基于升尺度方法的华北冬小麦区域生长模型初步研究 Ⅰ.潜在生产水平

作物生长模型是在田间尺度上开发的,而区域尺度上的作物生长信息更受决策部门的关注。作物模拟从单点研究发展到区域应用需要解决升尺度连接（Scaling-up）等一系列技术问题。本文利用以经纬度为权重的IDW空间插值法对气象数据和与温度有关的作物参数进行空间插值;根据华北冬小麦的品种地带性分布特点进行了冬小麦品种参数     

Multivariate analysis of water-related agroclimatic factors limiting spring wheat yields on the Canadian prairies

Water use by spring wheat and soil water contents at meteorological stations on the Canadian prairies were simulated with the Versatile Soil Moisture Budget model for different crop growth stages. Six water-related agroclimatic indices at five growth stages (seeding–emergence, emergence–jointing, jointing–heading, heading–soft dough and soft dough–harvest) and previous non-growing season were correlated to spring wheat yields in the three prairies provinces and in the entire prairie region for the years 1976–2006. Principal component analysis was applied to explore major modes of joint variability in the regional water-related agroclimatic indices. Canonical correlation analysis was employed to further identify joint variability patterns of the water-related indices associated with regional spring wheat yields. Results showed some common features of the effects of the water-related factors at different growth stages: lower-than-normal moisture stress at the jointing–heading stage favoured spring wheat yields in all three provinces. Regional differences were also seen, for example, a slight moisture stress at the heading–soft dough stage could be beneficial to spring wheat yields in Manitoba because of its relatively wetter climate compared to the other two provinces. The results can be used for a better understanding of the effects of water-related agroclimatic conditions at different growth stages on final spring wheat yields on the Canadian prairies, leading to the improvement of crop management. The results can also be used in regional yield forecasting and in the projection of climate change impacts on crop production. This study provided an example of how to quantify crop–climate relationships by the use of statistical multivariate analysis tools.     

从农业生态系统承载力看全球生态经济系统前景

本研究运用3种方法预测作物产量潜力,结果表明：（1）利用作物历年单产回归拟合后进行趋势外推,得出多数作物的未来产量潜力极限大约是现在单产的2~3倍;（2）运用“国际应用系统研究所”（IIASA）与“联合国粮农组织”（FAO）共同开发的“农业生态区划”（AEZ）模型计算中国主要粮油作物的区域单产最高潜力,得出水稻、小麦、玉米、马铃薯、油菜和大豆的单产潜力分别是它们2005年全国平均单产的1.2倍、2.2倍、2.2倍、2.9倍、2.0倍、1.9倍;（3）运用自然界中植物的最大光能利用率计算世界主要粮油作物单产的光合潜力,得出水稻、小麦、玉米、马铃薯、油菜、大豆产量的最大光合生产潜力大约分别是目前高产地区单产的1.4倍、2.5倍、1.2倍、1.8倍、1.9倍、2.2倍。据此：从作物产量潜力极限出发,阐述了农业生态系统的承载力;再从“封闭”系统特性出发,论述了全球生态经济系统的不可持续性。     

‘Fingerprints’ of four crop models as affected by soil input data aggregation

The spatial variability of soil properties is an important driver of yield variability at both field and regional scale. Thus, when using crop growth simulation models, the choice of spatial resolution of soil input data might be key in order to accurately reproduce observed yield variability. In this study we used four crop models (SIMPLACE<LINTUL-SLIM>, DSSAT-CSM, EPIC and DAISY) differing in the detail of modeling above-ground biomass and yield as well as of modeling soil water dynamics, water uptake and drought effects on plants to simulate winter wheat in two (agro-climatologically and geo-morphologically) contrasting regions of the federal state of North-Rhine-Westphalia (Germany) for the period from 1995 to 2008. Three spatial resolutions of soil input data were taken into consideration, corresponding to the following map scales: 1:50 000, 1:300 000 and 1:1 000 000. The four crop models were run for water-limited production conditions and model results were evaluated in the form of frequency distributions, depicted by bean-plots.In both regions, soil data aggregation had very small influence on the shape and range of frequency distributions of simulated yield and simulated total growing season evapotranspiration for all models. Further analysis revealed that the small influence of spatial resolution of soil input data might be related to: (a) the high precipitation amount in the region which partly masked differences in soil characteristics for water holding capacity, (b) the loss of variability in hydraulic soil properties due to the methods applied to calculate water retention properties of the used soil profiles, and (c) the method of soil data aggregation.No characteristic “fingerprint” between sites, years and resolutions could be found for any of the models. Our results support earlier recommendation to evaluate model results on the basis of frequency distributions since these offer quick and better insight into the distribution of simulation results as compared to summary statistics only. Finally, our results support conclusions from other studies about the usefulness of considering a multi-model approach to quantify the uncertainty in simulated yields introduced by the crop growth simulation approach when exploring the effects of scaling for regional yield impact assessments.     

