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This article is an attempt to assess the invasion risk from the most noxious alien plant species using the GPS recorded locations and environmental variables. The invasion risk was modelled by combining the three ecological niche modelling algorithms- DesktopGARP, Openmodeller DesktopGARP and Maxent after validating their accuracies. The accuracies ranged from moderate to good in all the algorithms, for all six species. The result showed Ageratina adenophora and Ageratum conyzoides as highly invasive species both in terms of area coverage and the ecological tolerance range of the study site. It was also indicative that, irrespective of the species, agricultural lands are most susceptible to invasion among all other types of land uses in the study area.

This article is an attempt to assess the invasion risk from the most noxious alien plant species using the GPS recorded locations and environmental variables. The invasion risk was modelled by combining the three ecological niche modelling algorithms- DesktopGARP, Openmodeller DesktopGARP and Maxent after validating their accuracies. The accuracies ranged from moderate to good in all the algorithms, for all six species. The result showed Ageratina adenophora and Ageratum conyzoides as highly invasive species both in terms of area coverage and the ecological tolerance range of the study site. It was also indicative that, irrespective of the species, agricultural lands are most susceptible to invasion among all other types of land uses in the study area.

Matching fertilization with crop needs is important for maximizing yields and reducing fertilizer losses. Seasonal variation in nutrient uptake dynamics is poorly understood and thus, the ability to optimize fertilization strategies is limited. This study aims to investigate the effects of planting dates on macronutrient uptake dynamics in cassava genotypes with full irrigation. The performance of cassava genotypes, i.e., CMR38-125-77, Kasetsart 50 and Rayong 11, were evaluated in the early rainy (ERS) and post rainy seasons (PRS) for two years using a randomized complete block design with four replicates. The plants were harvested at 1, 3, 6, 9 and 12 months. Planting dates had significant effects on the accumulation of dry matter and storage roots as well as nutrient uptakes and partitioning. On average, the total nutrient uptake per plant to produce 2831–3279 g of biomass with 1244–1810 g of storage roots in the ERS varied among cassava genotypes, ranging from 21.1–24.3 g N, 5.1–5.9 g P, 26.5–29.5 g K, 14.1–22.2 g Ca, 6.1–7.6 g Mg and 2.0–2.3 g S. The total nutrient uptake per plant to produce 3353–3824 g of biomass with 1604–2253 g of storage roots in the PRS ranged from 27.1–32.4 g N, 5.2–6.0 g P, 29.1–31.3 g K, 11.9–20.3 g Ca, 7.3–9.9 g Mg and 1.2–1.5 g S. In the ERS, the majority of the total nutrient uptake occurred at the early growth stages, whereas in the PRS, this occurred at the mid- to late growth stages. At final harvest, the percentages of nutrient removal by the storage roots for ERS were 24.7–36.0% N, 26.0–32.3% P, 43.4–51.5% K, 12.4–17.6% Ca, 22.2–31.5% Mg and 27.2–31.5% S, whereas in the PRS the percentages were 30.4–44.4% N, 33.3–41.6% P, 44.7–57.3% K, 12.0–15.1% Ca, 20.2–28.1% Mg and 12.0–25.4% S. CMR38-125-77 exhibited satisfactory performance in nutrient uptake, nutrient use efficiency and storage roots yield across the planting dates. The evidence obtained from this study would greatly facilitate more efficient adoption of precision agriculture in cassava production by applying recommended fertilizers, e.g., rates, kinds and timings, according to crop demand in each growing season in Thailand and for choosing superior cassava genotypes.

The objective of this study was to investigate the effect of canopy size (CS) on light penetration and leaf photosynthesis of three cassava genotypes (Kasetsart 50 (KU50), Rayong 11 and CMR38-125-77) under two planting dates in two years. Data were recorded for CS, leaf area index (LAI), leaf photosynthesis, biomass (BM), storage root yield (SRY), starch content (SC) and harvest index (HI). The variation of CS depended on temperature, solar radiation (SR), relative humidity (RH) and day length (DL). In early growth stages, the crops planted in May had higher CS than the crops planted in November, because they were subjected to higher temperature, more SR, higher RH and longer DL. In contrast, the storage root accumulation of the crops planted in November was under better climatic factors than the crops planted in May. Therefore, the crops planted in November had higher BM and SRY than the crops planted in May for KU50 and CMR38-125-77. However, Rayong 11 in the May planting date maintained higher CS during storage root accumulation than the other genotypes, and BM and SRY of Rayong 11 were not different for the two planting dates in both years. KU50 and CMR38-125-77 had higher BM and SRY in the November planting, whereas in the May planting, the SRY and BM of the three genotypes were not significantly different. Genotypes that maintained high CS at the storage root accumulation stage could be indirectly selected for high BM and SRY in cassava breeding programs.

