Mugiyo Hillary, Mhizha Teddious, Chimonyo Vimbayi G P, Mabhaudhi Tafadzwanashe
Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P. Bag X01, Scottsville, Pietermaritzburg, 3209, South Africa.
Department of Physics, Faculty of Science, University of Zimbabwe, 630 Churchill Avenue, Harare, Zimbabwe.
Heliyon. 2021 Feb 6;7(2):e06109. doi: 10.1016/j.heliyon.2021.e06109. eCollection 2021 Feb.
Water scarcity and unreliable weather conditions frequently cause smallholder farmers in Zimbabwe to plant maize ( varieties outside the optimum planting timeframe. This challenge exacts the necessity to develop sowing management options for decision support. The study's objective was to use a hybrid approach to determine the best planting windows and maize varieties. The combination will guide farmers on planting dates, dry spell probability during critical stages of the crop growth cycle and rainfall cessation. To capture farmer's perception on agroclimatic information, a systematic random sampling of 438 smallholders was carried out. An analysis of climatic data during 1949-2012 was conducted using INSTAT to identify the best planting criterion. The best combination of planting criterion and maize varieties analysis was then achieved by optimizing planting dates and maize varieties in the DSSAT environment. It was found that 56.2% of farmers grew short-season varieties, 40.2% medium-season varieties and 3.6% long-season varieties. It was also established that the number of rain days and maize yield had a strong positive relationship (p = 0.0049). No significant association was found amongst maize yield (p > 0.05), and planting date criteria, Depth (40mm in 4 days), the AREX criterion- Agricultural Research Extension (25 mm rainfall in 7 days) and the MET Criterion-Department of Meteorological Services (40 mm in 15 days). Highest yields were simulated under the combination of medium-season maize variety and the AREX and MET criteria. The range of simulated yields from 0.0 t/ha to 2.8 t/ha formed the basis for the development of an operational decision support tool (cropping calendar) with (RMSE) (0.20). The methodology can be used to select the best suitable maize varieties and a range of planting time.
水资源短缺和不可靠的天气状况经常导致津巴布韦的小农户在最佳种植时间范围之外种植玉米品种。这一挑战使得有必要开发播种管理选项以提供决策支持。该研究的目标是采用混合方法来确定最佳种植窗口和玉米品种。这种组合将指导农民确定种植日期、作物生长周期关键阶段的干旱期概率以及降雨停止时间。为了了解农民对农业气候信息的看法，对438名小农户进行了系统随机抽样。使用INSTAT对1949 - 2012年期间的气候数据进行了分析，以确定最佳种植标准。然后通过在DSSAT环境中优化种植日期和玉米品种，实现了种植标准与玉米品种分析的最佳组合。结果发现，56.2%的农民种植短季品种，40.2%种植中季品种，3.6%种植长季品种。还确定降雨天数与玉米产量之间存在很强的正相关关系（p = 0.0049）。在玉米产量（p > 0.05）与种植日期标准、深度（4天内40毫米）、AREX标准 - 农业研究推广（7天内25毫米降雨）和气象服务部的MET标准（15天内40毫米）之间未发现显著关联。在中季玉米品种与AREX和MET标准的组合下模拟出了最高产量。模拟产量范围为0.0吨/公顷至2.8吨/公顷，这为开发具有（均方根误差）（RMSE）为0.20的运营决策支持工具（作物种植日历）奠定了基础。该方法可用于选择最合适的玉米品种和一系列种植时间。
