Increased utilization of lengthening growing season and warming temperatures by adjusting sowing dates and cultivar selection for spring maize in Northeast China

Global warming has lengthened the theoretical growing season of spring maize in Northeast China (NEC), and the temperatures during the growing season have increased. In practise, crop producers adjust sowing dates and alternate crop cultivars to take advantage of the lengthening growing season and increasing temperatures. In this study, we used crop data and daily weather data for 1981–2007 at five locations in NEC to quantify the utilization of the lengthening growing season and increasing temperatures by adjusting sowing dates and cultivar selection for spring maize production. If these two positive factors are not fully utilized, then it is important to know the potential impacts of these climatic trends on spring maize grain yields. The results show that in NEC, both the actual and theoretical growing seasons are lengthening, i.e., the sowing dates have been advanced and the maturity dates have been delayed. The actual sowing dates are 1–8 days later and the actual maturity dates are 6–22 days earlier than the theoretical perspective. Advancing sowing dates and changing cultivars led to 0–5 days and 6–26 days extension of the growing season. For the potential thermal time (TT), adjusting the sowing dates decreased the unutilized TT before sowing, while the cultivar selection increased the utilized TT and decreased the unutilized TT after maturity. On average, the unutilized heating resource before sowing is less than that after the maturity date (0.3–1.9% vs. 2.1–7.8%). During 1981–2007, for per day extension of the growing season, the spring maize grain yield increased by 75.2 kg ha⁻¹. The spring maize grain yields have increased by 7.1–57.2% when both early sowing and changing cultivars during 1981–2007. In particular, adjusting the sowing dates increased the grain yield by 1.1–7.3%, which was far less than the increase effect (6.5–43.7%) from switching to late maturing cultivars. Therefore, selecting late maturing cultivars is an important technique to improve maize grain yields in NEC under the global warming context. Nevertheless, if the currently unutilized TT were fully explored, the local spring maize grain yield would have increased by 12.0–38.4%.     

Selection for high spike fertility index increases genetic progress in grain yield and stability in bread wheat

Spike fertility index (SF) has been proposed as a promising trait to be used as a selection criterion in wheat breeding programs aimed at increasing grain yield, but no actual evidence of its successful application has been reported. In this study, 146 recombinant inbred lines derived from a cross between ‘Baguette 10’ and ‘Klein Chajá’, Argentinean spring bread wheat cultivars with contrasting SF, were evaluated during three crop seasons (2013, 2014 and 2015) at Balcarce, Argentina. Grain yield, grain number/m², grain weight, and SF were measured at maturity. Changes in grain yield (i.e., responses to selection) after application of different selection strategies, including different selection criteria and selection intensities, were determined. Significant correlations were observed between grain number and grain yield, SF and grain yield, and SF and grain weight. Analysis of SF variance components showed a significant genotype?×?environment interaction, but it represented only 9% of the total variation, whereas 51% of the variation was genetic, resulting in a high narrow-sense heritability (0.84). The use of SF as a selection criterion, either solely or in combination with selection for high yield, increased yield, resulting in higher and more stable yields than if selecting for high yield alone. Our findings support the use of spike fertility index as a selection criterion for increasing genetic progress and stability of yield in bread wheat breeding programs.     

