利用模型对黑土条件下玉米生长和土壤碳氮循环的模拟研究
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摘要
黑土是吉林省主要的土壤类型之一,种植作物以玉米为主,近年来的不合理耕作和施肥致使黑土退化加剧,环境污染风险提高。随着模型模拟技术的发展,利用模拟手段对作物生长和土壤碳氮循环的研究被认是行之有效的方法。
农业技术转化决策系统(DSSAT)是世界上应用最广泛的模型之一,但如今仍没有在黑土上的研究报道,特别是应用作物生长模拟校验后的参数同时去模拟土壤碳氮循环在国内并未见报道。因此,本文应用DSSAT4.5模型通过单独设置的田间试验和长期定位试验,对吉林省黑土区域内主要农作物玉米的生产力和玉米田土壤碳氮循环进行模拟研究。通过对主要输出变量(作物生长、产量、作物氮吸收、土壤氮)的敏感性分析,找出影响模型输出的敏感性参数。同时探讨不同栽培技术、不同气候、不同氮肥施肥用量等对生物量,产量和土壤碳、氮变化趋势的影响。从而筛选出适合我国黑土地区玉米稳产高产、提高土壤肥力和降低环境污染风险的综合农艺措施,为今后制定农业发展战略、政府决策、土地利用和农业种植业的调整提供有力手段。DSSAT4.5 CERES-Maize模型按本文提供的校验和敏感性分析方法,还可以应用于其他作物、土壤和地区,也可以用于不同水肥管理条件下的长期预测预报,为农业技术推广部门提供模型和技术支持。同时也为DSSAT4.5的出版发行和扩大应用范围提供实验依据和数据支持。本论文几年的研究结果可以归纳如下：
对DSSAT模型运行原理的解析是正确设置模型参数的关键,对正确设计田间实验和实验室样品分析是获得校验分析数据的关键；有效搜集过去的长期定位试验数据是进行模拟长期定位／轮作试验的前提。应用DSSAT作物系统模型模拟“田间”作物生长和土壤CN循环是开展区域模拟的关键。模型敏感性分析和校验分析是模型应用研究的常用方法。在阅读同类研究论文的基础上,系统学习这两种方法是校验和应用模型的必要手段,而了解DSSAT模型中的作物系统模型,土壤模型和水分运动模型的基本理论和数量关系是应用模型和修改模型的基础。
在应用和引进DSSAT模型中,一个很关键的环节是确定模型的输入参数对产量和土壤养分的敏感性,因为在一个地区的敏感性并不能保证在其它地区具有同样的影响。正因为如此,本文对DSSAT模型的4个主要旱田作物(玉米、大豆、小麦、马铃薯)的农业管理参数进行了系统的敏感性分析,得到了一致的结果：即作物生长和土壤无机氮都受到播种期、密度、施氮肥量和时间的影响,这和实验研究结果基本一致。
应用DSSAT CERES-Maize模型,对2008年的田间试验玉米生长进行了系统的模拟分析(叶面积指数,地上干物质,籽粒重量),应用当地平均产量和生长期对玉米品种参数进行校验。模拟结果的综合分析表明：玉米提前播种8-10d比正常播种减产大约10%。玉米产量随播种密度呈现抛物线趋势；即在低密度下,产量曲线递增,但是当密度大于5 plant m-2时,产量增加平缓。产量和氮肥施用量呈典型的效应递减曲线,最佳施氮量勾200-240 kg N hm-2。最佳追肥时间为6月15日至6月28日。本研究证明DSSAT模型能够用于中国其它地区的玉米生长模拟,并且,本研究建立的敏感性分析方法能够用于其它作物,如水稻和小麦。
基于2009年的田间试验,本文用DSSAT CERES-Maize作物模型和DSSAT-CENTURY土壤模型对玉米田的土壤C、N循环进行了系统的模拟分析；即选择作物栽培措施、N肥用量、土壤供氮和秸杆还田对玉米生长、氮吸收、土壤供氮能力以及对有机碳氮的生态平衡进行了综合模拟和敏感性分析,从而筛选维持玉米高产和保持黑土碳氮平衡的管理措施。2008和2009年两年敏感性分析模拟结果的综合分析表明：在玉米目标产量12000-15000 kg hm-2条件下,化肥最佳施N量为200-240 kg N hm-2。在该N肥用量下,玉米地上氮吸收为250-290 kg N hm-2;其中120-140 kg N hm-2来自土壤N(播前土壤提供约50 kg N hm-2,生长期内土壤提供的矿化氮约70-90 kg N hm-2),130-150 kg N hm"2来自肥料N。提高氮肥用量(250-420 kg N hm-2)导致土壤残留氮明显增加(63-183 kg N hm-2)。延迟追肥时间(晚于6月28日)同样导致土壤残留氮增加。当玉米秸杆还田量超过6000 kg hm-2时,土壤活性有机碳、氮可以维持当年的供需平衡。建议在吉林省中部地区黑土玉米带,控制化肥施氮量为200-240 kg N hm-2,适时追肥,秸杆还田量在6000 kg hm-2以上是确保高产和维持土壤生态和养分平衡的关键措施之一。