Diurnal photosynthesis responses of cassava cultivar Rayong 9 ('RY9') three months after planting, grown in a field conditions under irrigated and rainfed conditions, were evaluated during the rainy, cool, and hot seasons. Under the mild conditions of the rainy and cool seasons, net photosynthetic rates (PN) increased in parallel with light intensity and attained the maximum at 13.00 or 11.00 h. In the hot season, PN attained the prominent peak at 9.00 h, after which stomatal conductance decreased rapidly coordinated with declining PN and nonphotochemical quenching was enhanced. Photosynthetically active radiation was the major factor influencing PN in the rainy and cool seasons, whereas vapor pressure deficit was the major factor in the hot season. 'RY9' adapted extremely well in this climate because the maximal quantum yield of PSII photochemistry recovered fully in the evening even under the rainfed conditions in the hot season.

Seasonal variations in photosynthesis of cassava cv. Rayong 9 (RY9) under irrigated and rain-fed conditions were evaluated at the age of three and six months after planting (MAP). Photosynthetic light-response (PN/I) curves revealed that cassava leaves attained the highest maximum net photosynthetic rates (PNmax) in the rainy season, followed by the hot one, while the lowest PNmax was found in the cool season. Photosynthetic potential of the 3-month-old plants was mostly higher than that of the 6-month-old plants, and the seasonal variation in photosynthetic capacity was also more apparent in the younger plants. PN/I curves were used to predict daily net photosynthetic rate (PN) for each season based on daily average solar radiation data. The predicted PN were considerably lower than the PNmax values. This indicated that solar radiation is a limiting factor for photosynthesis, particularly in the rainy season. The data provided basic information for breeding cassava genotypes with enhanced photosynthesis during the period of unfavorable environment. Furthermore, the data are potentially useful in modeling photosynthesis and crop growth as affected by environmental factors.

Information on macronutrient uptake and nutrient use efficiency (NUE) of cassava genotypes under different planting dates is limited. The aims of this study were 1) to investigate the responses of cassava genotypes to planting dates for nutrient uptake and NUE and 2) to identify the superior genotypes based on these traits. Three cassava genotypes (Kasetsart 50 (KU50), Rayong 11 (RY11) and CMR38-125-77) were planted in a randomized complete block design with four replications at four planting dates (20-Apr, 30-Jun, 5-Oct and 15-Dec 2016) and harvested at 12 months after planting (MAP). Planting cassava on 20-Apr and 15-Dec produced the highest total crop nutrient uptake and nutrient uptake for storage roots. Whereas planting cassava on 5-Oct and 15-Dec with higher solar radiation, temperature, relative humidity and longer photoperiod during canopy establishment until high carbohydrates translocation and with lower temperature and shorter photoperiod during a dormancy period resulted in greater NUE for biomass and storage roots and nutrient harvest index for all genotypes and most elements. CMR38-125-77 and KU50 showed good performance in terms of total nutrient uptake across planting dates (1.45–38.31 g plant−1) for most elements. CMR38-125-77 seems to be the best genotype on the basis of high storage root dry weight, high NUE for both biomass (90.1–1,594.6) and storage roots (39.9–927.1) and nutrient harvest index (0.14–0.58) for almost all planting dates, except in planting date 20-Apr in which RY11 was the highest for these traits. The genotype identified is useful as parental source for further breeding programs to improve genotypes with high NUE and high storage root yield.

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.

