黄土丘陵区不同耕作措施下春小麦和豌豆轮作水肥协同效应

为探索水肥协同作用对不同耕作措施下作物产量影响规律,运用在黄土丘陵区率订和验证后的APSIM(agricultural production system simulator)模型,模拟研究区近35年传统耕作(T)、免耕(NT)和免耕覆盖(NTS)措施下轮作小麦/豌豆产量,并采用多元回归分析施氮量(X₁)、休闲期降水量(X₂)和生育期降水量(X₃)对小麦/豌豆模拟产量的水肥协同效应。结果表明,自然降水条件下3因素对不同耕作措施小麦和豌豆产量的贡献率均为:X₃＞X₂＞X₁。生育期降水量和休闲期降水量对产量的贡献率均为:NTS＞NT＞T。T、NT和NTS措施下小麦和豌豆的产量与施氮量均呈开口向下二次抛物线型变化,但小麦最佳施氮量分别为65.0,65.5和44.5 kg/hm²,豌豆的最佳施氮量分别为17.9,18.5和23.8 kg/hm²,并且施氮量对小麦产量的贡献率为:NT＞T＞NTS,而施氮量对豌豆产量的贡献率为:NTS＞NT＞T。在甘肃省定西黄土丘陵区,决定小麦和豌豆产量的关键因素是降水,降水量对免耕覆盖的增产效应最为显著,且3种耕作措施条件下小麦和豌豆对施氮效应有不同的表现。     

秸秆还田对旱作冬小麦后茬土壤水分的影响及其APSIM模拟

模型作为一种作物生长机理模型,可敏感捕捉气候变化、土壤水分变化引致的系统组分响应,适用于降水不确定性地区的农业系统生产预测。为确定APSIM模型对秸秆还田等水土保持耕作拦截夏季降水的模拟功能,在甘肃黄土高原开展了秸秆还田处理,对冬小麦收获后休闲期土壤水分的影响及其APSIM模拟。结果表明：自然降水条件下,免耕+秸秆还田（S）处理下土壤水分蒸发量较休闲裸地（F）、耕作+覆草（TS）及传统耕作（T）降低16.7%~23.9%,裸地休闲或耕作会造成土壤水分流失,秸秆还田的保水作用明显,APSIM模拟可代表71%~92%的土壤水分变化;在人工模拟降水(66 mm·h^-1)情况下,土壤蒸发在2 t·hm^-2(SS)、4 t·hm^-2(LS)秸秆还田量下比裸露休闲地(F)分别降低了27.8%和49.4%,APSIM模拟值解释了96%~99%的土壤水分变化。表明本土化的APSIM模型可以描述研究区土壤水分变化,适用于农业系统研究。     

不同降水年型下水氮调控对小麦产量及生物量的影响

水和氮是影响西北黄土高原雨养农业区粮食生产的主要因素,但其增产效应受降水年型影响明显。本水氮调控试验利用APSIM模型在甘肃省定西市安定区1971—2018年气象数据,分析了不同降水年型下水氮管理对小麦产量和生物量的变异系数、可持续性指数的影响,明确了各年型产量与施氮量、降水量之间的关系。结果表明,模型模拟的小麦产量和生物量的决定系数R2均在0.90以上,一致性指标D均在0.95以上,归一化均方根误差(NRMSE)均在15%以下,表明该模型在研究区具有较好的模型拟合度和适应性。通过二元二次回归方程探讨了其最优产量下的水氮优化组合,在当年年降水总量的基础上,干旱年小麦达潜在最优产量时(3492.6kghm–2),降水需增加39.73%,应施氮182.73 kg hm–2;平水年小麦达潜在最优产量时(4514.5 kg hm–2),降水需增加45.26%,应施氮208.26 kg hm–2;湿润年小麦达潜在最优产量时(4890.3 kg hm–2),降水需增加46.31%,应施氮211.15 kg hm–2。研究结果可为研究区不同降水年型下缓解小麦干旱和养分胁迫,节约化肥资源和农业可持续性发展提供理论依据。     

