• Energy Research

Traditional leafy vegetables (TLVs’) are vegetables that were introduced in an area a long time ago, where they adapted to local conditions and became part of the local culture. In Sub-Saharan Africa, the use of TLVs’ as a nutrient dense alternative food source to combat micronutrient deficiency of rural resource-poor households (RRPHs), has gained attention in debates on food and nutrition security. However, TLVs’ are underutilised because of lack of information on their yield response to water and fertiliser. To better assess TLVs’ yield response to water stress, the AquaCrop model was calibrated (using 2013/14 data) and validated (using 2014/15 data) for three repeatedly harvested leafy vegetables [Amaranthus cruentus (Amaranth), Cleome gynandra (Spider flower), and Beta vulgaris (Swiss chard)] in Pretoria, South Africa. Experiments were conducted during two consecutive seasons, in which the selected leafy vegetables were subjected to two irrigation regimes; well-watered (I30) and severe water stress (I80). Measured parameters were canopy cover (CC), soil water content (SWC), aboveground biomass (AGB), actual evapotranspiration (ETa), and water productivity (WP). Statistical indicators [root mean square error (RMSE), RMSE-standard deviation ratio (RSR), R2, and relative deviation] showed good fit between measured and simulated (0.60 2 a and WP. Nevertheless, the model simulated the selected parameters satisfactorily. These results revealed that there was a clear difference between transpiration water productivity (WPTr) for C4 crops (Amaranth and Spider flower) and a C3 crop (Swiss chard); WPTr for the C4 crops ranged from 4.61 to 6.86 kg m−3, whereas for the C3 crop, WPTr ranged from 3.11 to 4.43 kg m−3. It is a challenge to simulate yield response of repeatedly harvested leafy vegetables because the model cannot run sequential harvests at one time; therefore, each harvest needs to be simulated separately, making it cumbersome. To design sustainable food production systems that are health-driven and inclusive of RRPHs, we recommend that more vegetables (including traditional vegetables) should be included in the model database, and that sequential harvesting be facilitated.

During the millions of years of evolution, photosynthetic organisms have adapted to almost all terrestrial and aquatic habitats, although some environments are obviously more suitable for photosynthesis than others. Photosynthetic organisms living in low-light conditions require on the one hand a large light-harvesting apparatus to absorb as many photons as possible. On the other hand, the excitation trapping time scales with the size of the light-harvesting system, and the longer the distance over which the formed excitations have to be transferred, the larger the probability to lose excitations. Therefore a compromise between photon capture efficiency and excitation trapping efficiency needs to be found. Here we report results on the whole cells of the green sulfur bacterium Chlorobaculum tepidum. Its efficiency of excitation energy transfer and charge separation enables the organism to live in environments with very low illumination. Using fluorescence measurements with picosecond resolution, we estimate that despite a rather large size and complex composition of its light-harvesting apparatus, the quantum efficiency of its photochemistry is around ~87% at 20 °C, ~83% at 45 °C, and about ~81% at 77 K when part of the excitation energy is trapped by low-energy bacteriochlorophyll a molecules. The data are evaluated using target analysis, which provides further insight into the functional organization of the low-light adapted photosynthetic apparatus.

Intimate relationships exist between form and function of plants, determining many processes governing their growth and development. However, in most crop simulation models that have been created to simulate plant growth and, for example, predict biomass production, plant structure has been neglected. In this study, a detailed simulation model of growth and development of spring wheat (Triticum aestivum) is presented, which integrates degree of tillering and canopy architecture with organ-level light interception, photosynthesis, and dry-matter partitioning. An existing spatially explicit 3D architectural model of wheat development was extended with routines for organ-level microclimate, photosynthesis, assimilate distribution within the plant structure according to organ demands, and organ growth and development. Outgrowth of tiller buds was made dependent on the ratio between assimilate supply and demand of the plants. Organ-level photosynthesis, biomass production, and bud outgrowth were simulated satisfactorily. However, to improve crop simulation results more efforts are needed mechanistically to model other major plant physiological processes such as nitrogen uptake and distribution, tiller death, and leaf senescence. Nevertheless, the work presented here is a significant step forwards towards a mechanistic functional-structural plant model, which integrates plant architecture with key plant processes.

