Abstract:

To address the pronounced climatic heterogeneity within Guizhou Province and a scarcity of premium forage， this study aimed to construct a scientific cultivar evaluation framework utilizing multi-environment trials and multi-criteria decision-making （MCDM） models. The objective was to identify cultivars optimally suited for the distinct ecological zones of the region. The investigation involved field trials of 21 silage maize （Zea mays） cultivars， conducted across three representative ecological zones of Guizhou-eastern （Sinan）， central （Guiding）， and western （Dafang）-using a randomized complete block design. Ten critical parameters， encompassing fresh yield， dry matter yield， crude protein， and relative feed value， were systematically assessed. Grey relational analysis （GRA） was employed to elucidate the key meteorological determinants influencing cultivar performance， and an entropy-weighted dynamic technique for order preference by similarity to ideal solution （DTOPSIS） model was constructed for the objective ranking of the overall performance of each cultivar within each ecological zone. The results revealed significant variations in the rankings of cultivars across the different trial sites. The GRA indicated that， in contrast to traditional temperate zones， daily average precipitation and daily average photosynthetically active radiation were the primary limiting meteorological factors constraining the dry matter yield and crude protein content of silage maize in Guizhou， exhibiting high relational degrees （0.7429 and 0.6678， respectively）. The multivariate evaluation via the entropy-weighted DTOPSIS model facilitated the identification of cultivars adapted to specific ecological niches. For the warm and humid eastern and central regions （Sinan， Guiding）， the better-performing cultivars were identified as Yu silage 23， Dajingjiu 4059， and Quchen No.19. By contrast， for the cooler western region （Dafang）， Jingke silage 932， Quchen 512， and Jingjiu silage 16 were recommended. This study provides a novel， regionalized deployment strategy for silage maize in the complex mountainous terrain of Guizhou， grounded in genotype×environment （G×E） interaction analysis and MCDM modeling. It offers crucial decision-making support and a theoretical foundation for the precise implementation of the national “Grain-to-Fodder” policy and the sustainable development of the regional grass-feeding livestock industry.

Key words: silage maize, variety, yield, nutritional quality, grey relational analysis, dynamic technique for order preference by similarity to ideal solution