A global sensitivity analysis of cultivar trait parameters in a sugarcane growth model for contrasting production environments in Queensland,Australia

New sugarcane cultivars are continuously developed to improve sugar industry productivity. Despite this sugarcane crop models such as the ‘Sugar’ module in the Agricultural Productions System sIMulator (APSIM-Sugar) have not been updated to reflect the most recent cultivars. The implications of misrepresenting cultivar parameters in APSIM-Sugar is difficult to judge as little research has been published on the likely values of these parameters and how uncertainty in parameter values may affect model outputs. A global sensitivity analysis can be used to better understand how cultivar parameters influence simulated yields. A Gaussian emulator was used to perform a global sensitivity analysis on simulated biomass and sucrose yield at harvest for two contrasting sugarcane-growing regions in Queensland, Australia. Biomass and sucrose yields were simulated for 42 years to identify inter-annual variability in output sensitivities to 10 parameters that represent physiological traits and can be used to simulated differences between sugarcane cultivars. Parameter main effect (S_i) and total effect (ST_i) sensitivity indices and emulator accuracy were calculated for all year-region-output combinations. When both regions were considered together parameters representing radiation use efficiency (rue), number of green leaves (green_leaf_no) and a conductance surrogate parameter (k_L) were the most influential parameters for simulated biomass in APSIM-Sugar. Simulated sucrose yield was most sensitive to rue, sucrose_fraction (representing the fraction of biomass partitioned as sucrose in the stem) and green_leaf_no. However, climate and soil differences between regions changed the level of influence cultivar parameters had on simulation outputs. Specifically, model outputs were more sensitive to changes in the transp_eff_cf and k_L parameters in the Burdekin region due to lower rainfall and poor simulated soil conditions. Collecting data on influential traits that are relatively simple to measure (e.g. number of green leaves) during cultivar development would greatly contribute to the simulation of new cultivars in crop models. Influential parameters that are difficult to measure directly such as transp_eff_cf and sucrose_fraction are ideal candidates for statistical calibration. Calibrating crop models either through direct observation or statistical calibration would allow crop modellers to better test how new cultivars will perform in a range of production environments.     

Assessment of uncertainty and sensitivity analyses for ORYZA model under different ranges of parameter variation

We explore the effects of different ranges of parameter variation (RPV) on sensitivity and uncertainty analyses for ORYZA_V3 model. In this study, a latin hypercube sampling (LHS) technique is used to generate parameter sample sets, and a regression-based method is employed for the sensitivity analysis on 16 crop parameters. Then, a top-down concordance coefficient (TDCC) is calculated to assess the stability of parameter sensitivity rankings across diverse RPV. Furthermore, coefficients of variation (CV) and 90% confidence intervals (90CI) of daily model outputs are analyzed by considering uncertainty in observations. We find that the increasing RPV multiplies the CV of daily model outputs, whereas the RPV has no effect on the CV’s change rule over time. The 90CI of model outputs include most of the observations when the RPV is more than ±30% perturbation. The standardized regression coefficient (SRC) of some parameters are obviously minified when the RPV is ±5% or ±50% perturbation. The results highlights the importance of RPV selection in the sensitivity and uncertainty analysis of crop model, and ±30% perturbation was suggested when the RPV cannot be specifically obtained.     

