No abstract

Projected changes in temperature and rainfall directly threaten the overall agricultural crop production system in Zambia where 75% of agricultural production is rainfed. This study investigates the effects of future climate on maize (Zea mays L.) and soybean [Glycine max (L.) Merr.] yields using relatively high spatial resolution downscaled climate data in Central and Eastern Zambia. The Agrometshell model was applied to simulate historical and future crop yields. To account for the effects of climate change on crop yields, global climate models (GCMs) and Representative Concentration Pathways (RCPs) were used. Most yield functions were significant with R2 values >.7 with p < .05, indicating the model's ability to capture variations in observed data. Mean maize yield change decreased across RCPs and GCMs in Central Province and the decrease amplified in several areas in the far future, suggesting worsening effects into the future. But the projected mean yield changes in Eastern Province remain mostly within the historical range of variability, suggesting that maize yield in this province is less sensitive to climate change. Projected mean soybean yield changes in Central Province consist of a combination of reductions and improvements. Soybean yields in Eastern Province were projected to increase in the near future, shifting to large increases in the far future. Drastic reductions in maize yields are likely to cause major negative effects in the agricultural system because maize is a staple crop in the country. In contrast, soybean showed less sensitivity to future climate and demonstrates the value of diversifying agricultural systems and increasing the sustainability of food production systems.

Forest inventories in plantations of non-native trees are conducted every five years in Zambia. Characteristics of data collected through these inventories are presented here. The data includes diameter at breast height ( d ), total tree height ( h) and rotation categories for trees sampled. This data supported the development of robust h-d models for planted Pinus kesiya in the country. We have also presented graphical visualization of the composition and trends of the data by site and rotation. Datasets were filtered and cleaned and are ready to be used for other purposes in order to improve understanding of P. kesiya growth. For more insight please see “Modeling the height-diameter relationship of planted Pinus kesiya in Zambia” (Ng’andwe et al., 2019).