Genotypic variation for competitive ability in spring wheat

Herbicides are the primary method of weed control for crop production in developed countries. For economic and environmental reasons alternative control strategies are being devised. One of these strategies is the development of competitive crop cultivars. The objectives of this research were to establish whether spring wheat (Triticum aestivum L.) genotypes differed in competitive ability and if those differences were related to specific growth characteristics. Sixteen genotypes of spring wheat were grown under simulated weed competition conditions at Saskatoon, Canada over a 3–year period. Four high and four low tillering genotypes from each of two crosses (Neepawa/M1417 and Ingal/M1417) were studied. Weeds consisted of cultivated oat (Avena saliva cv. ‘Waldern’) and oriental mustard (Brassicajuncea cv. ‘Cutlass’) sown at two densities (48 and 96 seeds/m² per weed species). Seedling establishment, ground cover, and seed yield for the three species were determined, as was wheat tiller number, spike number, maximum height, leaf area index, leaf orientation, and flag leaf length and size. Significant (P = 0.001) weed rate by genotype interactions involving changes in genotype rank were detected for wheat grain yield, indicating that the 16 wheat genotypes differed in competitive ability. Wheat grain yield reductions averaged over the two weed densities ranged from 45% to 59%. The highest-yielding genotypes under weed-free conditions were not necessarily the highest yielding under weedy conditions. Genotypes which suffered smaller yield reductions were more effective in suppressing weed growth. Although competitive genotypes were generally taller than non-competitive genotypes, other traits such as large seedling ground cover and flag leaf length were associated with wheat yield under competitive conditions.     

Predicting Crop Yields with the Agricultural Reference Index for Drought

A generic agricultural drought index, called Agricultural Reference Index for Drought (ARID), was designed recently to quantify water stress for use in predicting crop yield loss from drought. This study evaluated ARID in terms of its ability to predict crop yields. Daily historical weather data and yields of cotton, maize, peanut and soybean were obtained for several locations and years in the south‐eastern USA. Daily values of ARID were computed for each location and converted to monthly average values. Using regression analyses of crop yields vs. monthly ARID values during the crop growing season, ARID‐yield relationships were developed for each crop. The ability of ARID to predict yield loss from drought was evaluated using the root mean square error (RMSE), the Willmott index and the modelling efficiency (ME). The ARID‐based yield models predicted relative yields with the RMSE values of 0.144, 0.087, 0.089 and 0.142 (kg ha^?1 yield per kg ha^?1 potential yield); the Willmott index values of 0.70, 0.92, 0.86 and 0.79; and the ME values of 0.33, 0.73, 0.60 and 0.49 for cotton, maize, peanut and soybean, respectively. These values indicated that the ARID‐based yield models can predict the yield loss from drought for these crops with reasonable accuracy.     

Screening chickpea for adaptation to water stress: Associations between yield and crop growth rate

Robust associations between yield and crop growth rate in a species-specific critical developmental window have been demonstrated in many crops. In this study we focus on genotype-driven variation in crop growth rate and its association with chickpea yield under drought. We measured crop growth rate using Normalised Difference Vegetative Index (NDVI) in 20 diverse chickpea lines, after calibration of NDVI against biomass accounting for morphological differences between Kabuli and Desi types. Crops were grown in eight environments resulting from the combination of seasons, sowing dates and water supply, returning a yield range from 152 to 366 g m⁻². For both sources of variation – environment and genotype – yield correlated with crop growth rate in the window 300 °Cd before flowering to 200 °Cd after flowering. In the range of crop growth rate from 0.07 to 0.91 g m⁻² °Cd⁻¹, the relationship was linear with zero intercept, as with other indeterminate grain legumes. Genotype-driven associations between yield and crop growth rate were stronger under water stress than under favourable conditions. Despite this general trend, lines were identified with high crop growth rate in both favourable and stress conditions. We demonstrate that calibrated NDVI is a rapid, inexpensive screening tool to capture a physiologically meaningful link between yield and crop growth rate in chickpea.     

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.     

Evaluation of Genotype × Environment Interactions in Chinese Spring Wheat by the AMMI Model, Correlation and Path Analysis

An understanding of the characteristics of crop varieties and advanced lines could help improve their cultivation and to further enhance their potential. The objectives of this study were to estimate the genotype (G), environment (E) and genotype × environment (GE) interactions on the grain yield of Chinese spring wheat genotypes in 2000 and 2001 by the additive main effects and multiplicative interaction (AMMI) model, and to evaluate the relationships between yield and its components by correlation and path analysis. Grain yield varied from 3.9 to 5.2 t ha^?1, among which SW8188 had the highest yield performance, followed by 58769‐6 and Chuannong 16. Three interaction principal components (IPC) accounted for a total of 79.99 % and 72.96 % of the interactions with 41.05 % and 52.08 % for the corresponding degrees of freedom in 2000 and 2001, respectively. When IPC3 was significant, the stability coefficient D_i was more useful in the evaluation of the stability of each genotype. The estimates of D_i in the 2 years indicated that the D_i values varied between genotypes and years. The D_i values ranged from 1.804 to 14.665 and 2.497 to 12.481 in 2000 and 2001 respectively. The suitable locations (environments) for all genotypes were characterized. These results would be useful for improving the Chinese spring wheat cultivation and improvement.     

