Drought resistance is an important breeding target for enhancing alfalfa productivity in arid and semi-arid regions. Identification of genes involved in drought tolerance will facilitate breeding for improving drought resistance and water use efficiency in alfalfa. Our objective was to use a diversity panel of alfalfa accessions comprised of 198 cultivars and landraces to identify genes involved in drought tolerance. The panel was selected from the USDA-ARS National Plant Germplasm System alfalfa collection and genotyped using genotyping by sequencing. A greenhouse procedure was used for phenotyping two important traits associated with drought tolerance: drought resistance index (DRI) and relative leaf water content (RWC). Marker-trait association identified nineteen and fifteen loci associated with DRI and RWC, respectively. Alignments of target sequences flanking to the resistance loci against the reference genome of M. truncatula revealed multiple chromosomal locations. Markers associated with DRI are located on all chromosomes while markers associated with RWC are located on chromosomes 1, 2, 3, 4, 5, 6 and 7. Co-localizations of significant markers between DRI and RWC were found on chromosomes 3, 5 and 7. Most loci associated with DRI in this work overlap with the reported QTLs associated with biomass under drought in alfalfa. Additional significant markers were targeted to several contigs with unknown chromosomal locations. BLAST search using their flanking sequences revealed homology to several annotated genes with functions in stress tolerance. With further validation, these markers may be used for marker-assisted breeding new alfalfa varieties with drought resistance and enhanced water use efficiency.

Crop simulation models have been used successfully to evaluate many systems and the impact of change on these systems, e.g. for climatic risk and the use of alternative management options, including the use of nitrogen fertilisers. However, for low input systems in tropical and subtropical regions where organic inputs rather than fertilisers are the predominant nutrient management option and other nutrients besides nitrogen (particular phosphorus) constrain crop growth, these models are not up to the task. This paper describes progress towards developing a capability to simulate response to phosphorus (P) within the APSIM (Agricultural Production Systems Simulator) framework. It reports the development of the P routines based on maize crops grown in semi-arid eastern Kenya, and validation in contrasting soils in western Kenya and South-western Colombia to demonstrate the robustness of the routines. The creation of this capability required: (1) a new module (APSIM SoilP) that simulates the dynamics of P in soil and is able to account for effectiveness of alternative fertiliser management (i.e. water-soluble versus rock phosphate sources, placement effects); (2) a link to the modules simulating the dynamics of carbon and nitrogen in soil organic matter, crop residues, etc., in order that the P present in such materials can be accounted for; and (3) modification to crop modules to represent the P uptake process, estimation of the P stress in the crop, and consequent restrictions to the plant growth processes of photosynthesis, leaf expansion, phenology and grain filling. Modelling results show that the P routines in APSIM can be specified to produce output that matches multiseason rotations of different crops, on a contrasting soil type to previous evaluations, with very few changes to the parameterization files. Model performance in predicting the growth of maize and bean crops grown in rotation on an Andisol with different sources and rates of P was good (75-87% of variance could be explained). This is the first published example of extending APSIM P routines to another crop (beans) from maize.

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