Stability of performance in lentil (Lens culinaris Medik) genotypes in Iran

The development of genotypes, which can be adapted to a wide range of diversified environment, is the ultimate goal of plant breeders in a crop improvement program. In this study, several stability methods were used to evaluate the genotype by environment interaction (GE) in 11 lentil (Lens culinaris Medik) genotypes. The genotypes were evaluated for grain yield at 4 different locations for 3 years in semi arid areas of Iran. The testing locations have different climatic and edaphic conditions providing the conditions necessary for the assessment of stability. A combined analysis of variance, stability statistics, rank correlations among stability statistics and yield stability statistic were determined. Significant differences were detected between genotypes and their GE interactions. Different univariate stability parameters were used to determine stability of the studied genotypes. The level of association among the parameters was assessed using Spearman's rank correlation. The different stability statistics which measured the different aspects of stability was substantiated by rank correlation coefficient. Rank-correlation coefficients between yield and some stability parameters were highly significant. Genotypes mean yield (Mean) was significantly correlated to the Lin and Binns stability parameter PI (r = 0.93^{* *}í) and desirability index Di (r = 0.89^{* *}í). A principal component analysis based on rank correlation matrix was performed for grouping the different stability parameters studied. In conclusion, based on most stability parameters, the genotypes G2, G5 and G9 were found to be the most stable. Results from rank correlation and principal component analysis showed that the stability variance (σ_i ²) was strongly correlated with Wricke's ecovalance, stability parameters of Plaisted and Peterson, and Plaisted.     

Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran

The genotype × environment (GE) interaction influences genotype selection and recommendations. Consequently, the objectives of genetic improvement should include obtaining genotypes with high potential yield and stability in unpredictable conditions. The GE interaction and genetic improvement for grain yield and yield stability was studied for 11 durum breeding lines, selected from Iran/ICARDA joint program, and compared to current checks (i.e., one durum modern cultivar and two durum and bread wheat landraces). The genotypes were grown in three rainfed research stations, representative of major rainfed durum wheat-growing areas, during 2005–09 cropping seasons in Iran. The additive main effect and multiplicative interaction (AMMI) analysis, genotype plus GE (GGE) biplot analysis, joint regression analysis (JRA) (b and S²di), six stability parameters derived from AMMI model, two Kang’s parameters [i.e., yield-stability (YSi) statistic and rank-sum], GGE distance (mean performance + stability evaluation), and two adaptability parameters [i.e., TOP (proportion of environments in which a genotype ranked in the top third) and percentage of adaptability (Ad)] were used to analyze GE interaction in rainfed durum multi-environment trials data. The main objectives were to (i) evaluate changes in adaptation and yield stability of the durum breeding lines compared to modern cultivar and landraces (ii) document genetic improvement in grain yield and analyze associated changes in yield stability of breeding lines compared to checks and (iii) to analyze rank correlation among GGE biplot, AMMI analysis and JRA in ranking of genotypes for yield, stability and yield-stability. The results showed that the effects due to environments, genotypes and GE interaction were significant (P < 0.01), suggesting differential responses of the genotypes and the need for stability analysis. The overall yield was 2,270 kg ha^?1 for breeding lines and modern cultivar versus 2,041 kg ha^?1 for landraces representing 11.2 % increase in yield. Positive genetic gains for grain yield in warm and moderate locations compared to cold location suggests continuing the evaluation of the breeding material in warm and moderate conditions. According to Spearman’s rank correlation analysis, two types of associations were found between the stability parameters: the first type included the AMMI stability parameters and joint regression parameters which were related to static stability and ranked the genotypes in similar fashion, whereas the second type consisted of the rank-sum, YSi, TOP, Ad and GGED which are related to dynamic concept of stability. Rank correlations among statistical methods for: (i) stability ranged between 0.27 and 0.97 (P < 0.01), was the least between AMMI and GGE biplot, and highest for AMMI and JRA and (ii) yield-stability varied from 0.22 (between GGE and JRA) to 0.44 (between JRA and AMMI). Breeding lines G8 (Stj3//Bcr/Lks4), G10 (Ossl-1/Stj-5) and G12 (modern cultivar) were the best genotypes in terms of both nominal yield and stability, indicating that selecting for improved yield potential may increase yield in a wide range of environments. The increase in adaptation, yield potential and stability of breeding lines has been reached due to gradual accumulation of favorable genes through targeted crosses, robust shuttle breeding and multi-locational testing.     