Assessment tool for biological nitrogen fixation of Vicia faba cultivated as spring main crop

Published data on experiments with faba bean were used to develop a software tool for estimating the amount of nitrogen fixed (separately in grains and harvest residues) and the percentage of total nitrogen derived from the atmosphere (%Ndfa) from a minimum input data set of soil parameters (soil texture or, if available, soil yield potential, mineral nitrogen at the time of seeding, soil pH) and water supply during the period of growth. The tool is applicable for cropping situations in Central and Western Europe, where faba bean is grown as a spring crop. It is designed either for an assessment after the harvest of the crop (grain yield known) or for anticipating the nitrogen amounts resulting from cropping situations in the future (grain yield unknown). For the latter case a rough yield estimate is included. A comparison of estimated values of fixed nitrogen (t ha⁻¹) and percentage of nitrogen derived from the atmosphere (%Ndfa) with data from the literature yielded coefficients of determination (r²) of 0.53 for the amount of fixed nitrogen and 0.29–0.63, depending on the used dataset, for %Ndfa.     

Post-harvest Evaluation of Nitrogen Sufficiency for Small-grain Cereals by Measuring Grain Protein Concentration

Nitrogen (N) fertilizer is an important and expensive input in small-grain cereal production, and growers therefore should aim to optimize its use. Possibilities for using grain protein concentration for post-harvesr evaluation of N sufficiency were determined in this study. Field experiments including spring wheat ( Triticum aestivum L.), spong barley ( Hordeum vulgare L) and spring oats ( Avena sativa L.), and various rates of N fertilizer application were conducted in southern and western-Finland over 2 years. Grain yield and grain protein were positively correlated and firred quadratic regression models. Both critical and optimum levels for grain protein concentration were determined by Cate-Nelson analysis Critical values were 12.2 for wheat, 10.2 for barley and 10.9 for oats, and corresponding optimum values were 13.3, 11.1 and 12.7, respectively. The accuracy of the method was tested using results from on-farm spring wheat trials. The results indicated that N fertilizer uptake and grain yield were best in held where gram protein concentration exceeded the critical values but not the optimum. Growers should use more intensive N fertilization management if grain protein concentration does not exceed critical values. Grain protein concentrations above optimum values indicate over-fertilization for maximum grain yield. Analysing previous research data to identify the "critical level" of grain protein concentration is not difficult, and will provide powers, extension personnel, and fertilizer dealers with a cost effective means of evaluating the efficiency of N use by the crop and for developing N fertilization recommendations.     

Mechanical control of clover improves nitrogen supply and growth of wheat in winter wheat/white clover intercropping

The major objective for clover in a winter wheat/white clover intercropping system is to supply nitrogen (N) for the wheat. A field experiment was repeated in 2 years on a loamy sand in Denmark to investigate the possibilities for increasing N supply to the winter wheat by cutting and mulching the clover between the wheat rows. The clover was cut with a weed brusher on three different dates in each year.Intercropped wheat with unbrushed clover had a lower grain yield than wheat as a sole crop. Brushing increased wheat N uptake and wheat grain yields. Intercropping with two or three brushing dates gave higher wheat yields than wheat as the sole crop. The largest increases in grain N uptake, 21–25 kg N ha⁻¹, were obtained for the brushings around wheat flag leaf emergence. The highest yield increases with a single brushing, 0.98–1.11 Mg DM ha⁻¹, were obtained when brushing was performed during the stem elongation phase. The largest grain yields for treatments with two brushings were obtained with a first brushing at start of stem elongation and a second around flag leaf emergence. The first brushing probably provided N to increase the wheat leaf area index and thus the light interception, while the second brushing provided N to sustain the leaf area during grain filling and reduced clover biomass and therefore competition for water. Intercropping wheat and clover increased grain N concentrations by 0.11–0.39%-point compared with wheat as a sole crop. Intercropping may thus offer possibilities for improving the bread-making quality of organically grown wheat.     

