APSIM模型的研究进展及其在中国的应用

作物模型可以在已有研究资料基础上对在多种模拟条件下的作物生长情况进行模拟,预测未知风险,优化农业资源管理措施,为农业生产的管理经营提供科学指导。APSIM模型就是作物模型的典型代表。为了研究APSIM模型的结构特征和它在中国的应用现状,本文首先阐述了APSIM模型的发展历史、运行框架、模块结构组成,其次归纳总结了APSIM模型在中国气候变化评估和农田管理方面的应用以及在中国各个气候区域的应用,最后指出了APSIM模型在中国应用时所出现的问题,并为APSIM模型的应用提出了与RS技术和GIS技术相结合等一些意见。     

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.     

APSIM模型对宁夏海原地区草田轮作系统的适应性

为验证APSIM模型对宁夏海原地区草田轮作系统的适用性,基于10年生苜蓿(Medicago sativa)与小麦(Triticum aestivum)、谷子(Oryza sativa)草田轮作试验数据和同期气象资料,运用APSIM模型对系统进行了模拟。通过试错法和文献记载完成了苜蓿、小麦和谷子几个品种的参数本地化。用统计和图形校验方法评价了APSIM模型模拟结果的可靠性和准确性。结果表明,谷子-小麦-小麦(MWW)、小麦-谷子-小麦(WMW)、谷子-谷子-小麦(MMW)、谷子-小麦-谷子(MWM)、小麦-谷子-谷子(WMM)、小麦-小麦-谷子(WWM)6种轮作方式下产量实测值和模拟值的决定系数R2值范围在0.83至0.98之间,D值范围在0.94至0.99之间,表现出了良好的相关性和一致性。土壤含水量实测值和模拟值的决定系数R2值范围在0.52至1之间,D值范围在0.92至0.97之间,相关性和一致性表现良好。表明APSIM模型对宁夏海原地区苜蓿与小麦、谷子轮作具有较好的模拟能力,可以用来模拟分析该地区草田轮作系统生产潜力和土壤水分动态,对该地区气候变化影响下草田轮作的优化管理具有一定指导意义。     

基于APSIM模型的不同施肥处理下春玉米产量模拟及气候敏感性分析

以平凉市泾川县大田试验为基础,利用农业生产系统模拟模型,探究不同类型施肥处理对春玉米产量的影响以及气候变化对春玉米产量影响的敏感性差异,分析 APSIM模型对模拟该地区春玉米产量的适应性。结果表明, APSIM模型对该区域春玉米产量的模拟效果较好,决定系数(R²)介于 0.934～0.975,归一化均方根误差(NRMSE)介于 6.073%～9.758%,模型一致性指标(D)介于 0.952～0.987。模型敏感分析显示,年平均温度是模拟不同施肥处理下春玉米产量的敏感参数,其变化程度对模拟结果影响较大,不同施肥处理(CK、 N、 NP)下的敏感指数分别为 0.533 2、 0.558 7和 0.568 2。     

Modelling phenological and agronomic adaptation options for narrow-leafed lupins in the southern grainbelt of Western Australia

Australian modern narrow-leafed lupin (Lupinus angustifolius L.) cultivars tend to flower early and are vernalisation-unresponsive (VU). Cultivars have generally been selected for the warmer climates zones and sandy soils of the northern grain belt of Western Australia (NWA), where lupins are predominantly grown. In areas where climates are cooler and growing seasons are longer and wetter, such as the southern grain belt of Western Australia (SWA), it is probable that lupin would have a higher yield potential. Given that VU cultivars would have a longer vegetative phase (i.e. late flowering) we hypothesise that they may be more productive than those that are early flowering. Here we used a modelling approach to: 1) test the hypothesis that cool-climate SWA would have higher lupin yield than warm-climate NWA; 2) explore lupin phenological adaptation and yield potential in SWA over a range of proposed VU cultivars; and 3) further evaluate the combined effects of cultivar phenology, sowing time and seasonal type on lupin yields.Simulations from the Agricultural Production Systems Simulator (APSIM) showed that, on average, lupin yield in SWA was higher than that in NWA, with 23% greater yield for the early-flowering cultivar Mandelup. Proposed cultivars flowering 22 days (late-flowering) and 15 days (medium flowering) later than Mandelup would have their phenology better adapted in the high and medium rainfall zones of SWA, producing 16 and 7% more grain in the two rainfall zones, respectively. The proposed late-flowering cultivar sown before the end of April achieved higher yields for all seasons in the high rainfall zone and for above average and average rainfall seasons in the medium rainfall zone. In more water-limited situations early sowing was preferable with no obvious difference in yield among cultivars. Despite this, the early-flowering cultivar yielded more when sown in late April. The results indicate that lupin production would benefit from breeding VU varieties with a long vegetative phase for the SWA that should be sown in mid to late April.     