秩次分析法对安徽花生产量性状评价研究

为筛选适合安徽省种植的花生品种,利用秩次分析法对9个环境点、22个参试品种的产量表现进行数据分析,在方差分析基础上,对各品种的秩次值(H2)、环境区分指数(YM)、秩次均方(S2)等统计数进行计算,从品种的高产性和稳产性2个方面进行比较。结果表明,在参试的22个品种(系)中4个品种(系)具有较好的高产性和稳产性,秩次分析法能够较为客观准确地评价参试品种的产量表现,具有较高的应用价值。     

作物产量“三合结构”定量表达及高产分析

针对目前作物产量水平长期徘徊难以突破，产量分析理论缺乏量化指标体系，可操作指导作用小等问题，依据“三合结构”模式二级结构层各因素的关系，建立了“三合结构”定量表达式，并通过田间试验与模型模拟相结合的方法，对春玉米、夏玉米、水稻和冬小麦高产实例进行定量化分析，明确了限制产量进一步提高的关键因素，提出了高产突破的可能方向。结果表明，提高叶片平均净同化率（MNAR），改善群体的物质生产能力，是水稻产量进一步提升的关键；适当提高平均叶面积指数（MLAI）或经济系数（HI）可能会进一步增加冬小麦产量；春玉米籽粒产量主要伴随着MLAI和单位面积穗数（EN）的增加而提高，其实质是平均作物生长率（MCGR）的提高增加了单位面积上总粒数（TGN）。进一步研究确定了“三合结构”定量表达式参数间的函数关系式，通过公式代换可推导出某一参数与目标参数的函数关系。作物产量“三合结构”定量表达式的建立为作物群体定量化研究提供了新的思路和方法，有助于全面掌握群体参数变化与产量形成的定量关系，为指导作物生产进行有效的技术调控提供依据。     

Performance and sensitivity of the DSSAT crop growth model in simulating maize yield under conservation agriculture

With the practice of conservation agriculture (CA) soil water and nutrient dynamics are modified by the presence of a mulch of crop residues and by reduced or no-tillage. These alterations may have impacts on crop yields. The crop growth model DSSAT (Decision Support Systems for Agrotechnology Transfer) has recently been modified and used to simulate these impacts on crop growth and yield. In this study, we applied DSSAT to a long-term experiment with maize (Zea mays L.) grown under contrasting tillage and residue management practices in Monze, Southern Province of Zambia. The aim was (1) to assess the capability of DSSAT in simulating crop responses to mulching and no-tillage, and (2) to understand the sensitivity of DSSAT model output to input parameters, with special attention to the determinants of the model response to the practice of CA. The model was first parameterized and calibrated for the tillage treatment (CP) of the experiment, and then run for the CA treatment by removing tillage and applying a mulch of crop residues in the model. In order to reproduce observed maize yields under the CP versus CA treatment, optimal root development in the model was restricted to the upper 22 cm soil layer in the CP treatment, while roots could optimally develop to 100 cm depth under CA. The normalized RMSE values between observed and simulated maize phenology and total above ground biomass and grain yield indicated that the CA treatment was equally well simulated as the CP treatment, for which the model was calibrated. A global sensitivity analysis using co-inertia analysis was performed to describe the DSSAT model response to 32 model input parameters and crop management factors. Phenological cultivar parameters were the most influential model parameters. This analysis also demonstrated that in DSSAT mulching primarily affects the surface soil organic carbon content and secondly the total soil moisture content, since it is negatively correlated with simulated soil water evaporation and run-off. The correlations between the input parameters or crop management factors and the output variables were stable over a wide range of seasonal rainfall conditions. A local sensitivity analysis of simulated maize yield to three key parameters for the simulation of the CA practice revealed that DSSAT responds to mulching particularly when rooting depth is restricted, i.e., when water is a critical limiting crop growth factor. The results of this study demonstrate that DSSAT can be used to simulate crop responses to CA, in particular through simulated mulching effects on the soil water balance, but other, often site-specific, factors that are not modeled by DSSAT, such as plough pan formation under CP or improved soil structure under CA, may need to be considered in the model parameterization to reproduce the observed crop yield effects of CA versus CP.     