Maize Grain Yield Response to Tillage and Fertilizer Nitrogen Rates on a Tara Silt Loam

Effects of tillage on the appropriate fertilizer N applications needed to achieve maximal grain yield are poorly denned. The study objective was determination of relative corn grain yield response to N application rate for four tillage practices: no-tillage (NT), ridge tillage (RT), fall chisel plowing (CP) and fall moldboard plowing (MP). Maize (Zea mays L.) grain yield and N accumulation were monitored over a 6 year period with the same tillage treatment and the same fertilizer N rate applied each year to each plot. Two hybrids, differing in relative maturity rating, were planted each year. Fertilizer N rates ranged from 10 to 190 kg ha^?1 and consisted of 10 kg ha^?1 of liquid starter N applied at planting with varying amounts of fall applied anhydrous ammonia. With only starter fertilizer, grain yields increased with tillage intensity in the order NT ≤ RT ≤ CP ≤ MP. With ≥ 55 kg total applied Nha^?1, 6 year average grain yields were unaffected by tillage. Total N removed in grain annually with only starter fertilizer ranged from 25–85 kg ha^?1 Maximal amounts of N removed, about 145 kg N ha^?1, occurred with 100–145 kg applied N ha^?1 for all tillage treatments under the more favorable climatic conditions. Several interactions affecting grain yield appear climatically sensitive with exception of tillage by fertilizer N interactions. Because of variability in climate, planting dates varied by almost 4 weeks. Relative yield loss due to planting delay were Fertilizer N (mean change ??124 –?275 kg ha^?1 day^?1) > Starter N only and MP (mean ?? 259 kg ha^?1 day^?1) > other tillages in general. Yield loss due to delayed planting ranged from 0.0–275 kg ha^?1 day^?1. Grain yield gains due to early spring soil temperatures were 16.0–21.8 kg ha^?1 index-degree^?1 with MP tillage and averaged 2.7– 16.7 kg ha^?1 index-degree^?1 more than those of other tillage-hybrid combinations.     

Growth and Yield Response of Facultative Wheat to Winter Sowing, Freezing Sowing and Spring Sowing at Different Seeding Rates

Growth and yield of wheat are affected by environmental conditions and can be regulated by sowing time and seeding rate. In this study, three sowing times [winter sowing (first week of September), freezing sowing (last week of October) and spring sowing (last week of April)] at seven seeding rates (325, 375, 425, 475, 525, 575 and 625 seeds m^?2) were investigated during the 2002–03 and 2003–04 seasons, in Erzurum (Turkey) dryland conditions, using Kirik facultative wheat. A split‐plot design was used, with sowing times as main plots and seeding rates randomized as subplots. There was a significant year × sowing time interaction for grain yield and kernels per spike. Winter‐sown wheat produced a significantly higher leaf area index, leaf area duration, spikes per square metre, kernel weight and grain yield than freezing‐ and spring‐sown wheat. The optimum time of sowing was winter for the facultative cv. Kirik. Grain yields at freezing and spring sowing were low, which was largely the result of hastened crop development and high temperatures during and after anthesis. Increasing seeding rate up to 525 seeds m^?2 increased the spikes per square metre at harvest, resulting in increased grain yield. Seeding rate, however, was not as important as sowing time in maximizing grain yield. Changes in spikes per square metre were the major contributors to the grain‐yield differences observed among sowing times and seeding rates. Yield increases from higher seeding rates were greater at freezing and spring sowing. We recommended that a seeding rate of 525 seeds m^?2 be chosen for winter sowing, and 575 seeds m^?2 for freezing and spring sowing.     