The objectives of this study were to investigate the effect of seasonal variation on canopy size, and the effect of canopy size on light penetration of &lsquo, Rayong 9&prime, cassava under irrigated and rainfed conditions. Rayong 9 was planted under two water regimes in a randomized complete block design with four replications in May and November for two years. At final harvest, years were significantly different (p &le, 0.05) for biomass, shoot dry weight, and harvest index and contributed to large portions of total variations in shoot dry weight (56.8%) and HI (44.5%). Planting date was a significant source of variations in all measured characters, and it contributed to the largest portions of variations in biomass, storage root dry weight and storage root fresh weight (46.1&ndash, 60.9%). Water regimes were not significantly different for most characters except for harvest index (p &le, 0.01). The canopy of the crop planted in May grew rapidly in early growth stages in the rainy season and then slowly after the rainy season. The canopy of the crop planted in November grew rapidly in the middle to the late growth stages. Irrigation did not significantly increase root yield although it slightly increased canopy development, leaf area index (LAI), light penetration and photosynthesis. Irrigation at the late growth stages of the crop planted in May significantly increased storage root yield. Irrigation at these growth stages helped maintain canopy development, LAI and light penetration.

Selecting the appropriate genotypes for particular planting dates may help increase cassava productivity. The CSM–MANIHOT–Cassava model is a new tool to support decision making for crop management. However, it is important to explore the potential of a model prior to any application. The objective of this study was to evaluate the CSM–MANIHOT–Cassava model in simulating the performance of cassava genotypes for different planting dates. An experiment with four cassava genotypes and eight planting dates was conducted on the experimental farm of the Faculty of Agriculture at Khon Kaen University, Thailand, under optimum conditions. The data for soil and weather conditions, crop management, and crop traits were recorded. The cultivar parameters were determined using experimental data for three planting dates, while the remaining five planting dates were used for model evaluation. A scenario analysis for different planting dates using historical weather data from 2003 to 2018 was also conducted. The calibration results showed a good agreement between simulated and observed phenology and crop traits. Model evaluation based on the d-index showed a good agreement between simulated and observed total crop, stem, and storage root dry weights with values above 0.8 for almost all four genotypes and five planting dates. The model provided acceptable outputs for final total crop dry matter and storage root yield. The results from both the scenario analysis with the CSM–MANIHOT–Cassava model and the actual experiment classified Kasetsart 50 and CMR38–125–77 as one out of the top two genotypes based on final total crop biomass, demonstrating the potential of the model to help identify promising cassava genotypes.

SUMMARYPhysiological traits can be used to improve the efficiency of selecting suitable genotypes to grow under nitrogen (N) limitation. The objective of the current study was to investigate the relationship between physiological characteristics and storage root yield of three cassava genotypes under three rates of N fertilizer. The experiments were conducted from 2014 to 2016 at farm fields in Thailand. A split-plot randomized complete block design with four replications was used. Three different rates of N fertilizer, i.e., 46·9, 90·0 and 133·2 kg N/ha and three cassava genotypes, Rayong 9, Rayong 11 and Kasetsart 50, were used. Kasetsart 50 had the highest mean performance for most crop traits. Growth rate of stem (SGR), storage root (SRGR) and crop (CGR) during 180–210 days after planting (DAP) and leaf area index (LAI) at 120 DAP were related to storage root dry weight for all three rates of N fertilizer. Storage root growth during 90–120 DAP, CGR during 180–210 DAP and specific leaf area (SLA) at 210 DAP contributed most to storage root dry weight of the three genotypes grown at 46·9 kg N/ha, while the combination of SRGR during 90–120 DAP, SRGR during 180–210 DAP, LAI at 210 DAP and SLA at 210 DAP was best for N fertilizer at 90·0 kg N/ha and the combination of leaf growth rate (LGR) during 180–210 DAP and LAI at 210 DAP was best for N fertilizer at 133·2 kg N/ha.

