Application of low altitude remote sensing (LARS) platform for monitoring crop growth and weed infestation in a soybean plantation

Crop growth and weed infestation in a soybean field were monitored by processing low altitude remote sensing (LARS) images taken from crane-mounted and unmanned radio controlled helicopter-mounted platforms. Images were taken for comparison between true color (R?CG?CB) and color-infrared (NIR) digital cameras acquired at different heights above ground. All LARS images were processed to estimate vegetation-indices for distinguishing stages of crop growth and estimating weed density. LARS images from the two platforms (low-dynamic and high-dynamic) were evaluated. It was found that crane-mounted RGBC and NIRC platforms resulted in better quality images at lower altitudes (<10?m). This makes the crane-mounted platform an attractive option in terms of specific low altitude applications at an inexpensive cost. Helicopter-mounted RGBH and NIRH images were found suitable at altitudes >10?m. Comparison of NDVIC and NDVIH images showed that NDVI values at 28 DAG (days after germination) exhibited a strong relationship with altitudes used to capture images (R ² of 0.75 for NDVIC and 0.79 for NDVIH). However, high altitudes (>10?m) decreased NDVI values for both systems. Higher R ² values (??0.7) were also obtained between indices estimated from crane-and helicopter-mounted images with those obtained using an on-ground spectrometer, which showed an adequate suitability of the proposed LARS platform systems for crop growth and weed infestation detection. Further, chlorophyll content was well correlated with the indices from these images with high R ² values (>0.75) for 7, 14, 21 and 28 DAG.     

A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring

Image-based remote sensing is one promising technique for precision crop management. In this study, the use of an ultra light aircraft (ULA) equipped with broadband imaging sensors based on commercial digital cameras was investigated to characterize crop nitrogen status in cases of combined nitrogen and water stress. The acquisition system was composed of two Canon^? EOS 400D digital cameras: an original RGB camera measuring luminance in the Red, Green and Blue spectral bands, and a modified camera equipped with an external band-pass filter measuring luminance in the near-infrared. A 5?month experiment was conducted on a sugarcane (Saccharum officinarum) trial consisting of three replicates. In each replicate, two sugarcane cultivars were grown with two levels of water input (rainfed/irrigated) and three levels of nitrogen (0, 65 and 130?kg/ha). Six ULA flights, coupled with ground crop measurements, took place during the experiment. For nitrogen status characterisation, three indices were tested from the closed canopy: the normalised difference vegetation index (NDVI), the green normalised difference vegetation index (GNDVI), and a broadband version of the simple ratio pigment index (hereafter referred to as the SRPI_b), calculated from the ratio between blue and red bands of the digital camera. The indices were compared with two nitrogen crop variables: leaf nitrogen content (N_L) and canopy nitrogen content (N_C). SRPI_b showed the best correlation (R ²?=?0.7) with N_L, independently of the water and the N treatment. NDVI and GNDVI were best correlated with N_C values with correlation coefficients of 0.7 and 0.64 respectively, but the regression coefficients were dependent on the water and N treatment. These results showed that SRPI_b could characterise the nitrogen status of sugarcane crop, even in the case of combined stress, and that such acquisition systems are promising for crop nitrogen monitoring.     

Coffee ripeness monitoring using a UAV-mounted low-cost multispectral camera

Coffee beverage quality is highly correlated with the degree of fruit ripeness. In this sense, monitoring fruit ripeness is of utmost importance for harvest planning and, especially for obtaining high-quality beverages. Currently, this process is carried out through manual counts of unripe fruits, which is laborious and limited to a few plants within the field. This study aimed at evaluating the potential of a low-cost multispectral camera for coffee ripeness monitoring in the Zona da Mata region of Minas Gerais State, Brazil. For that, five fields of Arabica coffee with distinct characteristics were evaluated. During the coffee ripeness period, four flights were carried using a Phantom 4 Pro quadcopter equipped with a Mapir Survey 3W camera for imagery acquisition. After that, nine vegetation indices (VIs) were obtained. For the same dates, the percentage of unripe fruits was obtained using an irregular grid in all fields. The data was split into two ripeness classes: suitable for harvest (R) with?<?30% of unripe fruits; and not suitable for harvest (U), with?>?30% of unripe fruits. Then, a principal component analysis was used to infer the importance of the VIs to discriminate plants with unripe fruits from those with ripe fruits. The first two principal components explained?>?75% of the variance in the datasets from all coffee fields. The VIs were able to discriminate the ripeness classes (U and R) in most fields; however, their performance was directly influenced by the crop yield and canopy volume.