Evaluation of regional climate scenario data for impact assessment of climate change on rice productivity in Korea

Spatial evaluation of the uncertainty associated with climate data would allow reliable interpretation of simulation results for regional crop yield using gridded climate data as input to a crop growth model. The objective of this study was to examine the spatial uncertainty of regional climate model data through determining optimal seeding date with the ORYZA2000 model for assessment of climate change impact on rice productivity in Korea. The optimal seeding date was determined at each grid point using regional climate model outputs under the RCP 8.5 scenarios. In major rice production areas such as inland plain regions, where temperatures of regional climate data were relatively accurate, the optimal seeding date determined using those gridded data were reasonable. However, areas with complex terrains including areas near bodies of water, e.g. coastal areas, riverbasins, lakes, and mountainous areas, had a relatively large uncertainty of the optimal seeding date determined using the regional climate data. These results indicated that the uncertainty of regional climate data at a high spatial resolution of 12.5 km should be taken into account in the regional impact assessment based on crop growth simulations in Korea. In addition, further studies would be merited to assess the impact of climate change on rice yield at an ultra-high spatial resolution of 1 km in Korea. Crop yields were projected to decrease after the 2020s when crop yield simulations from inland plain areas were considered, which suggested that adaptation strategies should be established and implemented in the near future.     

Geostatistical interpolation and aggregation of crop growth model outputs

Many crop growth models require daily meteorological data. Consequently, model simulations can be obtained only at a limited number of locations, i.e. at weather stations with long-term records of daily data. To estimate the potential crop production at country level, we present in this study a geostatistical approach for spatial interpolation and aggregation of crop growth model outputs. As case study, we interpolated, simulated and aggregated crop growth model outputs of sorghum and millet in West-Africa. We used crop growth model outputs to calibrate a linear regression model using environmental covariates as predictors. The spatial regression residuals were investigated for spatial correlation. The linear regression model and the spatial correlation of residuals together were used to predict theoretical crop yield at all locations using kriging with external drift. A spatial standard deviation comes along with this prediction, indicating the uncertainty of the prediction. In combination with land use data and country borders, we summed the crop yield predictions to determine an area total. With spatial stochastic simulation, we estimated the uncertainty of that total production potential as well as the spatial cumulative distribution function. We compared our results with the prevailing agro-ecological Climate Zones approach used for spatial aggregation. Linear regression could explain up to 70% of the spatial variation of the yield. In three out of four cases the regression residuals showed spatial correlation. The potential crop production per country according to the Climate Zones approach was in all countries and cases except one within the 95% prediction interval as obtained after yield aggregation. We concluded that the geostatistical approach can estimate a country’s crop production, including a quantification of uncertainty. In addition, we stress the importance of the use of geostatistics to create tools for crop modelling scientists to explore relationships between yields and spatial environmental variables and to assist policy makers with tangible results on yield gaps at multiple levels of spatial aggregation.     