Information on leaf performances in different growing seasons of cassava could help achieve the recommendation regarding suitable cassava genotypes for particular planting date and a new plant type to improve cassava productivity. The objective of this study was to investigate leaf growth habit and biomass of the four cassava genotypes grown under irrigated condition in different growing seasons. The four cassava genotypes, i.e., Kasetsart 50, Rayong 9, Rayong 11 and CMR38-125-77 were grown at 30 June 2015 and 20 May 2016 and 3 November 2016 at Khon Kaen University, Thailand. A Randomised Complete Block Design (RCBD) with four replications was used for each planting date. Data recorded were leaf life, fallen leaf ratio, leaf area index (LAI), leaf area duration (LAD), storage root dry weight, total crop dry weight, harvest index (HI) and daily weather data. The results indicated that greater values of leaf life during 180 days after planting (DAP), LAD during 180-270 DAP, and LAI at 270 DAP appeared to associate with higher temperature. A combination of LAI at 180 DAP, leaf life during 90 and 180 DAP and fallen leaf ratio for 90 and 180 DAP was related to total biomass, while storage root dry weight was associated with a combination of LAI at 180 and 270 DAP and fallen leaf ratio for 90 and 180 DAP. CMR38-125-77 produced high total biomass and storage root dry weight at 270 DAP with possessing high LAI at 180 DAP.

The determination of optimum crop management practices for increasing soybean production can provide valuable information for strategic planning in the tropics. However, this process is time consuming and expensive. The use of a dynamic crop simulation model can be an alternative option to help estimate yield levels under various growing conditions. The objectives of this study were to evaluate the performance of the Cropping System Model (CSM)-CROPGROSoybean and to determine optimum management practices for soybean for growing conditions in the Phu Pha Man district, Thailand. Data from two soybean experiments that were conducted in 1991 at Chiang Mai University and in 2003 at Khon Kaen University were used to determine the cultivar coefficients for the cultivars CM 60 and SJ 5. The CSM-CROPGRO-Soybean model was evaluated with data from two experiments that were conducted at Chiang Mai University. The observed data sets from farmers’ fields located in the Phu Pha Man district were also used for model evaluation. Simulations for different management scenarios were conducted with soil property information for seven different soil series and historical weather data for the period 1972–2003 to predict the optimum crop management practices for soybean production in the Phu Pha Man district. The results of this study indicated that the cultivar coefficients of the two soybean cultivars resulted in simulated growth and development parameters that were in good agreement with almost all observed parameters. Model evaluation showed a good agreement between simulated and observed data for phenology and growth of soybean, and demonstrated the potential of the CSM-CROPGROSoybean model to simulate growth and yield for local environments, including farmers’ fields, in Thailand. The CSM-CROPGRO-Soybean simulations indicated that the optimum planting dates from June 15 to July 15 produced maximum soybean yield in a rainfed environment. However, the planting date December 15 produced the highest yield under quality irrigation. Soybean yield was slightly improved by applying nitrogen at a rate of 30 kg N ha)1 at planting. Soybean yield also improved when the plant density was increased from 20 to 40 plants m)2. The results from this study suggest that the CSM-CROPGRO-Soybean model can be a valuable tool in assisting with determining optimum management practices for soybean cropping systems in the Phu Pha Man district and might be applicable to other agricultural production areas in Thailand and southeast Asia.

Neglected and Underutilized Crop Species (NUS) are grown primarily by traditional farmers in developing countries. They were once more widely grown but are today falling into disuse for a variety of agronomic, genetic, economic and cultural factors. While these crops continue to be maintained by socio-cultural preferences and use practices, most of them remain inadequately characterized and neglected by research and conservation. The aim of our work was to improve the agricultural portfolio by identifying promising neglected and underutilised species in China, Cambodia, Northeastern Thailand and Northern Vietnam. We began with preparing a “Masterlist” containing 260 species, based on farmer interviews and literature review. After an initial pre-selection we identified 17 NUS for China, 13 for Cambodia, 12 for Thailand, and 22 for Vietnam as of highest priority. These NUS then underwent a multi-criteria and trans-disciplinary assessment involving 511 stakeholders such as scientists, farmers, NGOs and policy makers. Based on the assessment we identified the most promising NUS for each country. We also identified some limitations for the promotion of so-called priority NUS regarding to some substancial disagreement between the involved stakeholders. In China and Cambodia, for example, farmers judged the potential of NUS significantly lower than other stakeholders, and in Vietnam scientists and policy makers had substantially different opinions on NUS compared to farmers. Addressing these ambiguous views will be highly relevant to the develoment of an improved agricultural portfolio.

