Panda Sudhanshu S, Siddique Aftab, Terrill Thomas H, Mahapatra Ajit K, Morgan Eric, Pech-Cervantes Andres A, van Wyk Jan A
Institute for Environmental Spatial Analysis, University of North Georgia, Oakwood, GA, United States.
Department of Agricultural Sciences, Fort Valley State University, Fort Valley, GA, United States.
Front Plant Sci. 2025 Jul 17;16:1520163. doi: 10.3389/fpls.2025.1520163. eCollection 2025.
Small-scale farmers in the southeastern United States face increasing challenges in sustaining forage production due to erratic rainfall, poor soils, and limited access to precision agricultural tools. These constraints demand site-specific solutions that integrate climate resilience with sustainable land use. This study introduces a pioneering Site-Specific Fodder Management Decision Support System (SSFM-DSS) designed to optimize the cultivation of (sericea lespedeza), a drought-tolerant, nitrogen-fixing legume well-suited for marginal lands. By integrating high-resolution geospatial technologies-Geographic Information Systems (GIS), Global Navigation Satellite Systems (GNSS), and remote sensing-with empirical field data and predictive modeling, we have developed an automated suitability framework for SL cultivation across Alabama, Georgia, and South Carolina. The model incorporates multi-criteria environmental parameters, including soil characteristics, topography, and climate variability, to generate spatially explicit recommendations. To translate these insights into actionable strategies, we also developed a farmer-focused WebGIS Dashboard that delivers real-time, location-based guidance for SL production. Our findings underscore the significant potential of SSFM-DSS to enhance fodder availability, improve system resilience under climate stress, and promote sustainable livestock production. This integrative approach offers a promising pathway for climate-smart agriculture, supporting broader food security objectives in vulnerable agroecosystems.
由于降雨不稳定、土壤贫瘠以及难以获得精准农业工具,美国东南部的小农户在维持饲料生产方面面临着越来越多的挑战。这些限制需要因地制宜的解决方案,将气候适应能力与可持续土地利用结合起来。本研究引入了一个开创性的特定地点饲料管理决策支持系统(SSFM-DSS),旨在优化绢毛胡枝子的种植,绢毛胡枝子是一种耐旱、固氮的豆科植物,非常适合边际土地。通过将高分辨率地理空间技术——地理信息系统(GIS)、全球导航卫星系统(GNSS)和遥感——与实地经验数据和预测模型相结合,我们开发了一个用于在阿拉巴马州、佐治亚州和南卡罗来纳州种植绢毛胡枝子的自动化适宜性框架。该模型纳入了多标准环境参数,包括土壤特性、地形和气候变异性,以生成空间明确的建议。为了将这些见解转化为可操作的策略,我们还开发了一个以农民为中心的网络地理信息系统仪表板,为绢毛胡枝子生产提供实时、基于位置的指导。我们的研究结果强调了SSFM-DSS在提高饲料供应、增强气候压力下的系统恢复力以及促进可持续畜牧生产方面的巨大潜力。这种综合方法为气候智能型农业提供了一条有前景的途径,支持脆弱农业生态系统中更广泛的粮食安全目标。