G. Zhao1 – M. Hoffmann2 – J. Schellberg3
1 Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany
2 Crop Production Systems in the Tropics, Georg-August-Universität Göttingen, Grisebachstr. 6, 37077 Göttingen, Germany
3 Agro- and Production Ecology, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Melbweg 42, 53115 Bonn, Germany
Introduction
As a frequent happening natural hazard, drought is continuously endangering the food production and security of South Africa. This study assesses the vulnerability of grain maize yield to meteorological droughts of three different severities, and compares the vulnerability between subsistence and commercial farms.
Materials and Methods
The commercial and subsistence farms were distinguished by South Africa National Land-Cover Change Map 2000 (Figure 1). Gridded daily weather (1955-2010, ~25 km resolution) and soil data (1 km resolution) were used to drive APSIM (Keating et al., 2003) to simulate the grain maize yields under rainfed conditions. Simulation setup for subsistence and commercial farms differed by nitrogen application rates (0 kg N ha-1 versus N rates vary with rainfall), planting density (3 plant m-2 versus density vary with rainfall) and residue management (removal versus retention). The Standard Precipita-tion Index (SPI, McKee et al., 1993) was used to quantify the spatial-temporal variabil-ity and severvariabil-ity of meteorological droughts. The simulated phenology and leaf area index were validated against remote sensing data (MODIS MCD15A3). Spatial and temporal variability of simulated was validated against province and district yield sta-tistics. We used the probabilistic-based method presented by van Oijen et al., (2013) to quantify the vulnerability as the yield difference between drought and normal years:
𝑉𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑦𝑖 = 𝐸𝑌𝑖𝑒𝑙𝑑|𝑛𝑜𝑚𝑎𝑙 − 𝐸𝑌𝑖𝑒𝑙𝑑|𝑑𝑟𝑜𝑢𝑔ℎ𝑡𝑖 (1)
where i denotes the moderate (-1.5<SPI<-1), severe (-2<SPI<-1.5) and extremely (SPI<-2) droughts.
Results and Discussion
APSIM captured the spatial-temporal variability of maize yields comparing to the yield statistics at province and district levels. It also simulated reasonable phenology and leaf area index (LAI) comparing to the MODIS LAI products. Under moderate drought, the vulnerability of maize yield under subsistence dryland (0.6 t ha-1) farming practices
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was lower than commercial ones (0.8 t ha-1) (Figure 1), while it became larger under extreme drought (1.2 t ha-1 for subsistence and 1.1 t ha-1 for commercial). Since com-mercial farms applied higher N, the expected return was also higher, which resulted in its high vulnerability under moderate drought. The risk associated with higher input depicts a challenge to the currently promoted intensification strategy by the govern-ment. The discovered strong spatial and temporal variability can potentially be used to find adaptive management practices.
Figure 1. Land use of the study area and vulnerability of maize yields under different drought severities(
moderate, severe and extreme) for two farm types (gray for subsistence and dotted green for commercial).
Conclusions
The mean yield in subsistence farms can be by improved by increasing N application and residue retention without increasing vulnerability, indicating that water availability is of less importance when compared to nutrient limiting factors.
Acknowledgements
This study is supported by the German Federal Ministry of Education and Research (BMBF) through the SPACES project “Living Landscapes Limpopo” project.
References
Keating, B.A. et al., (2003). An overview of APSIM, a model designed for farming systems simulation.
European Journal of Agronomy 18, 267-288.
McKee, T.B., et al., (1993). The relationship of drought frequency and duration to time scales, Proceedings of the 8th Conference on Applied Climatology. American Meteorological Society Boston, MA, pp. 179-183.
van Oijen et al., (2013). A novel probabilistic risk analysis to determine the vulnerability of ecosystems to extreme climatic events. Environmental Research Letters 8, 015032.