Analyzing the effects of climate variability on spatial pattern of yield in a maize–wheat–soybean rotation

The identification of homogeneous management zones within a field is crucial for variable rate application of agronomic inputs. This study proposed a methodology to identify homogeneous management zones within a 8 ha field, based on the stability of measured and simulated yield patterns in a maize–soybean–wheat crop rotation in north-east Italy. Crop growth and yield were simulated over a 14-year period (1989–2002) using CERES-Maize, CROPGRO-Soybean and CERES-Wheat models to account for weather effects on yield spatial patterns. The overlay of long-term assessments of yield spatial and temporal data allowed for the identification of two stable zones with different yield levels, one with greater yield (called HS for high and stable yield) and one with lower yield (called LS for low and stable yield). The size of the HS zone identified using 14 years of simulated yield was smaller than the one obtained when considering only yield monitor data taken during the 5-year crop rotation. The LS zone was larger when using simulated data, confirming that the consistency of temporal stability increased by increasing the years considered. The models were able to closely simulate yield across the field when site-specific inputs were used, showing potential for use in yield map interpretation in the context of precision agriculture. Results showed that a combination of GIS tools and crop growth simulation models can be used to identify temporally stable zones, which is a fundamental prerequisite for adopting variable rate technologies.     

Yield variation of spring cereals in relation to selected soil physical properties on three clay soil fields

The crop growth is highly dependent on growth conditions which vary from year to year making precision farming challenging. In the present paper was first investigated whether varying soil physical properties reflect the within-field yield variation of small grain cereals and how do the variations in weather conditions between growing seasons affect the within-field yield variation. Secondly, the potential biomass accumulation of the crop in existing soil and weather conditions was simulated. The simulated and experimentally based site-specific total biomasses were compared in order to find out whether the soil data explains the observed variations in yield.Three experimental fields size of 3–4 ha were established to examine the spatio-temporal yield variation during three years. The clay content of soils was high (> 46%) and soils were classified as Stagni-Vertic Cambisols. Correlations between soil water retention properties and crop yield were studied. Top and subsoil saturated (SWC), field capacity (FC) and permanent wilting point (PWP) water content, and saturated hydraulic conductivity of soil (K_sat), were determined from 19 to 24 places on each field once during the three years experimental period. During growing seasons, soil moisture content and leaf area index (LAI) were determined at same places biweekly, and yield was harvested. Spring barley (Hordeum vulgare) was grown on two fields, and spring wheat (Triticum aestivum, 2 years) and spring oilseed rape (Brassica napus L., one year) were grown on the third field.The measured grain yields correlated with selected soil physical properties only in few cases. The observed spatial variation in the biomass was in most cases found to be higher than the simulated. Therefore, the above mentioned parameters were not enough to predict the yield correctly in case of high variations. There were other factors decreasing the observed yield e.g. lodging, cold summer, extremely high precipitation and slopes in field. According to our results it is evident that it is very difficult to predict site-specific biomass accumulation solely by soil properties in order, for instance, to fertilize in a site-specific manner. Therefore one needs to measure the crop during the growing season in order to simulate the biomass accumulation for precision farming purposes.     

