Details on growth and yield for cassava planted on different dates are useful for determining suitable genotypes for particular growing seasons. Our aim was to study growth and yield of cassava planted on different dates. Four cassava genotypes (Kasetsart 50, Rayong 9, Rayong 11 and CMR38-125-77) were evaluated using a randomized complete block design (RCBD) with four replications in six growing periods (20 Apr, 25 May, 30 June, 5 Oct, 10 Nov and 15 Dec 2015-2016) at Khon Kaen, Thailand. Soil properties were determined prior to planting, and crop traits and weather data were recorded. The six planting dates had a statistically significant effect on all crop traits. Low temperatures and solar radiation related to low biomass accumulation rates and short periods of linear phases for total crop and storage root dry weights with the 30 June planting date. CMR38-125-77 is likely to be a good genotype with respect to total crop and storage root dry weights at final harvest for almost all growing dates, except for the 20 Apr. Leaf area index (LAI) at 120, 240 and 300 days after planting (DAP), specific leaf area (SLA) at 120 DAP, storage root growth rate (SRGR) during 300-360 DAP and leaf growth rate (LGR) during 60-120 and 300-360 DAP were the components for the physiological determinants of total crop and storage root dry weight. The relationship between these physiological traits and storage root could be useful for cassava breeding.

Information on the forking, leaf area index, and biomass of cassava for different growing seasons could help design appropriate management to improve yield. The objective was to evaluate the forking date, leaf growth, and storage root yield of different cassava genotypes grown at different planting dates. Four cassava genotypes (Kasetsart 50, Rayong 9, Rayong 11, and CMR38–125–77) were evaluated using a randomized complete block design with four replications. The cassava genotypes were planted on 20 April, 25 May, 30 June, 5 October, 10 November, and 15 December 2015, and 19 May and 3 November 2016. The soil properties prior to the planting, forking date, leaf area index (LAI), dry weights, harvest index (HI), starch content, and weather data were recorded. The forking date patterns for all of the growing seasons varied depending on the cassava genotypes. The weather caused occurring in the first forking for the Rayong 11 and CMR38–125–77 and the second forking for Rayong 11, but not for Kasetsart 50. The forking CMR38–125–77 had a higher LAI, leaf dry weight, biomass, and storage root dry weight than the non-forking Rayong 9. The higher storage root yields in Rayong 9 compared with Rayong 11 were due to an increased partitioning of the storage roots.

Crop simulation models can be used to identify appropriate genotypes and growing environments for improving cassava yield. The aim of this study was to determine the best genotypes for different cassava production environments using the cropping system model (CSM)–MANIHOT–Cassava. Data from cassava experiments that were conducted from 2009–2011 and 2014–2015 at Khon Kaen, Thailand, were used to evaluate the model. Simulations were then conducted for different scenarios using four cassava genotypes (Kasetsart 50, Rayong 9, Rayong 11, and CMR38–125–77), twelve planting dates (at monthly intervals starting in January and ending in December), and ten locations in Thailand under fully irrigated and rainfed conditions using 30 years of historical weather data. Model evaluation with the experimental data for total biomass and storage root yield indicated that the model classified well for relative productivity among different planting dates. The model indicated that growing cassava under irrigated conditions generally produced higher biomass and storage root yield than under rainfed conditions. The cassava genotype CMR38–125–77 was identified for high biomass, while the genotype Rayong 9 was identified as a good genetic resource for high yield. The December planting date resulted in the highest biomass for all locations, while the February planting date produced the highest storage root yield for almost all locations. The results from this study suggest that the CSM–MANIHOT–Cassava model can assist in determining suitable genotypes for different cassava production environments for Thailand, and that this approach could be applicable to other cassava growing areas.

Global warming causes an alarming increase in temperature which negatively affects growth, physiology, and yield of rice. To gain some insights into the impacts of booting HT (at 42°C, 3 h for 7 days), we investigated its effect in three rice genotypes, namely, N22, KDML105 and IR64. Booting HT caused an extended phenology and lower photosynthesis and plant growth rate but an increase in chalkiness. Although, prolonged phenology from dough to physiological maturity led to longer duration of grain filling, the negative effects of its was significantly lower yield components, yield and harvest index in all rice. Among cultivars, N22 presented adapted ability to maintain leaf gas exchange and compensated vegetative part by an increase in tiller numbers resulting in less affected growth rate. It prolonged phenology that caused extended grain filling. Consequence, N22 was the lowest reduction in number of seed panicle^–1, number of filled seeds hill^–1, yield, HI as well as the lowest percentage chalkiness. KDML105 adapted to booting HT by maintaining leaf gas exchange, increasing specific leaf area and prolonging phenology. The longest extended phenology together with the maintenance of high photosynthesis during dough grain were associated with moderate yield reduction and chalkiness. However, IR64 showed dramatic reductions in photosynthesis, growth rate, and yield but highest percentage grain chalkiness. Therefore, in response to booting HT, N22, KDML105 and IR64 could be indicated as being heat tolerant, moderately heat sensitive, and heat sensitive, respectively. This can be applied for crop modelling and rice heat tolerance in breeding program.