应用DSSAT轮作模型(Sequence model),本文对1990-2007年的公主岭长期定位试验进行17年的玉米连作模拟分析；即每年5-9月种植玉米,9月-第二年年4月为休闲。设计了不施肥(N0),单施化肥165kg N hm-2(N165)和有机(112kg N hm-2)+无机肥料(165kg N hm-2)配施(M112+N165)三个处理,来研究不同施肥对连作玉米的产量和土壤有机C,N和无机氮,氮淋溶的影响。模拟和测量结果都表明：玉米连作情况下的籽粒产量,耕层土壤有机碳氮含量反映了随气候变化的趋势(既干旱年产量明显降低),充分说明了水分,温度和光照是影响作物生长的关键因素。模型的模拟产量和测量结果呈显著正相关(R2=0.69)。模拟的土壤有机CN结果表明：单施氮肥N165kg N hm-2,土壤氮淋溶增加,最高可达150kg N hm-2。但是同时发现在单施N165条件下,100%秸杆还田能够保持17年土壤中活性有机碳,氮平衡。有机肥和化肥配施(M112+N165能显著提高土壤活性有机碳、氮贮量,并且降低土壤硝态氮淋溶。
Black soil(Mollisols) is a typical soil and maize (Zea mays L.) is the main crop in Jilin province. In recent year, soil degradation increased with over application of fertilizers and improper tillage and this consequently increase the risk of environmental pollution. It is regarded a feasible method to study crop growth and soil C and N cycling using modeling approach.
Decision Support Systems for Agro-technology Transfer (DSSAT) is one of the most widely applied models globally, but there was no report that the DSSAT model has been applied in the Black soil region in China, especially there was no report on using the calibrated crop model and cultivar parameters to simulate soil C N cycling in China. Therefore, this thesis arm at simulating maize growth and soil C and N cycling in the Black soil field in Jilin province using the DSSAT 4.5 model with specially designed field and long term experiments. Through the sensitivity analysis of main output variables (crop growth, yield, crop N uptake, and soil mineral N), sensitive parameters was found effectively. At the same time, the effects of crop managements, climate and different fertilizer N application levels on yield and soil C N dynamics were studied. The best management practices were selected to maintain high yield and soil fertility, and to reduce environmental pollution risk. The research results will provided useful methods for agricultural development, decision making, land use policy and cropping adjustment in the future. The evaluation and sensitivity methods for the DSSAT 4.5 CERES-Maize model can be applied to other crop, soil and region. The methods can also be used to forecast long term yield potential by agricultural extension sectors. Meanwhile, our researches provide useful dataset to support DSSAT 4.5 application. The main research fruits can be summarized as below.