     

小型温室环境监控系统的研究

日光温室可以为作物提供最佳的生长环境,使作物生长不受时间和地域的限制。设计了一种小型温室环境调控系统,实现可调可控适宜作物生长的温室环境。该系统由环境控制器、作物生长影像仪和上位机软件组成。控制器采用PLC实现,通过控制器采集空气温度,空气湿度,土壤温度和土壤水分等环境信息,控制加热器、加湿器、卷帘、湿帘、水泵、风机、微喷、通风和补光灯等执行设备,达到现场调控温室环境的目的;作物生长影像仪通过定点摄像头扑捉作物生长图像,观察作物生长态势;上位机软件主要用于实现远程控制、历史数据查询与数据导出等功能。该系统经过试验验证,可以实现温室环境的温湿度调控。     

Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean

Leaf photosynthesis and rhizobial nitrogen fixation are the two metabolic processes of utmost importance to legume growth and development. As these processes are closely related to each other, measuring of leaf chlorophyll content can provide information on the nodulation and nitrogen fixation status of crop plants. In the present investigation, a number of soybean breeding lines consisting of near-isogenic families which are genetically segregating for the nodulation trait were utilized in field experiments carried out across three growing seasons at Vienna, Austria. For phenotyping leaf chlorophyll content, the Minolta SPAD spectrometer was applied in parallel to a simple leaf digital image analysis procedure based on a commercial digital still camera. The main objectives of the research included the comparison of SPAD metering and image analysis for determination of chlorophyll content, phenotyping of the soybean nodulation vs. non-nodulation characteristic with respect to leaf, agronomic and seed traits, and relating both chlorophyll and image analysis data to seed quality characteristics. Nodulating and non-nodulating soybean lines significantly differed in chlorophyll content from the V5 (five leaves fully developed) soybean developmental stage onwards. Apart from chlorophyll content, leaf size, plant height, number of pods per plant, 1000-seed weight, and seed protein and oil content were also affected by nodulation type. The chlorophyll content of soybean leaves as determined by SPAD metering was significantly correlated (r = −0.937) to the green color value (RGB color model) of leaf image analysis at the R3 (beginning of pod growth) soybean developmental stage. Both chlorophyll content and leaf image analysis parameters were correlated to 1000-seed weight, seed protein and seed oil content. Thus, it appears that these leaf parameters related to photosynthesis and nitrogen fixation could be utilized to determine the nitrogen status of a soybean crop and subsequently in forecasting seed quality parameters of the harvest product.     

作物科学中的环境型鉴定（Envirotyping）及其应用

全球气候变化正在对地球的环境产生日益重要的影响,而作物生产取决于作物基因型和环境之间的相互作用。利用现代生物学技术可以在分子水平上精细解析作物的基因型及其各个遗传组分对于表现型的贡献;然而对于作物具有重大影响的环境因子,目前,只能通过作物在不同环境下的表现型来推测其综合作用,或对整个试验区的个别环境因子进行对比分析,因而无法对各类环境因子进行深入剖析。笔者首次在国际上提出了环境型鉴定概念,并创造了一个英文新词etyping来表示。在本文中,环境型鉴定用envirotyping来代替。环境型（envirotype）用来描述包括所有影响作物不同生长发育阶段的内部和外部环境因子及其各种组合,外部环境因子主要包括水、肥、气、热、光、土壤、耕作制度和伴生生物等;而环境型鉴定用来表述对所有环境因子的解析和测定。环境型信息可以通过多种方式采集。作物多年多点区域试验积累了大量相关试验点的环境数据;地理信息系统（geographic information system,GIS）和土壤信息系统积累了大量气候、天气、土壤的数据;小型气象站可以监测小范围的天气、降雨、温度、气流等气象因子。众多环境检测仪器的使用,可以大规模采集与植物冠层、植物周边甚至单个试验小区和单个测试材料有关的土壤、光照、温度、水分、病虫害、伴生生物等外界环境因子。环境型信息将日益广泛应用于环境及其特征性鉴定、作物基因型-环境型互作、表现型预测、病虫害流行预测、近等环境型（near iso-envirotype）确定、作物对特定环境的反应研究、农艺组学（agronomic genomics）、精准高效农业等。展望未来,环境型鉴定需要将研究对象聚焦在单个材料的水平,实现单个材料的相关环境因子不同阶段的动态鉴定;需要开发和建立与基因型、表现型相结合的综合信息系统以及相应的决策支撑系统;环境型信息将最终有助于建立基于基因型-表现型-环境型的三维作物生产和研发系统,从而使未来作物育种中的选择建立在此三维空间概念的基础之上,并推动高产高效作物生产体系的建立。     