Detailed growth analysis and developmental data are normally required for calibration of crop simulation models in order to determine the cultivar coefficients that correspond to the unique characteristics of each crop and cultivar. The objective of this study was to evaluate the suitability of using variety trial data in which no phenology data were collected for estimating the cultivar coefficients for the CSM-CROPGRO-Peanut model. We selected three peanut cultivars of different maturity, which are commonly grown cultivars in the southeastern U.S. The cultivars were an early maturity cultivar (Virugard), a medium maturity cultivar (Georgia Green), and a late maturity cultivar (C-99R). The variety trial data for these cultivars were obtained from the research reports on variety performance tests conducted in three locations in Georgia from 1994 to 2001. A similar cultivar in the same maturity group was used as a starting point from which to calibrate each of the cultivars. Synthetic cultivars with corresponding values for the different combinations of the selected cultivar coefficients were created for the CSM-CROPGRO-Peanut model; the model was then run for each of these cultivars. The set of coefficients for the synthetic cultivar that produced the lowest root mean square error (RMSE) between simulated and observed yield from the variety trials was adopted as the final values for the candidate cultivar. The accuracy of the cultivar coefficients was evaluated using data on plant growth, development, and yield that were collected during the 2005 growing season in a research station and two farmers' fields in southwest Georgia. Our results demonstrated that variety trial data in which no phenology data have been collected could be used successfully to derive cultivar coefficients for the CSM-CROPGRO-Peanut model. Simulated values of various development and growth parameters were in good agreement with their corresponding observed values for almost all parameters that were evaluated. At final harvest, the RMSE between observed and simulated yield components was 259 kg ha-1 for seed yield, 301 kg ha-1 for pod yield, and 608 kg ha-1 for aboveground biomass. The calibration procedure described in this article could facilitate the estimation of cultivar coefficients for new peanut cultivars if local variety trial data are available.

Gamma oryzanol is an important phytochemical of black glutinous rice as it has positive effects on human health. The objective of this study was to evaluate stability of gamma oryzanol content and grain yield for black glutinous rice genotypes grown under different environments. Twenty-five black glutinous rice genotypes and 2 white glutinous rice genotypes were evaluated during 2011 and 2012 in 8 farmers’ fields of Thailand. The experimental trials were laid out in a randomized complete blocks design (RCBD) with 3 replications. Soil data before growing, weather data during growing season, yield components, grain yield and gamma oryzanol content were recorded. The results indicated that location (L), genotype (G) and G×L interaction were highly significant for number of panicles per hill, number of filled grains per panicle, 1000-grain weight, grain yield, gamma oryzanol per grain weight and gamma oryzanol yield. The GGE biplot analysis indicated that KKU-GL-BL-06-023 (V17), Khaokam Gs.no. 88084 (V5) and KKU-GL-BL-11-001 (V25) had both high mean performance and stability for grain yield. The superior genotypes for gamma oryzanol per grain weight were KKU-GL-BL-11-001 (V25) and KKU-GL-BL-06-043 (V23). The desirable genotypes for gamma oryzanol yield were KKU-GL-BL-11-001 (V25), Niewdam Gs.no. 21427 (V3) and KKU-GL-BL-06-043 (V23).

Black rice is an alternative source of blast disease resistance and appropriate screening method is necessary tobe proposed. The objectives were to 1) evaluate leaf blast disease of different black glutinous rice genotypes,and 2) determine correlations between disease symptoms at seedling and tillering stages. Leaf blast disease for25 black glutinous rice genotypes and 3 check genotypes were observed at Khon Kaen University, Thailand in2013 and 2014. HY 71, Niawdam Gs.no.21629, Khaokam Gs.no.88084, KKU-GL-BL-05-003, KKU-GL-BL-05-004, KKU-GL-BL-06-010 and KKU-GL-BL-06-023 were high resistant genotypes across growth stages, timesof evaluation and years. Seedling and tillering stages were significantly different for severity index.Correlation coefficients between severity indices at seeding and tillering stages in 2013 and 2014 for evaluationat 7 days after inoculation (DAI) were 0.34 (p