干旱对太行山山前平原雨养农田产量影响的模拟研究

以多时间尺度标准化降水蒸散指数（SPEI）作为干旱指标,利用APSIM模型（农业生产系统模拟模型）模拟太行山山前平原雨养旱作农田冬小麦-夏玉米近30 a产量变化,分析干旱对作物产量的影响。结果显示：APSIM模型对模拟雨养条件下作物产量具有良好的适用性,玉米产量年际波动较大、变异系数为51.2%,易受降雨因素影响;小麦产量波动相对较为稳定,变异系数为26.4%。干旱指数与作物产量极显著相关（P<0.01）,其中小麦产量与SPEI-3-Apr相关系数达0.79,玉米产量与SPEI-3-Sep相关系数达0.88,适宜干湿状态在0～2之间;所建立的回归方程分别可以解释61.8%的小麦产量变异和87.7%的玉米产量变异。研究表明,SPEI-3-Apr、SPEI-3-Sep可分别作为该地区雨养农田小麦、玉米产量的估计指标。     

基于APSIM模型的地膜覆盖与露地种植条件下旱地小麦灌浆过程模拟

准确模拟小麦籽粒干物质积累过程可为调控小麦生产提供技术支持。基于APSIM模型模拟数据和大田实测数据同步研究旱地冬小麦籽粒干物质积累过程,并在模型验证的基础上分析积温对小麦籽粒干物质积累过程的影响。APSIM-Wheat模型模拟地膜覆盖和露地种植在灌浆期各阶段粒重和籽粒灌浆速率的决定系数大于0.92,归一化均方根误差小于16.25%,有效性指数大于0.91,表明APSIM-Wheat模型在研究区模拟小麦灌浆过程具有较好的拟合度和适应性。地膜覆盖种植优于露地种植,两年度模型模拟地膜覆盖籽粒干物质日积累量模拟值高于露地5.23%,田间实测地膜覆盖的最大灌浆速率平均高于露地10.46%,快增期平均灌浆速率高于露地13.68%,千粒重平均高于露地2.50%。Logistic进一步分析表明,地膜覆盖种植有利于提前最大灌浆速率的时间,减少灌浆渐增期、缓增期的持续时间;其次,积温通过影响籽粒干物质日积累量进而影响灌浆速率,在灌浆期大气日积温、土壤日积温与籽粒干物质日积累量均呈极显著正相关关系。     

多模式集合模拟气候变化对玉米产量的影响

气候模式驱动作物模型是气候变化影响评估的主要手段。但是,单一气候模式输出和作物模型的结构差异使得研究结果存在不确定性。多模式集合的概率预估可以有效减少研究结果的不确定性。为此,本文利用1981—2009年东北地区海伦、长岭、本溪3地区农业气象站的历史气象资料和玉米作物数据,分别建立了作物统计模型并验证了APSIM机理模型在研究区域的适用性。在此基础上,与CMIP5在RCP4.5情景下的8个全球模式结合,尝试基于多模式集合评估了未来2010—2039年时段和2040—2069年时段气候变化对玉米产量的可能影响(相对于1976—2005年基准时段)。研究结果表明,APSIM模型对玉米生长发育和产量形成有很好的模拟能力。玉米生育期的模拟误差(RMSE)为3~4 d,产量的RMSE为0.6~0.8 t?hm~(-2)。建立的产量统计模型表明,玉米出苗阶段(5月中旬)的温度增加对产量增加有积极作用,而开花到成熟阶段(7月中旬到9月上旬)的温度和降水的增加、光照的不足均不利于产量增加。与1976—2005年基准时段相比,气候因素影响下2010—2039年玉米产量减少3.8%(海伦)~7.4%(本溪),减产的概率为64%(长岭)~73%(本溪);2040—2069年时段减产6.4%(海伦)~10.5%(本溪),减产的概率为74%(海伦)~83%(本溪)。未来2010—2039年时段和2040—2069年时段基于机理模型模拟的产量降低分别为6.6%(海伦)~8.9%(本溪)和9.7%(海伦)~13.7%(本溪),均高于相应时段基于统计模型得到的0.9%(海伦)~6.0%(本溪)和2.0%(长岭)~7.3%(本溪)减产结果。     