Understanding the DSSAT model and its working principle is the key step for parameter calibration. Designing a good field experiment and using a correct lab analysis method are the key step for obtaining model evaluation dataset; effectively collecting long term field experimental data is a pre-condition for applying DSSAT model in sequence/rotation analysis of the long term field experiment. Applying the DSSAT crop system model to simulate field crop growth and soil C N cycling is the key step for regional simulation. Sensitivity analysis and evaluation methods are mostly used method for model application and evaluation. After literature review, systematically learn the two methods are necessary to successfully evaluate a model. Learning quantitative theory of crop growth, soil C N dynamics and soil water balance lay the foundation of model evaluation and modification.
When applying the DSSAT model to a new system, a key step is to determine model output sensitivity to input parameters because some sensitive parameter in some region may not be sensitive in other region. For this reason, this study carried out a systematically sensitivity analysis to the main crop management parameters of 4 main dry land crops (Maize, soybean, spring wheat and potato). The results showed agreements for all crops; crop growths and soil mineral N were sensitive to sowing date, density, fertilizer N application rates and times. These results were in agreement with field experimental results.
DSSAT CERES-Maize model was applied to our 2008 field experiment with Maize in Black soil to simulate crop growth (LAI, aboveground dry weight and grain yield). Maize cultivar parameters were calibrated using average field data, and the simulation results follow. Planting 8 to 10 days earlier resulted in maize yield reductions of 10%. Yields increased curveilinearly with the increases in plant density in the low to mid range (<5 plants m-2), and levelled off when the density reached 5 plants m-2. Yield and fertilizer N rate followed a diminishing yield pattern with the maximum yield being obtained at a fertilizer N rate of 200-250 kg N hm-2. The optimum fertilizer dress date was June 15-28. The research results also showed that DSSAT model can be used to simulate maize growth in other region of China and the sensitivity method that was established in this research can be applied to other crops, such as rice and wheat.
Using 2009 field experiment, DSSAT CERES-Maize model and DSSAT CENTURY soil model were used to simulate crop N uptake and soil C and N dynamics to crop management parameters (sowing date, density and fertilizer N application rates and dates). The results showed that maize targeted yield of 12000-15000 kg hm-2 can be achieved by applying 200 to 240 N kg hm-2. Under this fertilization., the simulated N uptake (aboveground) ranged 250 to 290 N kg hm-2, including 120 to 140 N kg hm-2 from soil N and 130 to 150 N kg hm-2 from fertilizer N. Higher fertilizer N rates of 250 to 420 N kg hm-2 resulted in the increased residual soil N of 63 to 183 N kg hm-2 at harvest. Delaying dress date of N fertilizer (after June 28) also resulted in the increases of residual soil mineral N. When applying up to 6000 kg hm-2 crop residue to the field, simulated soil active organic C and N maintained supply/demand balance during growing season. The study recommended that 200 to 240 kg N hm-2 fertilizer N and up to 6000 kg hm-2 crop residue should be applied to maize field to achieve the targeted maize yield and maintain soil organic C and N balance in black soil zone of Jilin province, China.
DSSAT Sequence model was used to simulate 17 years long term maize continuous field experiments (1990-2007) in Gongzhuling, Jilin China. Three N levels (treatments) were used in the simulation; no fertilizer N (NO),165 kg fertilizer N hm-2 (N165) and Organic N(112 kg N hm-2) plus fertilizer N 165 kg N hm-2) (M112+N165). Both measured and simulated results showed that maize yields, soil C and N changed with time, and the trends reflect climate changes (i.e., yields was lower in drought years). This proved that vater, temperature and solar radiation are key factors for crop growth. There was a significant correlation between the simulated and the measured maize yield (R2=0.69). Simulated soil organic C and N showed that under single fertilizer N165 kg N hm-2, soil N leaching increased up to 150 kg N hm-2 at harvest. It was also found that under N165 treatment,100% crop residual return can maintain soil active organic C and N balance during 17 year period. Under M112+N165 treatment,100% crop residual return can increase soil active organic C and N significantly and reduce soil mineral N leaching.