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Integrating xylem and phloem fluxes into whole-plant models for simulating fleshy fruits
J. Zhu1* – Z. Dai1 – P. Vivin1 – G. Gambetta2 – N. Ollat1 – M. Henk3 – S. Delrot4
1 INRA, Université de Bordeaux, ISVV UMR 1287 EGFV, 33140 Villenave d’Ornon, France
2 Bordeaux Sciences Agro, ISVV, INRA, UMR 1287 EGFV, 33140 Villenave d’Ornon, France
3 Department of Ecoinformatics, Biometrics and Forest Growth, University of Göttingen, 37077 Göttingen, Germany
4 Université de Bordeaux, ISVV, INRA, UMR 1287 EGFV, 33140 Villenave d’Ornon, France
* Correspondance: junqi.zhu@bordeaux.inra.fr Introduction
The expansion of fleshy fruit is mainly driven by processes related with water and sugar fluxes, which are mediated by mechanisms of xylem water transport and phloem loading and unloading. Despite the importance of xylem water potential and phloem sugar concentration in regulating water and sugar fluxes, they have rarely been incorporated into plant models. Here we present a novel functional-structural grapevine (Vitis vinifera L.) model which integrates xylem water and phloem sugar fluxes focused on berry development.
Materials and Methods
A new grapevine model was constructed in the plant modelling software GroIMP (Kniemeyer, 2008). The model integrated the current most advanced algorithms on: 1) coupling of photosynthesis and transpiration (Yin and Struck, 2009); 2) coordination of stomatal aperture, abscisic acid (ABA), transpiration and root conductance (stomata-ABA-root conductance module, Tardieu et al., 2015); 3) balance of sugar loading and unloading via phloem sugar concentration (Bladazzi et al., 2013); 4) fruit growth (Fish-man and Genard, 1998); 5) nitrogen economy model within plant architecture (Bertheloot et al., 2011); 6) their interactions and feedback mechanisms.
The model simulates the potential individual leaf photosynthesis, transpiration, and temperature as a mutually dependent process. The potential stomata conductance resulting from this mutually dependent process was used as an input into the stomata-ABA-root conductance module in order to estimate the real stomata conductance. The actual transpiration, leaf temperature and photosynthesis were updated, in sequence, based on the actual stomata conductance. The sum of leaf transpiration together with soil water potential was used for estimating root conductance and root water potential (considered as xylem water potential because the resistance of the wood and internode for water flux are very small). The leaf nitrogen content was updated based on the rate of N synthesis (related with individual leaf transpiration) and the rate of N degradation. The phloem sugar concentration was calculated based on the balance between the loading of leaf, internode, and wood and the unloading of berry, root, internode and wood.
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Figure 1. Visualization of the grapevine model at veraison (panel a) and the simulated diurnal change of xylem water potential at various soil water contents expressed as fraction of field capacity (panel b).
The color gradient of the leaf in panel (a) represents the proportion of absorbed radiation (from black, low absorbed radiation, to light green, high absorbed radiation).
Results and Discussion
The model produces accurate diurnal changes in photosynthesis and water flux as compared to observations at different soil water contents and various light intensities.
The model simulations showed that water stress and shading both reduce the carbon assimilation but affects differently on berry sugar concentration. Water stress reduces the xylem water potential and thus decreases the berry water import, while shading increases xylem water potential and thus increases the berry water import.
Conclusions
An innovative whole-plant grapevine model which integrates xylem and phloem fluxes has been developed. The model can be used to assess the influence of environmental conditions, management practices, and plant traits on the rate of water and sugar accumulation by the berry.
Acknowledgements
We thank Drs Jochem B. Evers, Xinyou Yin, Francois Tardieu, Jessica Bertheloot for sharing their model codes, and Drs Bruno Andrieu, Romain Barillot, Gilles Vercambre, Michel Genard, Eric Lebon for helpful discussions. We greatly acknowledge the financial support of the INNOVINE project, grant agreement no.FP7-311775.
References
Baldazzi, V., A. Pinet, G. Vercambre, C. Benard, B. Biais and M. Genard (2013). Frontiers in Plant Science 4.
Bertheloot, J., P.-H. Cournède and B. Andrieu (2011). Annals of Botany 108(6): 1085-1096.
Kniemeyer, O. (2008). PhD thesis, Brandenburg University of Technology.
Tardieu, F., T. Simonneau and B. Parent (2015). Journal of Experimental Botany 66 (8): 2227-2237..
Yin, X. and P. C. Struck (2009). NJAS - Wageningen Journal of Life Sciences 57(1): 27-38.
(a) (b)