Defining traits for selection using isopopulations of sorghum

Sorghum is grown in the subtropics in north-eastern Australia, where production is risky due to limited planting opportunities and highly variable rainfall during the crop cycle. To improve grain yields of sorghum there, plant breeders have adopted the empirical approach of selecting directly for grain yield, because traits likely to confer improved adaptation have not been clearly defined. Even for readily-observed traits such as maturity type, no clear selection goal had been identified for this highly variable environment. This paper examined the use of isopopulations as a tool for defining traits for selection in plant breeding programs, and discussed the merits of this approach relative to other alternatives. Phenology of sorghum in north-eastern Australia was used as a case study. Isopopulations differing in maturity were developed from three populations of sorghum, and were grown in five contrasting locations. For stable grain yields, the best time to flowering was consistently about 1200 day-degrees using a base temperature of 7°C (or about 60 days to flowering for midsummer plantings in Central Queensland). This result was in accord with other direct experimental evidence, but contrasted with recent simulations reported in the literature, which suggested a longer crop duration was preferred. Our conclusions were that the crop model failed to extend growth duration in response to water stress, thereby increasing the yield expectation for a later flowering hybrid in a poor season. The simulations also assumed a full profile of soil water at planting in every season, which would have provided a different outcome from simulations in which conditions prior to planting were permitted to impact antecedent soil water. The strengths and weaknesses of experimental and simulation approaches were discussed, before concluding that the comprehensive evaluation of traits may best be accomplished by a combination of approaches: analysis of variety trial data, direct comparisons using isopopulations or isolines, and appropriate use of a validated crop model.     

Uncertainty assessment of soya bean yield gaps using DSSAT‐CSM‐CROPGRO‐Soybean calibrated by cultivar maturity groups

Soya bean yield gap can be caused by different factors resulting in uncertainties when the objective is to use such information for farm decision‐making and reference yield determination. Thus, this study aimed to quantify the soya bean yield gap for four sites, located in Southern and Midwestern Brazil, as well as the uncertainties of that related to cultivars, sowing dates, soil types and reference yields. The crop simulation model DSSAT‐CSM‐CROPGRO‐Soybean was calibrated for cultivars with similar maturity groups, based on the data obtained from the best farmers at the county level. The yield gap by water deficit (YG_WD) was obtained through the difference between potential and attainable yields, and that one caused by sub‐optimum crop management (YG_CM) by subtracting actual yield of each county, obtained from official statistics between 1989/90 and 2014/15 growing seasons, from the estimated attainable yield. The yield was simulated using four sowing dates, three soil types and two soya bean maturity groups by county. The reference yield uncertainty was quantified using yield reference from crop model and regional winners of the soya bean yield context, conducted by CESB (Brazilian Soybean Strategic Committee), for the growing seasons from 2013/14 to 2015/16. The crop model showed a good agreement between measured and simulated crop development and growth using calibration by maturity group, with low root mean square error (347 kg/ha). Southern sites had a mean YG_WD of 1,047 kg/ha, while in the Midwest, it was lower than 100 kg/ha. The YG_CM was 1,067, 528, 984 and 848 kg/ha, respectively, for Castro, PR, Mamborê, PR, Montividiu, GO and Primavera do Leste, MT, representing the opportunity for yield gain when having the best farmers as reference. The maturity groups, sowing dates and soil types showed to be an important source of uncertainty for yield gap determination, being recommended to investigate the farms in detail for an appropriate quantification. The reference yield showed expressive uncertainties, with some farmers presenting conditions to increase their soya bean yields by more than 3,000 kg/ha, when considering as reference the yields obtained by the winners’ farmers. These results show that uncertainties must be reduced when assessing farm yield gaps, in order to ensure that expected rate of soya bean yield growth could be reached by adopting the same technologies from CESB winners and best farmers in the county as a reference.     

遥感信息应用于水分胁迫条件下的华北冬小麦生长模拟研究

由于初始土壤水分、灌溉量等变量的空间分布不易获得,区域尺度水分胁迫条件下作物生长模拟存在一定难度。本文在WOFOST模型本地化和区域化的基础上,采用调控型方法,重点探讨了利用MODIS数据反演的地表蒸散在大范围内估算土壤水分平衡过程中的参数或变量初始值,以实现水分胁迫条件下作物模型区域模拟的可行性。2002年模拟结果显示,引入遥感信息优化获得初始土壤有效含水量、返青期生物量及抽穗期灌溉量后,土壤水分的模拟效果得到改善;32个农业气象试验站点模拟产量的相对均方根误差（RRMSE）由0.63降至0.20;华北冬小麦模拟产量的空间分布与实际产量分布更加接近,产量低估的情况得到较好改善;河北、河南、山东3省平均产量的模拟误差分别为－4.9%、4.3%和8.6%。初步结果表明,结合卫星遥感信息通过优化方法在大范围内估算作物模型的相关参变量,以实现水分胁迫条件下作物模型的区域应用是行之有效的。     

