黑龙江省小麦产量数学模型的建立及应用分析

本文根据在黑龙江省31个县、市进行的多年小麦农业生态联合试验,并运用双重组合设计〔1〕原理进行模型挂接,建立了黑龙江省小麦区域性农业生产数学模型,初步揭示了降水、土壤肥力及农肥、化肥对小麦产量的影响程度及相互间的变化规律,并应用模型对小麦产量形成过程进行了模拟和预测．     

Verification of a European corn borer (Lepidoptera: Crambidae) loss equation in the major corn production region of the northeastern United States

Field studies in Pennsylvania and Maryland were conducted during 2000, 2001, and 2002 to test the applicability of published yield loss relationships developed in central Pennsylvania for European corn borer, Ostrinia nubilalis (Hübner), management in warmer, longer season corn, Zea mays L., grain production regions of the northeastern United States. Both isoline hybrids and non-Bt lead hybrids were compared against Bacillus thuringiensis (Bt) hybrids to measure effects of the pest on yield. The European corn borer economic analysis model was used to make site-specific predictions of loss per European corn borer larva for comparison with measured yield loss per larva. Although the model did not predict loss per larva at a field level, it did predict loss at a regional level. The model predicted an overall percentage of yield loss per larva of 2.69+/-0.12% over the region, which was similar to the measured yield loss per larva of 2.66+/-0.59% for isoline hybrids and 3.08+/-0.51% for lead hybrids. The model, on average, provided a good prediction of percentage of yield loss per larva within the climatic zones of 1100-1700 degree-days (DD) (base threshold 12.5 degrees C). Our results suggest that the yield loss relationship developed in Central Pennsylvania, when matched to the timing of third instar second generation European corn borer stalk tunneling is adequate for major corn grain production zones of the northeast United States.     

Response of potato tuber yield to stem injury by European corn borer (Lepidoptera: Crambidae) in the Mid-Atlantic United States.

The response of potato, Solanum tuberosum L., tuber yield to stem injury by European corn borer, Ostrinia nubilalis (Hübner), larvae was investigated in the Mid-Atlantic region of the United States for 3 yr. This response was described for 'Superior', 'Atlantic', and 'Snowden' potato, which are early-season, midseason, and late-season maturing cultivars, respectively. To model the yield/injury relationship, a range of corn borer injury levels was established in the field by augmenting the natural infestation with varying densities of laboratory-reared larvae. Linear and nonlinear regression analyses (plateau and second-order polynomial models) were used to describe the relationship between yield of U.S. No. 1 grade tubers and the percentage of stems injured by corn borer larvae. The maturity of the cultivar did not affect the response of potato yield to stem injury. In nine of 14 experiments, potato tolerated high levels of corn borer injury (55-90% of stems injured) without yield loss, suggesting that control of corn borer may not be necessary. Yet, in one of five Superior tests, in two of four Atlantic tests and in two of five Snowden tests, corn borer injury significantly reduced yield. Of the five data sets in which corn borer injury reduced yield, the plateau model fit two data sets and the quadratic model fit one data set. In two of the three cases, these models accounted for nearly identical amounts of total variation in yield as that accounted for by the linear model. The linear model fit four of the five data sets, but the R2 values were low for three of the four tests (0.10, 0.18, and 0.31). The parameter or parameters that interact with corn borer injury to cause tuber yield reduction should be identified before economic injury levels and thresholds are developed.     

Validation of a nested error component model to estimate damage caused by corn rootworm larvae

The corn rootworm complex (Coleoptera: Chrysomelidae) constitutes a significant threat to maize production in the United States, and more recently, in Europe. We conducted an analysis of readily available field trial data to validate an existing damage function for corn rootworm larvae. We used a nested error component model with unbalanced panel data to describe the relationship between yield loss and root injury caused by these insects. These data were collected by personnel with the Insect Management and Insecticide Evaluation Programme (Department of Crop Sciences, University of Illinois) and represent 19 location‐years. To our knowledge, this is the largest data set used to estimate a damage function for corn rootworm larvae. Unlike many experiments examining the relationship between root injury and yield loss caused by corn rootworm larvae, the data set used for our analysis includes many Bt maize hybrids. Our model suggests that for each node of roots injured by corn rootworm larvae, a yield loss of approximately 15% can be expected. Statistically significant variance components included an effect of location and experimental error. We speculate that variation in weather across experimental sites was the principal factor contributing to the significant effect of location. The substantial experimental error observed for our model highlights the limitations of utilizing a multi‐year, geographically diverse damage function for predicting yield loss because of root injury on a small scale. We discuss major factors contributing to the variance components estimated by our model and suggest techniques for improving future analyses of the damage function for corn rootworm larvae.     

计算机模拟渍水时期及持续时间对春玉米生产及产量的影响

借助田间、水槽试验的结果结合Penning de Vries的MACROS模型建立玉米生长发育与水分动态耦合的模拟模型,通过验证后用于模拟不同渍水时期及持续时间对春玉米生长及产量影响的动态,模拟结果表明在田间全程控制水分为田间持水量90％以下,春玉米孕穗期为渍水危害的敏感期,其次为4－6叶期,在自然降水及土壤状况影响下,春玉米幼苗期4－6叶时为渍水的敏感期,8叶及孕穗期相对较耐渍,在玉米4、6叶期时,渍害造成产量下降的临界期为5天;而在8叶、孕穗期为10－15天,但持续20天的渍水对任何时期的春玉米生长都造成严重的影响,因此在生产中应注意在苗期及孕穗期及时排除田间多余的水分及降低地下水位。     