Comparison of models and indicators for categorizing soft wheat fields according to their grain protein contents

The ability to deliver wheat grain with a specific protein content is a major determinant of the profitability of wheat grain production. Various crop models have been developed to predict yield and grain protein content on a field scale. They can be used to predict each year, before harvest, the yields and grain protein contents of the different fields in a collecting area, leading to an optimization of the grading process into low and high protein standards. Indicators have been developed for nitrogen management at the field scale. They can be used to predict grain quality because grain protein content depends strongly on the crop nitrogen uptake during the vegetative growth of the wheat crop. The aim of this study was to evaluate the accuracy of two indicators, nitrogen nutrition index and chlorophyll content of leaves (in SPAD units), and of two models for categorizing fields according to their grain protein contents. A data set including field measurements over 3 years was used to estimate the sensitivity and specificity of the models and indicators using the receiver operating characteristic evaluation procedure. High values of sensitivity and specificity were obtained for the two indicators, and decision thresholds leading to low false negative and false positive proportions were identified. Nitrogen nutrition index showed the best results for the three grain protein content thresholds considered. Conversely, sensitivity and specificity values obtained for the two models were low. Combining model predictions and indicator values did not give better discrimination than the use of a single indicator.     

春小麦籽粒灌浆期沉淀值动态的定量研究

以3个不同品质类型春小麦品种为材料,在设定不 同肥力和气象条件等环境因子基础上,通过建立灌浆期籽粒沉淀值动态曲线拟合方程,定量揭示籽粒沉淀值的动态与规律。结果表明,灌浆期籽粒沉淀值随时间的变化符合一元三次多项式凸性曲线,即自开花始先增后降的单峰曲线。灌浆期籽粒沉淀值的动态,不同基因型具有不同特点。高蛋白强筋、高蛋白中筋和低蛋白弱筋品种曲线峰值分别出现在开花后28 d、23 d和30 d前后。各品种沉淀值积累速度的动态特点是成熟时沉淀值高蛋白强筋品种最高,高蛋白中筋品种次之,低蛋白弱筋品种最低的主要原因。     

Canopy Temperature Depression Association with Yield of Irrigated Spring Wheat Cultivars in a Hot Climate

This study examines the utility of measurements of canopy temperature depression (TD) below air temperature as an indirect selection criterion for grain yield in breeding of wheat ( Triticum aestivum L.). TD differences can be rapidly assessed using a hand-held infra-red thermometer and may reflect differences in stomatal conductance. Replicated small plot experiments were conducted with 23–24 spring wheat cultivars planted at two dates in the winter cropping cycles of 1991–92 and 1992–93 at a hot low latitude location in Mexico.
Across a two fold range in grain yield at each planting, TD was usually highly significantly correlated with grain yield. Averaging the results for each year, the phenotypic correlation TD vs. yield was 0.84** and 0.89** for 1991–92 and 1992–93, respectively. Correlations were strongest when TD was measured between noon and 4 pm, and were unaffected by crop stage of development (pre-heading, heading, grain fill) or timing relative to irrigation. TD was positively correlated with stomatal conductance. We discuss possible causes of these high correlations, and their implications for wheat breeding.     

冬小麦-夏玉米“双晚”种植模式的产量形成及资源效率研究

为了进一步明确黄淮平原冬小麦晚播、夏玉米晚收的“双晚”增产及资源高效的效应,选用2个中熟冬小麦品种和2个中晚熟夏玉米品种,于2006—2008年先后在河南温县和焦作进行大田试验,研究作物群体物质生产、产量形成参数定量指标及光温资源的分配利用。结果表明,冬小麦晚播产量降低不明显,夏玉米晚收产量显著提高747~2 700 kg hm^-2,“双晚”周年产量21 891~22 507 kg hm^-2,比对照提高442~2 575 kg hm^-2。冬小麦晚播平均叶面积指数、每平方米穗数和穗粒数降低,但平均净同化率、收获指数和粒重提高达5%显著水平;夏玉米晚收平均叶面积指数、收获指数、生育期天数和粒重均显著提高。“双晚”栽培优化了周年资源分配,提高生育期与光、温资源变化的吻合度,其生产效率分别提高2.22%~10.86%和0.47%~11.56%。小麦和玉米品种的遗传类型是影响“双晚”栽培技术的关键。因此,选用小麦晚播早熟高产和玉米长生育期晚熟品种,通过有效调节资源配置,将小麦冗余的光温资源分配给C₄高光效作物玉米,是提高周年高产高效的重要途径。     