Response of Sweet Potato (Ipomoea batatas L.) to Different Planting Times

Agricultural seasons of the tropics are associated with rainfall, which provides the principal limiting resource for crop production. However, as tuber crops are sensitive to temperatures and moisture, the time of planting could have a profound influence on yields. Thus a study was carried out over a period of 12 months to determine the effect of different planting times on establishment, tuber initiation and yields of sweet potato. The trial was planted on similar soils in two agroecological zones as this species is a popular home garden crop in most regions of the tropics and subtropics.
Planting sweet potato with the onset of rains in October produced the highest yields. This is attributed to the receipt of adequate rainfall over the growth cycle, along with the higher diurnal variation in temperatures. Planting in the dry season or later in the wet season, which receives a lower quantity of rainfall with low diurnal variations in temperatures delayed tuber initiation and reduced yields. The study highlights differences in growth patterns of sweet potato when planed at different times. The importance of planting sweet potato in agricultural systems at appropriate times to produce higher yields is presented.     

气候变异对内蒙古武川县麦类作物产量的影响

【研究目的】内蒙古武川县位于阴山北麓农牧交错带温凉旱区,是春小麦、莜麦等喜凉作物的适宜产地,但由于气候的年际波动,产量低而不稳。通过分析产量与气候的关系,可采取措施减轻潜在的气候风险。【方法】笔者根据产量统计资料、生育期观测资料和历年气象数据,用相关分析法分析各时期平均温度、日照时数、降水量三个气候因子对春小麦、莜麦产量的影响,建立多因子产量评估模式。【结果】结果表明,武川小麦和莜麦的丰歉与生长季各月的平均气温及其总和呈负相关,与生长季各月降水量及生长季降水之和呈正相关,与日照时数关系不显著。通过找出影响产量丰歉的气象指标因子进行多元回归分析,得出产量的评价模型,可用于武川县麦类作物产量丰歉年评估。【结论】研究认为,气候暖干化将增加武川麦类作物生产的气候风险     

Including root architecture in a crop model improves predictions of spring wheat grain yield and above‐ground biomass under water limitations

Although the root length density (RLD) of crops depends on their root system architecture (RSA), the root growth modules of many 1D field crop models often ignored the RSA in the simulation of the RLD. In this study, two model set‐up scenarios were used to simulate the RLD, above‐ground biomass (AGB) and grain yield (GY) of water‐stressed spring wheat in Germany, aiming to investigate the impact of improved RLD on AGB and GY predictions. In scenario 1, SlimRoot, a root growth sub‐model that does not consider the RSA of the crop, was coupled to a Lintul5‐SlimNitrogen‐SoilCN‐Hillflow1D crop model combination. In scenario 2, SlimRoot was replaced with the Somma sub‐model which considered the RSA for simulating RLD. The simulated RLD, AGB and GY were compared with observations. Scenario 2 predicted the RLD, AGB and GY with an average root mean square error (RMSE) of 0.43 cm/cm³, 0.59 t/ha and 1.03 t/ha, respectively, against 1.03 cm/cm³, 1.20 t/ha and 2.64 t/ha for scenario 1. The lower RMSE under scenario 2 shows that, even under water‐stress conditions, predictions of GY and AGB can be improved by considering the RSA of the crop for simulating the RLD.     

西南地区不同套种模式对土壤肥力及经济效益的影响

为探讨不同作物组合的种植模式对土壤肥力及经济效益的影响,寻找作物搭配合理、低投入高产出的套作模式。采用单因素随机区组试验,研究比较了4种套作模式(小麦/玉米/大豆、小麦/玉米/甘薯、小麦/高粱/大豆、马铃薯/玉米/大豆)的土壤肥力、产量及经济效益的差异。连续种植3年后,各种植模式的土壤养分含量基本维持或得到提升(速效钾除外)。甘薯茬口对全钾、速效钾消耗均较大,需适当提高钾肥用量。小麦/玉米/甘薯的总产量最高,小麦/玉米/大豆模式的大豆单产量最高,小麦的单产各模式无显著差异。不同模式收益表现为,小麦/玉米/大豆马铃薯/玉米/大豆小麦/玉米/甘薯小麦/高粱/大豆;产投比表现为,小麦/玉米/大豆马铃薯/玉米/大豆小麦/玉米/甘薯小麦/高粱/大豆。小麦/玉米/大豆收益为14196.41元/hm~2,产投比为3.69:1,均为所有模式中最高,加之大豆茬口可以活化土壤养分,是相对理想的套作模式。     

Spatial and temporal uncertainty of crop yield aggregations

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty.The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics.Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05 (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.     

