ABSTRACT
Tapping Panel Dryness (TPD) is a physiological disorder affecting natural rubber production in Hevea brasiliensis. TPD is associated with clonal susceptibility and overexploitation of rubber trees. Most studies are based on a binary point view of the absence or presence of TPD. This study sets out to characterize the dynamic of the TPD onset through the monthly monitoring of the dry cut length. This reveals the presence of dry spots on the tapped panel of any trees. The frequency of these dry spots increases dramatically in trees developing high level of TPD. Brown bast is an irreversible form of TPD. Brown bast is correlated to a high level of dry cut length. Application of an intensive harvesting system induces early TPD occurrence, which facilitates the study of TPD. Among latex diagnosis parameters, only sucrose content is significantly associated with TPD. Other parameters are more prone to environmental effects and are not reliable as physiological markers. These findings explain the contradictory conclusions of some papers. This study suggests to use intensive harvesting system and monitor the dry cut length for genetic analysis of TPD.
ABSTRACT
Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2 ], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2 ], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2 ]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.