大豆灰斑病预防措施

大豆灰斑病作为主要大豆作物的重要病害之一,主要起源于我国黑龙江省地区,对我国 的大豆产量和大豆质量都产生了严重影响。东北平原地区的气候条件适合大豆灰斑病的病原 的萌发和发展,其中主要的原因就是温度和适度。大豆灰斑病有明确的发病的规律和明显的病 状,因此大豆灰斑病的病原体作用大豆作物的一系列原因受到了广泛的关注,本文针对大豆灰 斑病发生的各种原因进行详细的分析,以此得到一些有效的大豆灰斑病防治措施。     

Subfield crop yields and temporal stability in thousands of US Midwest fields

Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p?<?0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha^?1 for maize, 0.39 Mg ha^?1 for cotton, 0.34 Mg ha^?1 for soybean, and 0.59 Mg ha^?1 for wheat. The yield of the unstable zones was similar to the pixels classified as low-and-stable by the standard deviation algorithm, whereas the two-way outlier algorithm did not exhibit this bias. Furthermore, the increase in the number years of yield maps available induced a modest but significant increase in the certainty of stability classifications, and the proportion of unstable pixels increased with the precipitation heterogeneity between the years comprising the yield maps.

     

Time-Lapse Motion Picture Technique Applied to the Study of Geological Processes

Light-weight, battery-operated timers were built and coupled to 16-mm motion-picture cameras having apertures controlled by photoelectric cells. The cameras were placed adjacent to Emmons Glacier on Mount Rainier. The film obtained confirms the view that exterior time-lapse photography can be applied to the study of slow-acting geologic processes.     

Utility of Remote Sensing in Predicting Crop and Soil Characteristics

Remote sensing during the production season can provide visual indications of crop growth along with the geographic locations of those areas. A grid coordinate system was used to sample cotton and soybean fields to determine the relationship between spectral radiance, soil parameters, and cotton and soybean yield. During the 2 years of this study, mid- to late-season correlation coefficients between spectral radiance and yield generally ranged from 0.52 to 0.87. These correlation coefficients were obtained using the green–red ratio and a vegetation index similar to the normalized difference vegetation index (NDVI) using the green and red bands. After 102 days after planting (DAP), the ratio vegetation index (RVI), difference vegetation index (DVI), NDVI, and soil-adjusted vegetation index (SAVI) generally provided correlation coefficients from 0.54 to 0.87. Correlation coefficients for cotton plant height measurements taken 57 and 66 DAP during 2000 ranged from 0.51 to 0.76 for all bands, ratios, and indices examined, with the exception of Band 4 (720nm). The most consistent correlation coefficients for soybean yield were obtained 89–93 DAP, corresponding to peak vegetative production and early pod set, using RVI, DVI, NDVI, and SAVI. Correlation coefficients generally ranged from 0.52 to 0.86. When the topographic features and soil nutrient data were analyzed using principal component analysis (PCA), the interaction between the crop canopy, topographic features, and soil parameters captured in the imagery allowed the formation of predictive models, indicating soil factors were influencing crop growth and could be observed by the imagery. The optimum time during 1999 and 2000 for explaining the largest amount of variability for cotton growth occurred during the period from first bloom to first open boll, with R values ranging from 0.28 to 0.70. When the PCA-stepwise regression analysis was performed on the soybean fields, R ² values were obtained ranging from 0.43 to 0.82, 15 DAP, and ranged from 0.27 to 0.78, 55–130 DAP. The use of individual bands located in the green, red, and NIR, ratios such as RVI and DVI, indices such as NDVI, and stepwise regression procedures performed on the cotton and soybean fields performed well during the cotton and soybean production season, though none of these single bands, ratios, or indices was consistent in the ability to correlate well with crop and soil characteristics over multiple dates within a production season. More research needs to be conducted to determine whether a certain image analysis method will be needed on a field-by-field basis, or whether multiple analysis procedures will need to be performed for each imagery date in order to provide reliable estimates of crop and soil characteristics.     