Combined impacts of climate and nutrient fertilization on yields of pearl millet in Niger

Effects of climate variability and change on yields of pearl millet have frequently been evaluated but yield responses to combined changes in crop management and climate are not well understood. The objectives of this study were to determine the combined effects of nutrient fertilization management and climatic variability on yield of pearl millet in the Republic of Niger. Considered fertilization treatments refer to (i) no fertilization and the use of (ii) crop residues, (iii) mineral fertilizer and (iv) a combination of both. A crop simulation model (DSSAT 4.5) was evaluated by using data from field experiments reported in the literature and applied to estimate pearl millet yields for two historical periods and under projected climate change. Combination of crop residues and mineral fertilizer resulted in higher pearl millet yields compared to sole application of crop residues or fertilizer. Pearl millet yields showed a strong response to mean temperature under all fertilization practices except the combined treatment in which yields showed higher correlation to precipitation. The crop model reproduced reported yields well including the detected sensitivity of crop yields to mean temperature, but underestimated the response of yields to precipitation for the treatments in which crop residues were applied. The crop model simulated yield declines due to projected climate change by −11 to −62% depending on the scenario and time period. Future crop yields in the combined crop residues + fertilizer treatment were still larger than crop yields in the control treatment with baseline climate, underlining the importance of crop management for climate change adaptation. We conclude that nutrient fertilization and other crop yield limiting factors need to be considered when analyzing and assessing the impact of climate variability and change on crop yields.     

Comparison of regional climate scenario data by a spatial resolution for the impact assessment of the uncertainty associated with meteorological inputs data on crop yield simulations in Korea

Uncertainty of crop yield simulation would be affected by weather input data prepared from different sources of climate datasets. Although regional climate data at a high spatial resolution would be useful for the impact assessment of climate change on crop production, little effort has been made to characterize the uncertainty associated with such climate data in terms of crop yield simulations. The objectives of this study were to compare climate scenario data products obtained from a series of downscaling processes and to identify an overall pattern of uncertainty in these climate data in terms of crop yield simulation. Regional climate scenario data from 2011 to 2014 had a spatiotemporal pattern of uncertainty, which differed by meteorological variables and spatial resolution. Overall, the uncertainty of daily minimum temperature was greater than that of maximum temperature. Daily minimum temperature also had relatively greater uncertainty in an early season of crop production, which could result in the cumulative impact on the uncertainty of crop yield simulations. For the uncertainty of climate data at different spatial resolution, climate data at higher spatial resolution, e.g. 1 km, tended to have lower uncertainty than data at resolution of 12.5 km did. Still, the uncertainty of regional climate data was relatively similar between data at resolution of 12.5 km and 1 km in major rice production areas in Korea except in areas near Seosan. This merits further studies to examine actual differences in projected crop yields using regional climate scenario data in the future and to assess the impact of uncertainty associated with regional climate data on crop yield simulation.     

Effects of Types and Duration of Weeds on Yield Parameters of Cocoyam (Xanthosoma saghtifolium)

Weeds reduce yields of all tropical crops significantly, and these effects vary with different species. Thus, the influence of time of weed control and types of weeds on yield parameters of cocoyams ( Xanthosoma sagittifolium ), a tropical tuber crop, was determined by two experiments conducted simultaneously. In one experiment, weeds were removed at different times during crop growth. In the other, different weed types were removed selectively to leave either one or two of the identified categories, namely grasses, broadleaves and sedges. The presence of weeds throughout the growth cycle of the crop reduced yields of cocoyams by 60 %. Removal of weeds at early growth stages produced greater yields than weeds were present beyond 18–22 weeks after planting. The presence of weeds at the time of tuber initiation had the greatest adverse effect on yield parameters. Weeds had a delaying but not an inhibitory effect on cormel initiation. However, all other yield parameters were reduced by weeds. Broadleaved weeds, which have similar growth patterns as cocoyams reduced yields to a greater extent than grasses. Sedges had no significant impact on cocoyams. The presence of grasses and broadleaves together reduced growth and yields of this crop to a greater extent than any other combination.     