农业技术转化决策系统(DSSAT)是世界上应用最广泛的模型之一,但如今仍没有在黑土上的研究报道,特别是应用作物生长模拟校验后的参数同时去模拟土壤碳氮循环在国内并未见报道。因此,本文应用DSSAT4.5模型通过单独设置的田间试验和长期定位试验,对吉林省黑土区域内主要农作物玉米的生产力和玉米田土壤碳氮循环进行模拟研究。通过对主要输出变量(作物生长、产量、作物氮吸收、土壤氮)的敏感性分析,找出影响模型输出的敏感性参数。同时探讨不同栽培技术、不同气候、不同氮肥施肥用量等对生物量,产量和土壤碳、氮变化趋势的影响。从而筛选出适合我国黑土地区玉米稳产高产、提高土壤肥力和降低环境污染风险的综合农艺措施,为今后制定农业发展战略、政府决策、土地利用和农业种植业的调整提供有力手段。DSSAT4.5 CERES-Maize模型按本文提供的校验和敏感性分析方法,还可以应用于其他作物、土壤和地区,也可以用于不同水肥管理条件下的长期预测预报,为农业技术推广部门提供模型和技术支持。同时也为DSSAT4.5的出版发行和扩大应用范围提供实验依据和数据支持。本论文几年的研究结果可以归纳如下：
对DSSAT模型运行原理的解析是正确设置模型参数的关键,对正确设计田间实验和实验室样品分析是获得校验分析数据的关键；有效搜集过去的长期定位试验数据是进行模拟长期定位／轮作试验的前提。应用DSSAT作物系统模型模拟“田间”作物生长和土壤CN循环是开展区域模拟的关键。模型敏感性分析和校验分析是模型应用研究的常用方法。在阅读同类研究论文的基础上,系统学习这两种方法是校验和应用模型的必要手段,而了解DSSAT模型中的作物系统模型,土壤模型和水分运动模型的基本理论和数量关系是应用模型和修改模型的基础。
在应用和引进DSSAT模型中,一个很关键的环节是确定模型的输入参数对产量和土壤养分的敏感性,因为在一个地区的敏感性并不能保证在其它地区具有同样的影响。正因为如此,本文对DSSAT模型的4个主要旱田作物(玉米、大豆、小麦、马铃薯)的农业管理参数进行了系统的敏感性分析,得到了一致的结果：即作物生长和土壤无机氮都受到播种期、密度、施氮肥量和时间的影响,这和实验研究结果基本一致。
应用DSSAT CERES-Maize模型,对2008年的田间试验玉米生长进行了系统的模拟分析(叶面积指数,地上干物质,籽粒重量),应用当地平均产量和生长期对玉米品种参数进行校验。模拟结果的综合分析表明：玉米提前播种8-10d比正常播种减产大约10%。玉米产量随播种密度呈现抛物线趋势；即在低密度下,产量曲线递增,但是当密度大于5 plant m-2时,产量增加平缓。产量和氮肥施用量呈典型的效应递减曲线,最佳施氮量勾200-240 kg N hm-2。最佳追肥时间为6月15日至6月28日。本研究证明DSSAT模型能够用于中国其它地区的玉米生长模拟,并且,本研究建立的敏感性分析方法能够用于其它作物,如水稻和小麦。
基于2009年的田间试验,本文用DSSAT CERES-Maize作物模型和DSSAT-CENTURY土壤模型对玉米田的土壤C、N循环进行了系统的模拟分析；即选择作物栽培措施、N肥用量、土壤供氮和秸杆还田对玉米生长、氮吸收、土壤供氮能力以及对有机碳氮的生态平衡进行了综合模拟和敏感性分析,从而筛选维持玉米高产和保持黑土碳氮平衡的管理措施。2008和2009年两年敏感性分析模拟结果的综合分析表明：在玉米目标产量12000-15000 kg hm-2条件下,化肥最佳施N量为200-240 kg N hm-2。在该N肥用量下,玉米地上氮吸收为250-290 kg N hm-2;其中120-140 kg N hm-2来自土壤N(播前土壤提供约50 kg N hm-2,生长期内土壤提供的矿化氮约70-90 kg N hm-2),130-150 kg N hm"2来自肥料N。提高氮肥用量(250-420 kg N hm-2)导致土壤残留氮明显增加(63-183 kg N hm-2)。延迟追肥时间(晚于6月28日)同样导致土壤残留氮增加。当玉米秸杆还田量超过6000 kg hm-2时,土壤活性有机碳、氮可以维持当年的供需平衡。建议在吉林省中部地区黑土玉米带,控制化肥施氮量为200-240 kg N hm-2,适时追肥,秸杆还田量在6000 kg hm-2以上是确保高产和维持土壤生态和养分平衡的关键措施之一。
应用DSSAT轮作模型(Sequence model),本文对1990-2007年的公主岭长期定位试验进行17年的玉米连作模拟分析；即每年5-9月种植玉米,9月-第二年年4月为休闲。设计了不施肥(N0),单施化肥165kg N hm-2(N165)和有机(112kg N hm-2)+无机肥料(165kg N hm-2)配施(M112+N165)三个处理,来研究不同施肥对连作玉米的产量和土壤有机C,N和无机氮,氮淋溶的影响。模拟和测量结果都表明：玉米连作情况下的籽粒产量,耕层土壤有机碳氮含量反映了随气候变化的趋势(既干旱年产量明显降低),充分说明了水分,温度和光照是影响作物生长的关键因素。模型的模拟产量和测量结果呈显著正相关(R2=0.69)。模拟的土壤有机CN结果表明：单施氮肥N165kg N hm-2,土壤氮淋溶增加,最高可达150kg N hm-2。但是同时发现在单施N165条件下,100%秸杆还田能够保持17年土壤中活性有机碳,氮平衡。有机肥和化肥配施(M112+N165能显著提高土壤活性有机碳、氮贮量,并且降低土壤硝态氮淋溶。