Corn Residue Supply in the Irrigated Corn Belt

The present study quantifies the profit-maximizing supply of corn stover per unit of land under alternative crop rotations in irrigated cropland with and without limits to irrigation. The model is parameterized based on growing conditions in Nebraska, USA. Our analysis quantifies a trilemma between stover supply, groundwater consumption, and food/feed supply per unit of land. In the absence of water conservation policies, higher stover prices are associated with higher supply and increased irrigation application. If an upper limit is imposed on irrigation at the baseline level (for groundwater conservation purposes), an increase in net revenue from stover sale from $15 to $25/metric ton is associated with an increase in stover supply of 5 metric tons/ha and a 2-metric ton reduction in corn yield under continuous corn. Under corn/soybean rotation, such an increase in net revenue is associated with an increase in stover supply of 10 metric tons/ha, and reductions of 0.5 metric tons in corn yield and 0.7 metric tons in soybean yield.     

Farmers’ Supply Response, Price of Corn Residue, and Its Economic Viability as an Energy Feedstock

Previous economic analyses of energy from corn stover assumed yield reductions from residue removal (without nutrient replacement) and limited or no supply response by farmers to changes in the price of stover. We exploit agronomic and cost information from a randomized block design experiment to model and quantify farmers’ supply response to changes in relative prices of corn stover, corn grain, and soybean. We then couple this supply response with a model of a cost-minimizing processing plant. Results suggest that stover-based energy may be closer to economic viability than previously found. In addition, in areas where reductions in corn yield due to corn monoculture are small, processing plants may find optimal to pay a higher price for stover to induce farmers to adopt continuous corn because it reduces transportation cost. This suggests that such areas may experience changes in their land cover configuration if stover-based energy does become commercially viable.     

黄淮海平原林网保护区夏玉米生长过程的数值模拟

利用改进了产量生态学模型ＳＵＣＲＯＳ,对黄淮海平原林网保护区夏玉米的生长过程进行了数值模拟,并与田间监测资料做了比较,分析了影响夏玉米生长的各种生理、生态学因子,结果表明,改进后的模型能成功模拟夏玉米的生长过程,考虑病虫害、杂草影响后,模型输出的叶面积指数、器官生物量与生长监测资料十分一致。与单作农田比较,由于林网地区小气候条件的改善,夏玉米单产提高６．８％左右。播种密度、播种日期与籽粒产量关系的     

A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level

Understanding large‐scale crop growth and its responses to climate change are critical for yield estimation and prediction, especially under the increased frequency of extreme climate and weather events. County‐level corn phenology varies spatially and interannually across the Corn Belt in the United States, where precipitation and heat stress presents a temporal pattern among growth phases (GPs) and vary interannually. In this study, we developed a long short‐term memory (LSTM) model that integrates heterogeneous crop phenology, meteorology, and remote sensing data to estimate county‐level corn yields. By conflating heterogeneous phenology‐based remote sensing and meteorological indices, the LSTM model accounted for 76% of yield variations across the Corn Belt, improved from 39% of yield variations explained by phenology‐based meteorological indices alone. The LSTM model outperformed least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) approaches for end‐of‐the‐season yield estimation, as a result of its recurrent neural network structure that can incorporate cumulative and nonlinear relationships between corn yield and environmental factors. The results showed that the period from silking to dough was most critical for crop yield estimation. The LSTM model presented a robust yield estimation under extreme weather events in 2012, which reduced the root‐mean‐square error to 1.47 Mg/ha from 1.93 Mg/ha for LASSO and 2.43 Mg/ha for RF. The LSTM model has the capability to learn general patterns from high‐dimensional (spectral, spatial, and temporal) input features to achieve a robust county‐level crop yield estimation. This deep learning approach holds great promise for better understanding the global condition of crop growth based on publicly available remote sensing and meteorological data.     

黑龙江省大豆生长季旱涝时序特征及其对产量的影响

气候变化背景下黑龙江省降水时空分布不均,旱涝转换频繁,不利于黑龙江省的大豆安全生产。为研究黑龙江省大豆生长季旱涝对大豆产量的影响机制,基于黑龙江省60个气象站点1961—2018年逐日降水量数据和同期大豆单产数据,选取标准化降水指数(SPI)为旱涝判识指数,分析黑龙江省大豆生长季旱涝的时序特征,以及大豆不同生育阶段旱涝对其产量的影响。结果表明: 1961—2018年,大豆生长季干旱影响范围整体呈微弱的减小趋势,雨涝影响范围整体呈现弱的增加趋势;而同期干旱和雨涝强度总体均呈微弱的增加趋势,以雨涝强度稍强;干旱和雨涝同时发生的几率占比60.3%。大豆生长季或将进入一个较湿润阶段,2012—2018年间,雨涝的影响范围和发生强度均明显高于干旱,有6年均发生了全域性或区域性雨涝,其中,5年发生中度雨涝。不同区域大豆生长季旱涝对其产量影响程度有所差异,西北部、北部和东部地区雨涝的影响均明显大于干旱的影响,中部地区干旱和雨涝的影响程度相差不大,西南部、南部、东南部地区干旱的影响远大于雨涝的影响。在大豆开花-鼓粒期,旱涝与大豆产量的波动密切相关,西北部、西南部、中部、南部、东南部地区降水正常略偏多对大豆增产有利,但中度及以上雨涝仍会造成大豆减产;北部地区大豆产量波动主要受雨涝影响严重,东部地区干旱和雨涝对大豆产量波动造成的影响程度相似。