The influence of organic and conventional fertilisation and crop protection practices,preceding crop,harvest year and weather conditions on yield and quality of potato (Solanum tuberosum) in a long-term management trial

The effects of organic versus conventional crop management practices (fertilisation, crop protection) and preceding crop on potato tuber yield (total, marketable, tuber size grade distribution) and quality (proportion of diseased, green and damaged tubers, tuber macro-nutrient concentrations) parameters were investigated over six years (2004–2009) as part of a long-term factorial field trial in North East England. Inter-year variability (the effects of weather and preceding crop) was observed to have a profound effect on yields and quality parameters, and this variability was greater in organic fertility systems. Total and marketable yields were significantly reduced by the use of both organic crop protection and fertility management. However, the yield gap between organic and conventional fertilisation regimes was greater and more variable than that between crop protection practices. This appears to be attributable mainly to lower and less predictable nitrogen supply in organically fertilised crops. Increased incidence of late blight in organic crop protection systems only occurred when conventional fertilisation was applied. In organically fertilised crops yield was significantly higher following grass/red clover leys than winter wheat, but there was no pre-crop effect in conventionally fertilised crops. The results highlight that nitrogen supply from organic fertilisers rather than inefficient pest and disease control may be the major limiting factor for yields in organic potato production systems.     

WOFOST模型对山东省夏玉米发育期与产量模拟的适用性评价

夏玉米作为山东省最主要的粮食作物,其生长发育与产量变化,对保障地区乃至全国的粮食安全具有举足轻重的作用。不同生育期内气象要素的不同组合对夏玉米发育进程与产量形成等将产生重要影响。WOFOST作物模型机理性较强、定量水平高,且更为高效,能够为客观、定量、动态地评估气象要素对夏玉米生产的影响提供技术支撑。为提高作物模型模拟的准确性,将山东省分为鲁西北、鲁中、鲁西南、鲁东南与半岛5个调参区域,并结合山东省10个夏玉米观测站2012-2014年玉米主栽品种的生长发育数据,开展模型的调参验证与适用性评价。研究结果表明,WOFOST模型对山东省各观测站点所有年份出苗期的模拟误差均不超过4d,决定系数（R ²）在0.43~0.99,归一化均方根误差（nRMSE）为0.3%~1.9%;针对开花期和成熟期,各观测站点绝大多数年份模拟误差均不超过5d,大多数观测站点R ²分别在0.77~0.99与0.51~0.99,各观测站点nRMSE分别在0.4%~2.3%与0.7%~3.2%;绝大多数观测站点产量模拟R ²在0.68~0.99,相对误差为0.8%~16.7%,绝大多数观测站点相对误差小于10%;nRMSE在1.2%~19.5%,均小于30%,大部分观测站点nRMSE小于10%。各评价指标均在可接受的范围内,WOFOST模型能够对山东省夏玉米发育期与产量进行较准确的模拟。     

Optimizing nitrogen use efficiency in wheat and potatoes: interactions between genotypes and agronomic practices

One approach to decrease the environmental impact of crop production and reduce costs is to optimize agronomic practices and genotypes so that nutrients are used more efficiently. In this study the effects of agronomic practices (rotations, crop protection, fertilization) on yields, nitrogen use efficiency (NUE) and associated parameters were studied in an experiment using two winter wheat genotypes (Cordiale and Scaro) in one season and two potato genotypes (Sarpo Mira and Sante) in two seasons. The wheat showed no varietal differences in yield and NUE; instead the fertilization regime was the main factor affecting yield and NUE with higher values observed when conventional fertilization was used. The exception was for wheat grown after three years grass/clover ley when there was no added yield benefit from conventional fertilization of the organically bred variety (Scaro). This demonstrates the potential for N fixing crops to provide sufficient N to high yielding cereals if grown for long enough prior to planting. The greatest gains in NUE were achieved by combining an N efficient genotype with conventional crop management in an organic rotation. Fertilization and genotypic variation were the main factors affecting potato tuber yield and NUE, with the late maturing Sarpo Mira displaying elevated yields and NUE compared with the early maturing Sante. The use of organic fertility sources resulted in lower NUE, but N release from organic sources may increase NUE of future crops. This highlights the need for long-term nutrient balance and modelling studies to assess NUE at the crop rotation scale.