A flexible approach to managing variability in grain yield and nitrate leaching at within-field to farm scales

We up-scaled the APSIM simulation model of crop growth, water and nitrogen dynamics to interpret and respond to spatial and temporal variations in soil, season and crop performance and improve yield and decrease nitrate leaching. Grain yields, drainage below the maximum root depth and nitrate leaching are strongly governed by interaction of plant available soil water storage capacity (PAWC), seasonal rainfall and nitrogen supply in the water-limited Mediterranean-type environment of Western Australia (WA). APSIM simulates the interaction of these key system parameters and the robustness of its simulations has been rigorously tested with the results of several field experiments covering a range of soil types and seasonal conditions in WA. We used yield maps, soil and weather data for farms at two locations in WA to determine spatial and temporal patterns of grain yield, drainage below the maximum root depth and nitrate leaching under a range of weather, soil and nitrogen management scenarios. On one farm, we up-scaled APSIM simulations across the whole farm using local weather and fertiliser use data and the average PAWC values of soil type polygons. On a 70 ha field on another farm, we used a linear regression of apparent soil electrical conductivity (EC_a) measured by EM38 against PAWC to transform an EC_a map of the field into a high resolution (5 m grid) PAWC map. We then used regressions of simulated yields, drainage below the maximum root depth and nitrate leaching on PAWC to upscale the APSIM simulations for a range of weather and fertiliser management scenarios. This continuous mapping approach overcame the weakness of the soil polygons approach, which assumed uniformity in soil properties and processes within soil type polygons. It identified areas at greatest financial and environmental risks across the field, which required focused management and simulated their response to management interventions. Splitting nitrogen applications increased simulated wheat yields at all sites across the field and decreased nitrate leaching particularly where the water storage capacity of the soil was small. Low water storage capacity resulted in both low wheat yields and large leaching loss. Another management option to decrease leaching may be to grow perennial vegetation that uses more water and loses less by drainage.Paper from the 5th European Conference on Precision Agriculture (5ECPA), Uppsala, Sweden, 2005     

补灌量和覆盖量变化对旱地春小麦叶面积指数的影响

免耕覆盖中补灌量和秸秆覆盖量变化对叶片生长有较大影响,以甘肃省定西市安定区1979—2019年历史气象数据为基础,运用APSIM模型对补灌量与覆盖量耦合变化时旱地春小麦的叶面积指数进行模拟,并采用方差分析、二次多项式回归、单因素分析等方法,研究补灌量和覆盖量对旱地春小麦叶面积的影响机制。结果表明：在试验设计范围内,旱地春小麦叶面积指数随着补灌量变化在分蘖—拔节期呈开口向下的二次抛物线先增后减变化,补灌量在252.09 mm时春小麦叶面积指数出现最大值为1.83,其他各个时期均呈开口向上的二次抛物线递增变化。随着秸秆覆盖量变化,叶面积指数在出苗—分蘖期,呈开口向下的二次抛物线先增后减变化,试验设计范围内秸秆覆盖量为2397.09 kg/hm²时春小麦叶面积指数出现最大值为0.59,分蘖—拔节期呈开口向下的二次抛物线递增变化;其他各个时期均呈开口向上的二次抛物线递增变化。相同阶段下,补灌量每增加50 mm,春小麦叶面积指数最大增幅13.95%;秸秆覆盖量每增加1000 kg/hm²,春小麦叶面积指数最大增幅3.7%。补灌量对春小麦叶面积指数的影响程度远大于秸秆覆盖量的影响。免耕覆盖中,合理的进行秸秆覆盖和补灌能够保持土壤中的水分,有利于旱地春小麦叶片生长。     