Low altitude remote sensing technologies for crop stress monitoring: a case study on spatial and temporal monitoring of irrigated pinto bean

Site-specific crop management is a promising approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of high resolution crop data at critical growth stages is a key for real-time data-driven decisions during the production season. The goal of this study was to evaluate the possibility of using small unmanned aerial system (UAS)-based remote sensing technologies to monitor the crop stress of irrigated pinto beans (Phaseolus vulgaris L.) with varied irrigation and tillage treatments. A small UAS with onboard multispectral and infrared thermal imaging sensors was used to collect data from bean field plots on three growth stages in 2015 and 2016, respectively. Indicators including green normalized vegetation index (GNDVI), canopy cover (CC, ratio of ground covered by crop canopy to the total plot area) and canopy temperature (CT, °C) of crops were extracted from imaging data and correlated with ground-reference crop yield and leaf area index (LAI) estimated with a handheld ceptometer. Results show that GNDVI, CC and CT were able to differentiate crops with full and deficit irrigation treatments at each of the three growth stages in both years. Developed indicators were strongly correlated with to the crop yield with Pearson correlation coefficients (r) of approximate 0.71 and 0.72 for GNDVI and CC, respectively, in the early growth stage (54 days after planting) in both years. Canopy temperature showed even stronger correlation (r > 0.8) with yield at early growth stage. Performance of small UAS-based imagery-based indicators in crop stress monitoring and crop yield estimation was better than or comparable to that of the ground-based LAI estimates, indicating the potential of such remote sensing tool in rapid crop stress monitoring and management.     

A high-resolution airborne four-camera imaging system for agricultural remote sensing

This paper describes the design and testing of an airborne multispectral digital imaging system for remote sensing applications. The system consists of four high resolution charge coupled device (CCD) digital cameras and a ruggedized PC equipped with a frame grabber and image acquisition software. The cameras are sensitive in the 400 to 1000 nm spectral range and provide 2048 × 2048 active pixels with 12-bit data depth. A 24 mm lens is attached to each camera via an F to C mount adapter, resulting in an imaging size of 0.63 times the flight altitude. The four cameras are equipped with blue (430–470 nm), green (530–570 nm), red (630–670 nm), and near-infrared (NIR) (810–850 nm) bandpass interference filters, respectively, but have the flexibility to change filters for desired wavelengths and bandwidths. The cameras are arranged in a quad configuration and attached to adjustable mounts that facilitate aligning the cameras horizontally, vertically, and rotationally. The image acquisition software allows the synchronized black-and-white band images from the cameras to be viewed on the computer monitor in any one of the four modes: a quad, one band image at a time, a normal color composite, or a color-infrared (CIR) composite. The band images are refreshed continuously to allow the operator to selectively save images with correct areas of interest. The selected four-band composite image is saved as a tiff file and consecutive images can be saved in 1-s intervals. A band-to-band alignment procedure based on the first- and second-order polynomial transformations was presented to further align the four band images. The system performed well in both stationary and airborne testing conditions. Airborne images obtained from agricultural fields, rangelands, and waterways demonstrate that this system has potential for monitoring crop pest conditions, mapping invasive weeds and assessing natural resources.     