A model-based approach to assist variety evaluation in sunflower crop

Assessing the performance and the characteristics (e.g. yield, quality, disease resistance, abiotic stress tolerance) of new varieties is a key component of crop performance improvement. However, the variety testing process is presently exclusively based on experimental field approaches which inherently reduces the number and the diversity of experienced combinations of varieties × environmental conditions in regard of the multiplicity of growing conditions within the cultivation area. Our aim is to make a greater and faster use of the information issuing from these trials using crop modeling and simulation to amplify the environmental and agronomic conditions in which the new varieties are tested.In this study, we present a model-based approach to assist variety testing and implement this approach on sunflower crop, using the SUNFLO simulation model and a subset of 80 trials from a large multi-environment trial (MET) conducted each year by agricultural extension services to compare newly released sunflower hybrids. After estimating parameter values (using plant phenotyping) to account for new genetic material, we independently evaluated the model prediction capacity on the MET (relative RMSE for oil yield was 16.4%; model accuracy was 54.4%) and its capacity to rank commercial hybrids for performance level (relative RMSE was 11%; Kendall's τ = 0.41, P < 0.01). We then designed a numerical experiment by combining the previously tested genetic and new cropping conditions (2100 virtual trials) to determine the best varieties and related management in representative French production regions. Finally, we proceeded to optimize the variety-environment-management choice: growing different varieties according to cultivation areas was a better strategy than relying on the global adaptation of varieties. We suggest that this approach could find operational outcomes to recommend varieties according to environment types. Such spatial management of genetic resources could potentially improve crop performance by reducing the genotype–phenotype mismatch in farming environments.     

AquaCrop模型在东北黑土区作物产量预测中的应用研究

东北黑土区是我国玉米和大豆生产基地,为了实现利用AquaCrop模型优化管理和预测产量,本文基于作物小区田间试验和大田观测数据,采用OAT(one factor at a time)法分析了该模型参数的敏感性,率定了敏感性高的参数,并对率定后的模型进行了验证。结果表明:玉米和大豆产量均对影响经济产量的收获指数十分敏感,二者虽然对冠层和根系生长参数都敏感,但有所差异:玉米对冠层衰减系数(canopy decline coefficient,CDC)更为敏感,而大豆则对限制冠层伸展的水分胁迫系数曲线的形状因子(shape factor for water stress coefficient for canopy expansion,Pexshp)更为敏感;玉米因根系深对最大有效根深(maximum effective rooting depth,Zx)更敏感,大豆因根系浅对根区根系伸展曲线的形状因子(shape factor describing root zone expansion,Rexshp)更敏感。由于玉米需水量大,对冠层形成和枯萎前的作物系数(crop coefficient before canopy formation and senescence,KcTr,x)和归一化水分生产力(normalized water productivity,WP*)很敏感,大豆则是一般敏感。率定后模型模拟玉米产量与实测产量的回归系数由0.34提升至0.89,模拟大豆产量与实测产量的回归系数由0.80提升至0.88。进一步用大田实测产量的验证结果表明:预测的玉米与大豆产量与实测产量间回归方程的决定系数(coefficient of determination,R2)分别为0.775和0.779,均方根误差(root mean square error,RMSE)分别为1.076 t hm^–2和0.299 t hm^–2,标准均方根误差(normalized root mean square error,NRMSE)分别为0.097和0.178,模拟效率(model efficiency,ME)分别为0.747和0.730,率定后的AquaCrop模型能较精准地模拟东北黑土区玉米和大豆产量,可用于产量预测或优化管理。     

Growth and yield of winter wheat in the first 3 years of a monoculture under varying N fertilization in NW Germany