基于APSIM模型评估北方八省春玉米生产对气候变化的响应

基于北方地区农业气象试验站春玉米多年田间试验数据和逐日气象数据,分析农业生产系统模型APSIM在北方八省春玉米产区的适用性,在区域尺度上识别春玉米发育期和产量的关键气象响应因子,模拟过去54a（1961?2014年）该地区春玉米的生长发育和产量形成过程,探讨春玉米发育期和产量对气候变化的响应规律。结果表明：验证后的APSIM玉米模型在北方八省春玉米产区具有较好的适用性。气温和土壤温度是北方各地春玉米发育期的首要关键气象响应因子,其中北方春播区春玉米各关键发育期对最高气温响应最明显,西北内陆区春玉米各关键发育期对最低气温响应最明显。平均气温、日最高气温、日最低气温和土壤温度的升高均会导致春玉米生育期（出苗、开花和成熟）日序提前,发育天数减少,春玉米提前成熟。北方春播区春玉米产量对温度、降水、日照时数响应明显,西北内陆区春玉米产量对温度和潜在蒸散响应明显,大部分地区温度的升高和潜在蒸散的增加会引起玉米产量的显著下降。     

华北平原不同降水年型和作物种植模式下的产量和耗水模拟

华北平原是中国重要的粮食生产基地,在国家粮食产业中地位较高,但长期灌溉造成了华北平原地下水资源的严重亏损,地下水位持续下降。该研究利用APSIM模型对华北平原1986－2015年不同种植模式下的产量和耗水情况进行模拟研究,为华北平原调整作物种植模式、农业水资源管理以及农业发展政策的制定提供科学依据。研究结果表明：APSIM模型能够较好地模拟冬小麦和夏玉米的生育期、产量及水分利用特征,其中生育期模拟结果的误差在5 d之内,产量、ET和下渗量模拟结果的R2均在0.84以上,表明该模型在华北平原具有较好的适用性;在华北平原地区,冬小麦-夏玉米一年两熟种植模式（M2Y1）年均产量（13 445 kg/hm2）最高,但耗水量（724 mm）也是最大,水分亏损（233 mm）最为严重;一年一熟种植模式（M1Y1）年均耗水量（534 mm）较小,水分亏损量（43 mm）最少,但产量（9 215 kg/hm2）较低;两年三熟种植模式（M3Y2）兼顾产量和耗水,在保证一定产量的前提下减少了耗水量,产量耗水综合效果最好,适合在华北平原推广实行。此外,该研究对栾城站丰水年、平水年和枯水年等不同降水年型下的3种种植模式产量耗水特征进行了对比分析,研究表明在华北平原降水资源对于作物生长有重要意义,年降水量越大,作物产量越高,水分亏损量越少。     

Yield benefits of triticale traits for wheat under current and future climates

Triticale often out-yields wheat in both favourable and unfavourable growing conditions. Observed traits suggested for the higher yields in triticale include greater early vigour, a longer spike formation phase with same duration to flowering, reduced tillering, increased remobilization of carbohydrates to the grain, early vigorous root growth and higher transpiration use efficiency. To quantify the impact of these traits systematically across seasons and contrasting rainfall regions and soil types, these triticale traits were introduced into a wheat model (APSIM-Nwheat). The impact of each individual trait and their full combination was analysed in a simulation experiment for three Mediterranean growing environments, two contrasting soil types and long-term historical weather data. The simulated impact of these traits was compared with measured impacts from a range of field experiments across several environments. Simulated responses of various crop characteristics including yield, were in general similar to responses observed in wheat-triticale comparison field experiments across a large range of growing conditions. The simulation analysis indicated that the yield response to the incorporation of the triticale traits into wheat was positive, in both low and high yielding growing conditions, similar to measured differences, but the simulated benefit was on average lower than the range observed in data of triticale and wheat. This suggests that other traits might also be involved in higher-yielding triticale, or the magnitude of some of the traits may be underestimated in field experiments due to ‘trait by environment’ interactions. The simulation results suggest the highest yield benefit can be achieved from increasing transpiration use efficiency in wheat, but early vigour, remobilization of stem carbohydrates and early root growth also contribute positively to a yield increase in the different growing environments. The yield benefits from the triticale traits increased in the future climate change scenario in particular on soils with high water-holding capacity from contributions of increased early vigour, remobilization of stem carbohydrates and transpiration use efficiency, and remained stable on the lighter soils.