Current warming will reduce yields unless maize breeding and seed systems adapt immediately

The development of crop varieties that are better suited to new climatic conditions is vital for future food production1, 2. Increases in mean temperature accelerate crop development, resulting in shorter crop durations and reduced time to accumulate biomass and yield3, 4. The process of breeding, delivery and adoption (BDA) of new maize varieties can take up to 30 years. Here, we assess for the first time the implications of warming during the BDA process by using five bias-corrected global climate models and four representative concentration pathways with realistic scenarios of maize BDA times in Africa. The results show that the projected difference in temperature between the start and end of the maize BDA cycle results in shorter crop durations that are outside current variability. Both adaptation and mitigation can reduce duration loss. In particular, climate projections have the potential to provide target elevated temperatures for breeding. Whilst options for reducing BDA time are highly context dependent, common threads include improved recording and sharing of data across regions for the whole BDA cycle, streamlining of regulation, and capacity building. Finally, we show that the results have implications for maize across the tropics, where similar shortening of duration is projected

Genetic Characterization of a Core Set of a Tropical Maize Race Tuxpeño for Further Use in Maize Improvement

The tropical maize race Tuxpeño is a well-known race of Mexican dent germplasm which has greatly contributed to the development of tropical and subtropical maize gene pools. In order to investigate how it could be exploited in future maize improvement, a panel of maize germplasm accessions was assembled and characterized using genome-wide Single Nucleotide Polymorphism (SNP) markers. This panel included 321 core accessions of Tuxpeño race from the International Maize and Wheat Improvement Center (CIMMYT) germplasm bank collection, 94 CIMMYT maize lines (CMLs) and 54 U.S. Germplasm Enhancement of Maize (GEM) lines. The panel also included other diverse sources of reference germplasm: 14 U.S. maize landrace accessions, 4 temperate inbred lines from the U.S. and China, and 11 CIMMYT populations (a total of 498 entries with 795 plants). Clustering analyses (CA) based on Modified Rogers Distance (MRD) clearly partitioned all 498 entries into their corresponding groups. No sub clusters were observed within the Tuxpeño core set. Various breeding strategies for using the Tuxpeño core set, based on grouping of the studied germplasm and genetic distance among them, were discussed. In order to facilitate sampling diversity within the Tuxpeño core, a minicore subset of 64 Tuxpeño accessions (20% of its usual size) representing the diversity of the core set was developed, using an approach combining phenotypic and molecular data. Untapped diversity represents further use of the Tuxpeño landrace for maize improvement through the core and/or minicore subset available to the maize community

X-farm: Modelling Sustainable Farming Systems

The aim of this chapter is to illustrate the structure of X-farm, a model to manage farming systems under energetic, economical and ecological perspectives, using the dynamic simulation approach. The structure of X-farm is composed by some integrated modules representing the main centres of farming costs and production: soil management, crop production and processing and energy production and administration. The dynamic simulation is addressed to find the best combination of crop and livestock activities in the farm plan. The objective of energy production is afforded by using crops and reducing the energy use by optimising energy-saving techniques; the ecological objective is formulated by accounting the CO2 emissions; the economic objective is targeted to profit maximisation, constrained by the level of achievement of the energy and ecology targets. The dynamic simulation is expected to help in improving the farm management performance with the simultaneous achievement of the three objectives. Finally, combining the X-farm model with GIS techniques, the analysis will be expanded to the agro-district planning to support the regional strategy for agro-energy production