Is it possible to forecast the grain quality and yield of different varieties of winter wheat from Minolta SPAD meter measurements?

To optimize wheat segregation for the various markets, it is necessary to add to genotype segregation, a prediction before harvest of the values of yield and grain protein concentration (GPC) for the different fields of the collecting area. Different tools allowing a prediction of crop production exist. Among them, the evaluation of nitrogen concentration by a chlorophyll meter (Soil–Plant Analysis Development (SPAD) readings), classically used to adapt the nitrogen fertilizer application, has been used in few works to foresee grain yield and grain protein concentration. But the relationships between N crop status and SPAD measurements varies among varieties and this genotypic effect has rarely been incorporated in models of forecasting grain quality.This paper compares several models to forecast yield, nitrogen uptake in grain (NUG) and grain protein concentration from trials carried out in 2001 and 2002 at the INRA experiment station of Grignon (West of Paris). Trials crossed nine varieties by four (2002) or five (2001) nitrogen rates. Input variables of those models are mainly chlorophyll meter measurements (SPAD) on the penultimate leaf at GS65 and on the flag leaf at GS71 Zadoks growth stages and ear number per square meter (NE).A square root model of yield based on NE × SPAD gave the best fit (RMSE = 0.6 t ha⁻¹ for both stages) if considering three different groups of genotypes. Based on the same variable, NE × SPAD, a quadratic model for NUG without significant effect of genotypes gave the best fit (RMSE, between 21 and 30 kg ha⁻¹ depending of the growth stage). And, for GPC, considering the same three groups of genotypes, the slope of the linear model with the ratio of predicted grain nitrogen concentration to predicted yield, is the same at both stages and very close to the standard value used to calculate protein concentration from nitrogen concentration (5.7), but the predictive quality of the model is more than 10% higher at GS71 (R² of 0.77) than at flowering (R² of 0.64). Finally, the sensibility of the models to delay in the stage of measurement is discussed.     

Multi-model uncertainty analysis in predicting grain N for crop rotations in Europe

Realistic estimation of grain nitrogen (N; N in grain yield) is crucial for assessing N management in crop rotations, but there is little information on the performance of commonly used crop models for simulating grain N. Therefore, the objectives of the study were to (1) test if continuous simulation (multi-year) performs better than single year simulation, (2) assess if calibration improves model performance at different calibration levels, and (3) investigate if a multi-model ensemble can substantially reduce uncertainty in reproducing grain N. For this purpose, 12 models were applied simulating different treatments (catch crops, CO₂ concentrations, irrigation, N application, residues and tillage) in four multi-year rotation experiments in Europe to assess modelling accuracy. Seven grain and seed crops in four rotation systems in Europe were included in the study, namely winter wheat, winter barley, spring barley, spring oat, winter rye, pea and winter oilseed rape. Our results indicate that the higher level of calibration significantly increased the quality of the simulation for grain N. In addition, models performed better in predicting grain N of winter wheat, winter barley and spring barley compared to spring oat, winter rye, pea and winter oilseed rape. For each crop, the use of the ensemble mean significantly reduced the mean absolute percentage error (MAPE) between simulations and observations to less than 15%, thus a multi–model ensemble can more precisely predict grain N than a random single model. Models correctly simulated the effects of enhanced N input on grain N of winter wheat and winter barley, whereas effects of tillage and irrigation were less well estimated. However, the use of continuous simulation did not improve the simulations as compared to single year simulation based on the multi-year performance, which suggests needs for further model improvements of crop rotation effects.     