Different preceding crops interact with almost all husbandry and have a major effect on crop yields. In order to quantify the yield response of winter wheat, a field trial with different preceding crop combinations (oilseed rape (OSR)–OSR–OSR–wheat–wheat–wheat), two sowing dates (mid/end of September, mid/end of October) and 16 mineral nitrogen (N) treatments (80–320 kg N ha⁻¹) during 1993/1994–1998/1999, was carried out at Hohenschulen Experimental Station near Kiel in NW Germany. Single plant biomass, tiller numbers m⁻², biomass m⁻², grain yield and yield components at harvest were investigated. During the growing season, the incidence of root rot (Gaeumannomyces graminis) was observed. Additionally, a bioassay with Lemna minor was used to identify the presence of allelochemicals in the soil after different preceding crops.Averaged over all years and all other treatments, wheat following OSR achieved nearly 9.5 t ha⁻¹, whereas the second wheat crop following wheat yielded about 0.9 t ha⁻¹ and the third wheat crop following 2 years of wheat about 1.9 t ha⁻¹ less compared with wheat after OSR. A delay of the sowing date only marginally decreased grain yield by 0.2 t ha⁻¹. Nitrogen fertilization increased grain yield after all preceding crop combinations, but at different levels. Wheat grown after OSR reached its maximum yield of 9.7 t ha⁻¹ with 210 kg N ha⁻¹. The third wheat crop required a N amount of 270 kg N ha⁻¹ to achieve its yield maximum of 8.0 t ha⁻¹.Yield losses were mainly caused by a lower ear density and a reduced thousand grain weight. About 4 weeks after plant establishment, single wheat plants following OSR accumulated more biomass compared to plants grown after wheat. Plants from the third wheat crop were smallest. This range of the preceding crop combinations was similar at all sampling dates throughout the growing season.Root rot occurred only at a low level and was excluded to cause the yield losses. The Lemna bioassay suggested the presence of allelochemicals, which might have been one reason for the poor single plant development in autumn.An increased N fertilization compensated for the lower number of ears m⁻² and partly reduced the yield losses due to the unfavorable preceding crop combination. However, it was not possible to completely compensate for the detrimental influences of an unfavorable preceding crop on the grain yield of the subsequent wheat crop.     

基于python语言的冬小麦品种稳定性分析

旨在挑选出适宜的冬小麦品种稳定性分析参数及其计算方法。利用冬小麦实验数据,首次通过python语言计算了5个稳定性参数,并采用秩相关性和主成分分析,研究了参数的相关性。结果表明,python语言可以方便、灵活的计算各类参数,计算相关性和进行主成分分析。相关分析表明,PCOA和产量显著正相关,CV和Bi和产量极显著负相关。研究表明,PCOA模型能够同时评价品种产量及其稳定性。多元统计方法是单变量参数模型的有益补充。     

Impact of Nitrogen and Plant Growth Promoting Rhizobacteria on yield and yield components of sunflower in a glasshouse environment

The effects of Nitrogen (N) and Plant Growth Promoting Rhizobacteria (PGPR) on growth and development of sunflower (Helianthus annuus L. var. Hysun-33) grown in the greenhouse under a natural environment were studied. The N-use efficiency of a sunflower crop grown under three N rates (N₁ = 0 kg ha^?1, N₂ = 120 kg ha^?1, and N₃ = 240 kg ha^?1) and three PGPR levels (R₁ = 0 kg ha^?1, R₂ = 30 kg ha^?1, and R₃ = 60 kg ha^?1) were investigated. The maximum amount of N resulted in higher total dry matter production per plant and the effect was prominent from 34 days after sowing (DAS). Seed yields differed significantly among different sunflower crops especially at limiting N supply, with significant shifts according to the N level. N uptake was an important parameter for yield at all N rates. The 240 kg N ha^?1 treatments provided the maximum yield, while the oil contents in these treatments of higher yield showed a lower oil content (%). Harvest index was also significantly correlated to yield across N rates; however, its importance depended much on environmental conditions as well. It can be inferred from the study that sunflower crop is well-supplied with respect to growth, development, yield and yield components, to enhance N efficiency and depends very much on the N supply. All the parameters gave maximum results with the increment of N while PGPR regimes had no prominent impact on the sunflower crop, the target environment, and the target yield level grown under a specified controlled glasshouse environment.     

Mapping crop diseases using survey data: The case of bacterial wilt in bananas in the East African highlands

