional carbon emissions and thus affect farmland ecosystems' environmental performance. Based on 454 farm surveys on the North and Northeast China Plain, the carbon footprint (CF) of two farmland ecosystems (irrigated system for wheat and maize on the North China Plain and rainfed system for maize on the Northeast Plain) were assessed and emission reduction pathways explored by quantifying greenhouse gas emissions of agricultural inputs and farm practices during the entire crop growing seasons with an agricultural footprint model. The results demonstrated that the GHG emissions from wheat and maize rotation in the irrigated system were 7.63 t CO2 eq ha-1 and 3.17 t CO2 eq ha-1 for single season maize in the rainfed system. While energy consumption accounted for 12.5%-21.3% of the carbon footprint in both systems, the group assessment found that the largest difference in GHG emissions between the high and low emission groups came from mechanical energy consumption. Approximately 50.6% and 39.2% of the mechanical carbon footprint of wheat and maize, respectively, were caused by irrigation practices in the irrigated system. Regarding the rainfed system, where 46.6% of mechanical carbon emissions were generated by maize tillage operations. In addition, scenario analysis indicated that the mechanical carbon footprint could be reduced to 56 kg CO2 eq t-1 for NCP-wheat and 26 kg CO2 eq t-1 for NCP-maize, respectively, by optimizing yields and irrigation practices in irrigated systems and that the mechanical carbon footprint of NEP-maize could be reduced to 25 kg CO2 eq t-1 by optimizing yields and tillage practices in rainfed systems. Therefore, improvement in mechanization in irrigation and tillage practices can contribute to reduce GHG emissions in China. Water-saving irrigation technology is recommended in irrigated area and conservation tillage is recommended in rainfed agricultural area to reduce carbon footprints.