Black soil(Mollisols) is a typical soil and maize (Zea mays L.) is the main crop in Jilin province. In recent year, soil degradation increased with over application of fertilizers and improper tillage and this consequently increase the risk of environmental pollution. It is regarded a feasible method to study crop growth and soil C and N cycling using modeling approach.
Decision Support Systems for Agro-technology Transfer (DSSAT) is one of the most widely applied models globally, but there was no report that the DSSAT model has been applied in the Black soil region in China, especially there was no report on using the calibrated crop model and cultivar parameters to simulate soil C N cycling in China. Therefore, this thesis arm at simulating maize growth and soil C and N cycling in the Black soil field in Jilin province using the DSSAT 4.5 model with specially designed field and long term experiments. Through the sensitivity analysis of main output variables (crop growth, yield, crop N uptake, and soil mineral N), sensitive parameters was found effectively. At the same time, the effects of crop managements, climate and different fertilizer N application levels on yield and soil C N dynamics were studied. The best management practices were selected to maintain high yield and soil fertility, and to reduce environmental pollution risk. The research results will provided useful methods for agricultural development, decision making, land use policy and cropping adjustment in the future. The evaluation and sensitivity methods for the DSSAT 4.5 CERES-Maize model can be applied to other crop, soil and region. The methods can also be used to forecast long term yield potential by agricultural extension sectors. Meanwhile, our researches provide useful dataset to support DSSAT 4.5 application. The main research fruits can be summarized as below.