Approaches to modular model development

One of the main goals of the International Consortium for Agricultural Systems Applications (ICASA) is to advance the development and application of compatible and complementary models, data and other systems analysis tools. To help reach that goal, it will adopt and recommend modular approaches that facilitate more systematic model development, documentation, maintenance, and sharing. In this paper, we present criteria and guidelines for modules that will enable them to be plugged into existing models to replace an existing component or to add a new one with minimal changes. This will make it possible to accept contributions from a wide group of modellers with specialities in different disciplines. Two approaches to modular model development have emerged from different research groups in ICASA. One approach was developed by extending the programming methods used in the Fortran Simulation Environment developed in The Netherlands. This method is being used in revisions of some of the Decision Support Systems for Agrotechnology Transfer crop models. A simple example of this approach is given in which a plant growth module is linked with a soil water balance module to create a crop model that simulates growth and yield for a uniform area. The second approach has been evolving within the Agricultural Production Systems Research Unit group in Australia. This approach, implemented in software called Agricultural Production Systems Simulator, consists of plug-in/pull-out modules and an infrastructure for inter-module communication. The two approaches have important similarities, but also differ in implementation details. In both cases, avoiding reliance on any particular programming language has been an important design criterion. By comparing features of both approaches, we have started to develop a set of recommendations for module design that will lead to a ‘toolkit’ of modules that can be shared throughout the ICASA network.     

Using a systems modeling approach to improve soil management and soil quality

Soils provide the structural support, water and nutrients for plants in nature and are considered to be the foundation of agriculture production. Improving soil quality and soil health has been advocated as the goal of soil management toward sustainable agricultural intensification. There have been renewed efforts to define and quantify soil quality and soil health but establishing a consensus on the key indicators remains difficult. It is argued that such difficulties are due to the former ways of thinking in soil management which largely focus on soil properties alone. A systems approach that treats soils as a key component of agricultural production systems is promoted. It is argued that soil quality must be quantified in terms of crop productivity and impacts on ecosystems services that are also strongly driven by climate and management interventions. A systems modeling approach captures the interactions among climate, soil, crops and management, and their impacts on system performance, thus helping to quantify the value and quality of soils. Here, three examples are presented to demonstrate this. In this systems context, soil management must be an integral part of systems management practices that also include managing the crops and cropping systems under specific climatic conditions, with cognizance of future climate change.     

Productivity and residual benefits of grain legumes to sorghum under semi-arid conditions in south-western Zimbabwe: Unravelling the effects of water and nitrogen using a simulation model

The APSIM model was used to assess the impact of legumes on sorghum grown in rotation in a nutrient-limited system under dry conditions in south-western Zimbabwe. An experiment was conducted at Lucydale, Matopos Research Station, between 2002 and 2005. The model was used to simulate soil and plant responses in the experiment. Sequences of cowpea (Vigna unguiculata), pigeonpea (Cajanus cajan), groundnut (Arachis hypogaea) and sorghum (Sorghum bicolor) were used in the rotations. Legumes accumulated up to 130 kg of N ha⁻¹ which was potentially available for uptake by sorghum in the following season. The APSIM model predicted total biomass, grain and N yields of the legume phase within the experimental error and performed well in predicting sorghum yield and N supplied in the rotation after cowpea and groundnut. The model generally under-predicted sorghum total biomass and grain yield after pigeonpea. Observed patterns of crop water use, evaporative losses during the dry season and re-charge of soil profile at the start of the rainy season were generally well predicted by the model. An assessment of output on sorghum N and water stresses in the rotation indicated that the legume–cereal rotation is more driven by soil nitrogen availability than water availability even under semi-arid conditions. Further legume–cereal rotation analysis using the model will assist in the understanding of other processes in the rotations in dry environments.