The aim of the research described was to design permanent vegetable production systems for the Red River Delta in Vietnam. Permanent vegetable production systems better meet the increasing consumer demand for vegetables and may increase farmers’ income. Optimum crop sequences for permanent vegetable production in the Red River Delta were designed with the recently developed model PermVeg. The crop sequences designed were tested in a field experiment from May 2007 to May 2009. The production systems tested were five systems designed according to the scenarios of (i) high profitability, (ii) low labor requirement, (iii) low costs of pesticide use, (iv) high level of crop biodiversity, and (v) low perishable products, respectively. The five systems were compared with the traditional vegetable production system. At local prices, only the high profitability and low labor requirement systems yielded significantly higher profits than the traditional system. At city wholesale market prices, profits of all permanent vegetable production systems were significantly higher than that of the traditional system, except for the low perishability system. Permanent vegetable production systems required more labor than the traditional system. Labor-day incomes of permanent vegetable production systems generally were not higher than those of the traditional system. The labor-day income increased only with the low labor requirement system at city wholesale market prices. The model outcomes correlated reasonably well with the labor requirement and the length in days of production systems in the field. The model poorly predicted profits and costs of pesticide use. We concluded that permanent vegetable production systems can yield higher profits than the traditional system, and can contribute to enhancing employment opportunities and increasing household income.

Given the need for parallel increases in food and energy production from crops in the context of global change, crop simulation models and data sets to feed these models with photosynthesis and respiration parameters are increasingly important. This study provides information on photosynthesis and respiration for three energy crops (sunflower, kenaf, and cynara), reviews relevant information for five other crops (wheat, barley, cotton, tobacco, and grape), and assesses how conserved photosynthesis parameters are among crops. Using large data sets and optimization techniques, the C(3) leaf photosynthesis model of Farquhar, von Caemmerer, and Berry (FvCB) and an empirical night respiration model for tested energy crops accounting for effects of temperature and leaf nitrogen were parameterized. Instead of the common approach of using information on net photosynthesis response to CO(2) at the stomatal cavity (A(n)-C(i)), the model was parameterized by analysing the photosynthesis response to incident light intensity (A(n)-I(inc)). Convincing evidence is provided that the maximum Rubisco carboxylation rate or the maximum electron transport rate was very similar whether derived from A(n)-C(i) or from A(n)-I(inc) data sets. Parameters characterizing Rubisco limitation, electron transport limitation, the degree to which light inhibits leaf respiration, night respiration, and the minimum leaf nitrogen required for photosynthesis were then determined. Model predictions were validated against independent sets. Only a few FvCB parameters were conserved among crop species, thus species-specific FvCB model parameters are needed for crop modelling. Therefore, information from readily available but underexplored A(n)-I(inc) data should be re-analysed, thereby expanding the potential of combining classical photosynthetic data and the biochemical model.

Agriculture contributes significantly to the global greenhouse gas (GHG) emissions. Farmers need to fine-tune agricultural practices to balance the trade-offs between increasing productivity in order to feed a growing population and lowering GHG emissions to mitigate climate change and its impact on agriculture. We conducted a survey on the major cultural practices in four potato production systems in Zimbabwe, namely large-scale commercial, communal area, A1 and A2 resettlement production systems. The resettlement production systems were formed from the radical Fast Track Land Reform Programme initiated in 2000, which changed the landscape of commercial agriculture in Zimbabwe. We used survey data as an input into the ‘Cool Farm Tool – Potato’ model. The model calculates the contributions of various production operations to total GHG emission. Experienced growers were targeted. The average carbon footprint calculated was 251 kg CO2 eq./t potato harvested, ranging from 216 kg CO2 eq./t to 286 kg CO2 eq./t in the communal area and A2 resettlement production systems, respectively. The major drivers of the GHG emissions were fertilizer production and soil-related field emissions, which together accounted for on average 56% of the total emissions across all production systems. Although mitigation options were not assessed, the model outputs the factors/farm operations and their respective emission estimates allowing growers to choose the inputs and operations to reduce their carbon footprint. Opportunities for benchmarking as an incentive to improve performance exist given the large variation in GHG emission between individual growers.