Globally, crop diseases result in significant losses in crop yields. To properly target interventions to control crop diseases, it is important to map diseases at a high resolution. However, many surveys of crop diseases pose challenges to mapping because available observations are only proxies of the actual disease, observations often are not normally distributed and because typically convenience sampling is applied, leading to spatially clustered observations and large areas without observations. This paper addresses these challenges by applying a geostatistical methodology for disease incidence mapping. The methodology is illustrated for the case of bacterial wilt of banana (BWB) caused by Xanthomonas campestris pv. musacearum in the East African highlands. In a survey using convenience sampling, 1350 banana producing farmers were asked to estimate the percentage yield loss due to bacterial wilt. To deal with the non-normal distribution of the data, the percentages were classified into two binary variables, indicating whether or not the disease occurred and whether or not the yield loss was severe. To improve the spatial prediction of disease incidence in areas with low sampling density, the target variables were correlated in a logistic regression to a range of environmental variables, for which maps were available. Subsequently, the residuals of the regression analysis were interpolated using simple kriging. Finally, the interpolated residuals were added to the regression predictions. This so-called indicator regression kriging approach resulted in continuous maps of disease incidence. Cross-validation showed that the method yields unbiased predictions and correctly assesses the prediction accuracy. The geostatistical mapping is also more accurate than conventional mapping, which uses the mean of observations within districts as the predicted value for all locations within the district, although the accuracy improvement is not very large. The maps were also spatially aggregated to district level to support regional decision-making. The analysis showed that the disease is widespread on banana farms throughout the study area and can locally reach severe levels.     

AquaCrop模型在东北黑土区作物产量预测中的应用研究

Northeast black soil area is the production area of maize and soybean in China. In order to optimize the agricultural management and forecast crop yield with AquaCrop model, we use OAT (one factor at a time) method to analyze the sensitivity of the model parameters based on the experiment and field observation data, and to validate the model after calibrated the high sensitivity parameters. The results of sensitivity analysis showed that the yields of maize and soybean were both extremely sensitive to the reference harvest index (HI₀) and the parameters of canopy growth and root growth. The difference was that maize was more sensitive to the canopy decline coefficient (CDC), while soybean was more sensitive to the shape factor for water stress coefficient for canopy expansion (Pexshp). Maize was more sensitive to the maximum effective rooting depth (Z_x) because of its deep root, while soybean was more sensitive to the shape factor describing root zone expansion (Rexshp) because of its short roots. Maize was extremely sensitive to the crop coefficient before canopy formation and senescence (Kc_Tr,x) and the normalized water productivity (WP^*) due to the large water demand, while soybean was only generally sensitive. After calibrated the high sensitivity parameters with experiment data, the regression coefficient of simulated yield and measured yield of maize increased from 0.34 to 0.89, and the regression coefficient of simulated yield and measured yield of soybean increased from 0.80 to 0.88. Furthermore, the validation results of field observation data indicated that the determination coefficients (R²), the root mean square error (RMSE), the normalized root mean square error (NRMSE) and the model efficiency (ME) of the AquaCrop model of maize and soybean were 0.775 and 0.779, 1.076 t hm^-2 and 0.299 t hm^-2, 0.097 and 0.178, 0.747 and 0.730, respectively. The calibrated AquaCrop model can accurately simulate the yield of corn and soybean in the black soil area of Northeast China, and is useful for yield prediction and optimal management.     

Variation in Rate and Duration of Growth Among Spring Barley Cultivars1

Ten two-rowed spring barley cultivars (Hordeum vulgare L.) were evaluated for growth parameters, i.e. crop growth rate, crop growth duration, grain filling rate, grain filling duration, vegetative growth rate, vegetative growth duration, single caryopsis filling rate, single caryopsis filling duration. Field studies were conducted on a sandy loam at Hohenschulen, Northern Germany with three levels of nitrogen fertilization and three sowing rates in 1986 to 1988. Cultivar effects were observed for all growth parameters except for crop growth rate and vegetative growth rate. But only crop growth duration n and grain filling duration showed positive correlations with grain yield. No growth rate parameter was related to yield. Biomass was correlated to crop growth duration and not to crop growth rate, while average caryopsis weight was strongly related to caryopsis filling rate and only moderately to caryopsis filling duration. Comparing grain filling rate and duration to individual caryopsis filling rate and duration, only grain filling rate and duration appeared to be relevant to grain yield. Since genetic variability for crop growth rate was lacking in the spring barley material tested, further improvement of yield would only result from increase in harvest index and/or longer crop growth duration.     

