The Bio Economic Seaweed Model (BESeM) for modelling tropical seaweed cultivation - experimentation and modelling.

The Bio Economic Seaweed Model (BESeM) is a model designed for modelling tropical seaweed cultivation. BESeM can simulate the common tropical seaweed cultivation system with multiple harvests per year, clonal reproduction and labour intensive harvesting and replanting activities. Biomass growth is modelled as a sigmoid, with growth being initially exponentially and eventually flattening off towards a maximum weight per plant or per square meter (w f,max ). To estimate the latter, longer duration experiments than normal are needed - in the order of 100 days rather than 45 days. Drying (on platforms on the beach) is simulated as well as increase in harvested chemical concentration over time since planting, for harvested chemicals such as agar extracted from Gracilaria or carrageenan extracted from Kappaphycus or Euchema. BESeM has a limited number of parameters which makes it easily amenable to new sites and species. An experiment is presented for a site in Indonesia in which Gracilaria was monitored for 120 days in 6 nearby sites and from which BESeM model parameters were estimated. A simulation example is presented which illustrates how BESeM can be used to find the optimum combination of replanting weight and harvest cycle length (in days) for maximising gross and net farm income. Supplementary Information: The online version contains supplementary material available at 10.1007/s10811-022-02799-8.

Mapping abiotic stresses for rice in Africa: Drought, cold, iron toxicity, salinity and sodicity.

Maps of abiotic stresses for rice can be useful for (1) prioritizing research and (2) identifying stress hotspots, for directing technologies and varieties to those areas where they are most needed. Large-scale maps of stresses are not available for Africa. This paper considers four abiotic stresses relevant for rice in Africa (drought, cold, iron toxicity and salinity/sodicity). Maps showing hotspots of the stresses, the countries most affected and total potentially affected area are presented. In terms of relative importance, the study identified drought as the most important stress (33% of rice area potentially affected), followed by iron toxicity (12%) and then cold (7%) and salinity/sodicity (2%). Hotspots for iron toxicity, cold and salinity are identified. For drought, local variation along the hydromorphic zone was a stronger determinant than larger-scale climatic variation, therefore mapping of drought based on climatic zones has only limited value. Uncertainties in the mappings are discussed.

Phenology, sterility and inheritance of two environment genic male sterile (EGMS) lines for hybrid rice.

BACKGROUND: There is still limited quantitative understanding of how environmental factors affect sterility of Environment-conditioned genic male sterility (EGMS) lines. A model was developed for this purpose and tested based on experimental data from Ndiaye (Senegal) in 2013-2015. For the two EGMS lines tested here, it was not clear if one or more recessive gene(s) were causing male sterility. This was tested by studying sterility segregation of the F2 populations. RESULTS: Daylength (photoperiod) and minimum temperatures during the period from panicle initiation to flowering had significant effects on male sterility. Results clearly showed that only one recessive gene was involved in causing male sterility. The model was applied to determine the set of sowing dates of two different EGMS lines such that both would flower at the same time the pollen would be completely sterile. In the same time the local popular variety (Sahel 108, the male pollen donor) being sufficiently fertile to produce the hybrid seeds. The model was applied to investigate the viability of the two line breeding system in the same location with climate change (+2oC) and in two other potential locations: in M'Be in Ivory Coast and in the Nile delta in Egypt. CONCLUSIONS: Apart from giving new insights in the relation between environment and EGMS, this study shows that these insights can be used to assess safe sowing windows and assess the suitability of sterility and fertility period of different environments for a two line hybrid rice production system.

Do users ignore spatial data quality? A decision-theoretic perspective.

Risk analysis (RA) has been proposed as a means of assessing fitness for use of spatial data but is only rarely adopted. The proposal is that better decisions can be made by accounting for risks due to errors in spatial data. Why is RA so rarely adopted? Most geographical information science (GISc) literature stresses educational and technical constraints. In this article we propose, based on decision theory, a number of hypotheses for why the user would be more or less willing to spend resources on RA. The hypotheses were tested with a questionnaire, which showed that the willingness to spend resources on RA depends on the presence of feedback mechanisms in the decision-making process, on how much is at stake, and to a minor extent on how well the decision-making process can be modeled.