Optimierung der Produktion von Wintergetreide zur Bioethanolherstellung durch unterschiedlich intensive Anbauverfahren

The investigations were based on biennial field trials carried out at two locations comprising the factors location/previous crop, winter cereal genotype (rye cv. ‘Farino’,triticale cv. ‘Modus’, wheat cv. ‘Batis’) and production intensity level. One agronomical focus was to replace the mineral N‐supply due to its energetic relevance, by either the residues of legumes, or stillage, a processing residue containing organic N. The measurement included the crop yield ha^?1, the bioethanol exploitation dt^?1 and the bioethanol yield ha^?1. The last was closely correlated to the grain yield and thus dominated by intensity level. Highest bioethanol yields with an average peak at 4022 l ha^?1, always occurred at the highest intensity level. Bioethanol exploitation however, was mainly determined by the genotype. The cultivars showed significant exploitation and yield differences. An adequate bioethanol exploitation was observed with the wheat cv. Batis in contrast to diminished grain and bioethanol yields. Considering bioethanol exploitation and bioethanol yield, the triticale cv. Modus was the outstanding genotype. Despite high grain yields, the bioethanol yields of the rye cv. Farino stayed mean, because of a genotypic lowered bioethanol exploitation. Comparing the approaches of mineral nitrogen substitution, legume N was successful, whereas stillage fertilizing, according to the examined conditions, resulted in ample decreased grain and bioethanol yields ha^?1.     

Environmental factors affecting yield variability in spring and winter rapeseed genotypes cultivated in the southeastern Argentine Pampas

Rapeseed yields in Argentina are low (averaging 1400 kg/ha nationwide) with a high inter-annual variability. One of the limiting factors for improving yields is the lack of information on the adaptability of the cultivars, especially in the main rapeseed-producing area, the southeastern Pampas. The objectives of this study were to (i) quantify and analyze the yield variability of winter and spring rapeseed hybrids introduced in Argentina, (ii) identify the main environmental factors that affect the yields of the spring and winter genotypes in the southeastern Pampas, and (iii) model and validate rapeseed yields from environmental variables in the pre- and post-flowering periods. Principal component analysis (PCA) and linear regression methods were used to analyze 129 data points from 16 comparative yield trials in eight sites of southeastern Pampas. The rainfed crops were sown between April and July and from 2007 to 2009. Pre- and post-flowering phases were recorded in each experiment; temperature, frost occurrences, rainfall and radiation were measured during each phase. Yield variability (600–3700 kg ha⁻¹) was slightly lower in spring than in winter genotypes (CV 0.25 versus 0.38). Sixty percent of the winter genotype variability was explained by the first axis which was associated to the pre- and post-flowering durations, while 25% of the variability was explained by the second axis associated to yield. Almost 50% of the spring genotype variability was explained by the first axis associated to pre-flowering and total durations, while 27% of the variability was explained by the second axis in which post-flowering duration was associated to yield. Winter genotypes evidenced vernalization requirements that were either partially or not fulfilled, so, the longer the photoperiod, the longer the pre-flowering phase duration. In the critical period of 30 d post-flowering, yield was not associated to the photothermal quotient. In winter genotypes, yield was associated to a linear model which included rainfall during the crop cycle, radiation and pre-flowering temperatures (R² = 0.50). The model was adequately validated with independent data (n = 116) from official trials. For spring genotypes, only the frost occurrences during the critical period were relevant (R² = 0.26) and placing the flowering time after October decreased the risk of late frost damage. Water use efficiency (WUE) values ranged from 1.6 to 6.7 kg ha⁻¹ per mm of rain without a clear trend between spring and winter genotypes for this trait. In conclusion, winter genotypes did not necessarily yield more than the spring materials. In addition, rainfall during the crop cycle and frost occurrences during flowering were the main limiting factors of the winter and spring genotype yields, respectively, in the southeastern Pampas.