A model for light competition between vegetable crops and weeds

A precise prediction of the yield losses inflicted by weeds is the basis of decisions in weed management. Hitherto, only rough estimates, which neglect the specific production situation, have been available for vegetable crops. In this study a simple simulation model was developed to estimate yield loss by radiation competition as a function of environmental variables. In the model, the distribution of incoming photosynthetically active radiation (PAR) in the canopy is calculated using a spatially highly resolved approach. Growth is calculated as a function of absorbed radiation and its utilisation. Newly produced dry matter is allocated to roots and shoots, the latter comprising vegetative and reproductive organs according to the developmental stage. Vegetative shoot dry matter is partitioned according to the main functions of radiation interception (leaves) and structural stability (stems and petioles). The resulting leaf area is distributed in the canopy according to the spatial expansion of individual plants. Calibration runs revealed uncertainties predicting the growth of Chenopodium album and a high sensitivity of crop yield to leaf area development of the weed. Using the area of green leaves (LAI) of C. album as input gave a close correspondence between simulated and observed crop yield loss. Since plant height of C. album is calculated as a function of leaf area, this variable has a multiple effect on radiation absorption. A first evaluation with an independent data set likewise gave an acceptable prediction. To reduce model complexity, a simplified version is proposed.     

Breeding beans for resistance to terminal drought in the Lowland tropics

In the lowland regions of Latin America, a large proportion of beans are sown at the beginning of a dry season where a guaranteed terminal (end-of-season) drought will reduce yields. This study was undertaken to identify lines within two black bean recombinant inbred line (RIL) populations with resistance to terminal drought. The two RIL populations were developed from crosses between a drought resistant line, B98311 from Michigan, with TLP 19 and VAX 5, two lines from CIAT with improved disease resistance and adaptation to growing conditions in Latin America. The RIL populations were evaluated in experiments conducted in Zamorano, Honduras and Veracruz, Mexico under drought stress and well-watered (non-stress) treatments. Yields were reduced in each experiment by drought and the fungal pathogen, Macrophomina phaseolina. Drought stress, disease pressure and low yields contributed to high coefficients of variation (CV), which made it difficult to select superior lines. Selection was based on rank of geometric mean (GM) yield calculated from the yield in the stress and non-stress treatments. One RIL, L88-63 ranked first in GM yield at both locations. Subsequent testing in Honduras and Michigan confirmed the high yield potential and broad adaptation of L88-63. Breeding beans for drought resistance in lowland tropical environments should also include breeding for resistance to M. phaseolina. This revised version was published online in July 2006 with corrections to the Cover Date.     

A mathematical model for the evaluation of cooperation and competition effects in intercrops

Experimental results showing cooperation–competition interactions in dual-component crops for mixtures created from Pisum sativum L. (pea), Vicia sativa L. (spring vetch) and Linum usitatisimum L. (linseed) are studied by means of a mathematical model describing the plant interactions. The model introduces parameters enabling to distinguish competition and cooperation between the species in the crop. The model parameters are established on the basis of the entire vegetation period of plants, so they provide exhaustive characteristics of the plant interactions in the mixtures. The model factors allow the estimation of the mixtures with respect to the final biomass yield as well. The experimental data verify possible economic benefits from the proposed plant mixtures and allow to critically check the beliefs that legumes improve the field productivity. Additionally, for comparison with the biomass yield, the seed yield is analyzed in the respective crops. Especially, it is indicated that the increased biomass yield for linseed in the mixture with a leguminous plant is accompanied with a decrease of the seed yield for this species. As concerns pea in the mixture with linseed, a decrease in the pea biomass was registered when compared to the sole crop but at the same time an increase in the seed yield was achieved in the mixture. No influence on the biomass of the spring vetch was noticed when it was cultivated with linseed but its seed yield appeared to be diminished with respect to the sole crop.