The cropping system model, namely, the crop environment resource synthesis-rice (CSM-CERES-Rice) model, is a decision supporting tool for the design of crop management. This study aimed to determine management practices for increasing rice (Oryza sativa L.) production in Laos by using the CSM-CERES-Rice model. The model was evaluated with data sets from the TDK8 and TDK11 cultivars in farmers’ fields in the Vientiane plain in 2012. Anthesis and harvesting dates, growth and yield for various management scenario combinations (eight transplanting dates × two levels of plant densities × three rates of nitrogen (N) fertilizer application) for both cultivars were simulated by the model from 1980 to 2012. The model evaluation results showed strong agreement between simulated and observed data for days to harvest with a difference within four days. The model provided acceptable accuracy for grain yields with normalized root mean square error values ranging between 1 and 16 %. The results from the model application indicated that TDK8 and TDK11 produced similar yields. Transplanting TDK8 with two plant densities produced similar yields. The highest yield for both cultivars was achieved on the transplanting date of 15 Jan. N-fertilizer application at 60 and 120 kg N ha−1 was able to increase yield for TDK8 by 50 and 87 %, respectively, and for TDK11 by 54 and 70 %, respectively. Rice transplanted on 15 Jan with 5 seedlings hill−1 and N-fertilizer at 120 kg N ha−1 had the highest average yield for both cultivars with 6,460 and 6,351 kg ha−1 for TDK8 and TDK11, respectively. The CSM-CERES-Rice model is an alternative tool in determining crop management practices for rice production.

Drought is a major yield limiting factor in peanut production. Characterization of drought stress patterns of major production areas and information on crop responses to different levels of drought stresses are needed for the development of appropriate management strategies for individual locations. The CSM-CROPGRO-Peanut model could help in obtaining the required information, but its capability in predicting crop responses to different levels of drought stress needs to be firstly established. The objective of this study was to evaluate the capability of the CSM-CROPGRO-Peanut model in simulating the responses of two peanut cultivars to three levels of soil moisture regimes. The experiments were conducted under field conditions in the dry seasons of 2004 and 2005 at the Field Crop Research Station of Khon Kaen University in northeast Thailand. A split-plot in a randomized complete block design with 4 replications was used. Three levels of soil moisture (field capacity, 2/3 available water and 1/3 available water) were assigned to main-plots and two peanut cultivars (KK60-3 and Tainan 9) were arranged in sub-plots. Data collected on growth and development of the two peanut cultivars under the three soil moisture regimes were compared with the corresponding simulated data from model simulation using the CSM-CROPGRO-Peanut model. The results showed that the model performed fairly in simulating phonological development and patterns of dry matter accumulation but performed reasonably well in predicting the final biomass and pod yields of the two peanut cultivars under the three soil moisture regimes. The model, however, could predict the relative yield reductions from drought stress of the individual peanut cultivars quite accurately and could provide information on the time of occurrence and severity of water stress during the cropping period. These results indicate that the CSM-CROPGRO-Peanut model is sufficiently capable to be used in generating the required information for determining appropriate managements of drought stress

The assessment of yield stability of crop breeding lines requires data from multi-locations in order to be able to represent a sufficient number of different environments. In addition, the variation in yield of the test lines not only reflects the differential responses of the genotypes to different soil and climatic conditions, which are of primary concern to breeders, but also results from the effects of several other factors and the micro-environmental variability within the sites. Crop simulation models have the potential to assist in overcoming these difficulties. The objective of this investigation was to evaluate the potential application of the CROPGRO-Peanut model for yield stability assessment of advanced peanut lines. A group of 12 large-seeded Virginia type of peanut advanced breeding lines that was under multi-location evaluation at the regional yield trial stage in Thailand from 1998 to 1999 was used. These lines were tested in farmers’ fields and research stations in the northeastern and northern regions of Thailand during both the rainy and dry seasons of 1998–1999, totaling 10 different environments. The CROPGRO-Peanut model was used to simulate yield of the individual lines for the same environments in which they were tested. Both the observed and simulated yields were subjected to a stability analysis using a conventional regression model. The results showed that the CROPGRO-Peanut model predicted the relative mean pod yields over 10 test environments of the test peanut lines reasonably well; five out of the six highest yielding lines (top 50%) were identified by both experimentation and simulation. The model also gave estimates of the regression coefficients of individual genotype means over the site mean yields that were in good agreement with those obtained from actual testing. Some discrepancies were observed, but these were seen as providing additional information for the responses of the test genotypes to environmental factors that were not accounted for by the model. It was concluded that the CROPGRO-Peanut model could be a valuable tool for the evaluation of peanut breeding lines for yield stability.