Understanding the DSSAT model and its working principle is the key step for parameter calibration. Designing a good field experiment and using a correct lab analysis method are the key step for obtaining model evaluation dataset; effectively collecting long term field experimental data is a pre-condition for applying DSSAT model in sequence/rotation analysis of the long term field experiment. Applying the DSSAT crop system model to simulate field crop growth and soil C N cycling is the key step for regional simulation. Sensitivity analysis and evaluation methods are mostly used method for model application and evaluation. After literature review, systematically learn the two methods are necessary to successfully evaluate a model. Learning quantitative theory of crop growth, soil C N dynamics and soil water balance lay the foundation of model evaluation and modification.
When applying the DSSAT model to a new system, a key step is to determine model output sensitivity to input parameters because some sensitive parameter in some region may not be sensitive in other region. For this reason, this study carried out a systematically sensitivity analysis to the main crop management parameters of 4 main dry land crops (Maize, soybean, spring wheat and potato). The results showed agreements for all crops; crop growths and soil mineral N were sensitive to sowing date, density, fertilizer N application rates and times. These results were in agreement with field experimental results.
DSSAT CERES-Maize model was applied to our 2008 field experiment with Maize in Black soil to simulate crop growth (LAI, aboveground dry weight and grain yield). Maize cultivar parameters were calibrated using average field data, and the simulation results follow. Planting 8 to 10 days earlier resulted in maize yield reductions of 10%. Yields increased curveilinearly with the increases in plant density in the low to mid range (<5 plants m-2), and levelled off when the density reached 5 plants m-2. Yield and fertilizer N rate followed a diminishing yield pattern with the maximum yield being obtained at a fertilizer N rate of 200-250 kg N hm-2. The optimum fertilizer dress date was June 15-28. The research results also showed that DSSAT model can be used to simulate maize growth in other region of China and the sensitivity method that was established in this research can be applied to other crops, such as rice and wheat.
Using 2009 field experiment, DSSAT CERES-Maize model and DSSAT CENTURY soil model were used to simulate crop N uptake and soil C and N dynamics to crop management parameters (sowing date, density and fertilizer N application rates and dates). The results showed that maize targeted yield of 12000-15000 kg hm-2 can be achieved by applying 200 to 240 N kg hm-2. Under this fertilization., the simulated N uptake (aboveground) ranged 250 to 290 N kg hm-2, including 120 to 140 N kg hm-2 from soil N and 130 to 150 N kg hm-2 from fertilizer N. Higher fertilizer N rates of 250 to 420 N kg hm-2 resulted in the increased residual soil N of 63 to 183 N kg hm-2 at harvest. Delaying dress date of N fertilizer (after June 28) also resulted in the increases of residual soil mineral N. When applying up to 6000 kg hm-2 crop residue to the field, simulated soil active organic C and N maintained supply/demand balance during growing season. The study recommended that 200 to 240 kg N hm-2 fertilizer N and up to 6000 kg hm-2 crop residue should be applied to maize field to achieve the targeted maize yield and maintain soil organic C and N balance in black soil zone of Jilin province, China.
DSSAT Sequence model was used to simulate 17 years long term maize continuous field experiments (1990-2007) in Gongzhuling, Jilin China. Three N levels (treatments) were used in the simulation; no fertilizer N (NO),165 kg fertilizer N hm-2 (N165) and Organic N(112 kg N hm-2) plus fertilizer N 165 kg N hm-2) (M112+N165). Both measured and simulated results showed that maize yields, soil C and N changed with time, and the trends reflect climate changes (i.e., yields was lower in drought years). This proved that vater, temperature and solar radiation are key factors for crop growth. There was a significant correlation between the simulated and the measured maize yield (R2=0.69). Simulated soil organic C and N showed that under single fertilizer N165 kg N hm-2, soil N leaching increased up to 150 kg N hm-2 at harvest. It was also found that under N165 treatment,100% crop residual return can maintain soil active organic C and N balance during 17 year period. Under M112+N165 treatment,100% crop residual return can increase soil active organic C and N significantly and reduce soil mineral N leaching.
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