Application of a crop simulation model as a plant breeding tool requires cultivar-specific coefficients for a large number of breeding lines which are commonly not available. These cultivar coefficients are normally estimated from field experiments conducted under optimum conditions over several environments, that include a combination of multiple planting dates or locations. However, it is difficult to adhere to this recommendation when many genotypes are involved, such as in plant breeding applications. The objective of this study was to derive genetic coefficients of two groups of advanced peanut breeding lines based on experimental data collected during two growing seasons. The two groups investigated included 12 lines of the large-seeded Virginia type and 14 lines of the small-seeded Spanish type which were under multi-location evaluation at the regional yield trial stage in Thailand in 1998–1999. These lines were grown during the early-rainy season of 1999 and the dry season of 2000 at Khon Kaen University (KKU) in Northeast Thailand. Data were collected on plant development and growth characteristics, soil characteristics, weather and management as required for determining genetic coefficients of a new peanut cultivar. The data were then used for model calibration, using a stepwise procedure to derive the 15 genetic coefficients of the individual lines and cultivars required for the CROPGRO-Peanut model. The results showed that the derived genetic coefficients provided simulated values of various development and growth parameters that were in good agreement with their corresponding observed values for almost all parameters. Model evaluation with an independent data set of four peanut lines grown during the rainy and dry seasons of 2001 and 2002 gave similar results. The differences among breeding lines were also expressed through differences in the derived genetic coefficients. These results suggest that the genetic coefficients of peanut lines obtained from detailed experimental data for two distinct seasons are sufficiently accurate for further evaluation of the CROPGRO-Peanut model in breeding applications.

Physiological traits responsible for differences in yield performance among pre-released genotypes are normally not known. The objective of the present study was to evaluate seasonal variations in dynamic growth and development traits between 12 large-seeded Virginia-type and 14 small-seeded Spanish-type advanced breeding lines of peanut (Arachis hypogaea L.). The experiment was conducted in 1999 and 2000 at Khon Kaen University in Northeast Thailand for three planting dates, representing the early-rainy season, mid-rainy season and dry season. Very similar phenological development was observed for all lines within each group and between the early and the mid-rainy seasons. However, a lower temperature during the early growth stage in the dry season delayed flower initiation and extended maturity. During this period, crop growth rates as well as the development rate for leaf area index and specific leaf area were also lower compared with the rainy seasons. On average, both pod yield and total biomass for the rainy season and the dry season were about the same. Variations among lines within each group were also small. For these advanced breeding lines, the pod growth rate was the most important yield determinant while the crop growth rate had lesser effect and the partitioning coefficient and pod-filling duration had no significant effect. However, the crop growth rate, pod growth rate and partitioning coefficient were important traits for a line to be the top yielder. Pod-filling duration was also important when the lines involved were considerably diverse in maturity and seed size. Information on these traits, if used together with final pod yield, will make varietal selection more efficient.

Jerusalem artichoke could be an alternative feedstock for bioenergy during times when there are shortages of other raw materials for the ethanol industry. However, insufficient water under rainfed conditions is a major cause of Jerusalem artichoke losses. Genetic variation for drought tolerance is an essential prerequisite for the development of Jerusalem artichoke cultivars with improved drought tolerance. The objectives of this study were to determine the effects of drought stress on tuber dry weight and biomass and to investigate the genotypic variability in Jerusalem artichoke germplasm. The line-source sprinkler technique was used to compare moisture responses of a range of 40 Jerusalem artichoke genotypes grown using 3 water levels. Experiments were conducted on a Yasothon soil series in Northeast Thailand during 2010/11 and 2011/12 and included extended dry periods. Drought reduced tuber dry weight and biomass, and the reductions in tuber dry weight and biomass were greater under severe drought than moderate drought conditions. Over both seasons, CN 52867, HEL 53, HEL 231, HEL 335, JA 76, HEL 65, and JA 102 × JA 89 (8) had consistently high tuber dry weight (1.3 to 4.5 t ha -1 ) and HEL 53, HEL 61, HEL 231, HEL 335, JA 76, JA 15, JA 89, HEL 65, HEL 256, and JA 102 × JA 89 (8) had consistently high biomass (2.0 to 6.8 t ha -1 ). These Jerusalem artichoke genotypes are promising parents in breeding for drought tolerance.