那氏778诱导剂对大豆生长发育的影响

通过试验表明，那氏 778诱导剂处理大豆能使生长发育明显加快，开花期、结荚期、鼓粒前期植株鲜重分别比对照增加 51.8， 86， 73.8 g，较对照提早成熟 2∽ 3天，增产 21.8％。     

利用气象卫星遥感进行哈尔滨地区作物生长状况监测及产量预报

文章通过对近几年的极轨气象卫星实时接收到的AVHRR/HRPT资料进行处理,数据经校正生成哈尔滨地区归一化植被指数灰度图像,利用种植作物结构不同造成绿度值不同的基本原理,进行作物生长状况监测及产量预报。研究表明,利用气象卫星资料进行作物生长状况监测及产量预报,具有客观、及时和动态跟踪等优点,结合传统气象产量预报方法,使产量预报水平有所提高,准确率可达98%以上。     

AquaCrop模型在大葱生物量和土壤贮水量模拟中的应用和验证

为评估AquaCrop模型在华北平原模拟大葱生长和农田水分的适用性,本研究利用实测的农民施肥方式的一个小区和增施氮肥处理的土壤水分、作物生长和产量数据,结合气象资料,获得了AquaCrop模型模拟大葱生长和土壤水分的模型参数,并利用实测的农民施肥方式的另外一个小区、减施氮肥、优化施氮和秸秆还田处理的土壤贮水量和生物量数据进行了模型验证。结果表明：在无水分胁迫条件下AquaCrop模型对大葱土壤贮水量及生物量的模拟结果是可以接受的。对土壤贮水量实测值与模拟值的RMSE为19.4~24.9 mm之间,相对误差为3.9%~12.4%;大葱生物量实测值与模拟值的RMSE为0.31~0.73 t/hm2,相对误差为5.8%~12.8%。     

Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate

Glyphosate is a non-selective, systemic herbicide highly toxic to sensitive plant species. Its use has seen a significant increase due to the increased adoption of genetically modified glyphosate-resistant crops since the mid-1990s. Glyphosate application for weed control in glyphosate-resistant crops can drift onto an off-target area, causing unwanted injury to non-glyphosate resistant plants. Thus, early detection of crop injury from off-target drift of herbicide is critical in crop production. In non-glyphosate-resistant plants, glyphosate causes a reduction in chlorophyll content and metabolic disturbances. These subtle changes may be detectable by plant reflectance, which suggests the possibility of using optical remote sensing for early detection of drift damage to plants. In order to determine the feasibility of using optical remote sensing, a greenhouse study was initiated to measure the canopy reflectance of soybean plants using a portable hyperspectral image sensor. Non-glyphosate resistant soybean (Glycine max L. Merr.) plants were treated with glyphosate using a pneumatic track sprayer in a spray chamber. The three treatment groups were control (0 kg ae/ha), low dosage (0.086 kg ae/ha), and high dosage (0.86 kg ae/ha), each with four 2-plant pots. Hyperspectral images were taken at 4, 24, 48, and 72 h after application. The extracted canopy reflectance data was analyzed with vegetation indices. The results indicated that a number of vegetation indices could identify crop injury at 24 h after application, at which time visual inspection could not distinguish between glyphosate injured and non-treated plants. To improve the results a modified method of spectral derivative analysis was proposed and applied to find that the method produced better results than the vegetation indices. Four selected first derivatives at wavelength 519, 670, 685, and 697 nm could potentially differentiate crop injury at 4 h after treatment. The overall false positive rate was lower than the vegetation indices. Furthermore, the derivatives demonstrated the ability to separate treatment groups with different dosages. The study showed that hyperspectral imaging of plant canopy reflectance could be a useful tool for early detection of soybean crop injury from glyphosate, and that the modified spectral derivative analysis had a better performance than vegetation indices.     

Visual Programming of Stochastic Weather Generator and Future Applications on Agroecological Study

Based on former studies on weather simulator modules in IPMist laboratory, study on visual programming of stochastic weather generator (VS-WGEN) was continued. In this study, Marlkov Chain,Monte Carlo, Fourier Series, and weak stationary process were used to generate the daily weather data in software Matlab 6.0, with the data input from 40 years' weather data recorded by Beijing Weather Station. The generated data includes daily maximum temperature, minimum temperature, precipitation and solar radiation. It has been verified that the weather data generated by the VS-WGEN were more accurate than that by the old WGEN, when twenty new model parameters were included. VS-WGEN bas wide software applications,such as pest risk analysis, pest forecast and management. It can be implemented in hardware development as well, such as weather control in weather chamber and greenhouse for researches on ecological adaptation of crop varieties to a given location over time and space. Overall, VS-WGEN is a very useful tool for studies on theoretical and applied ecology.     

Determination of soybean yield gap and potential production in Iran using modeling approach and GIS

Increasing crop production is necessary to maintain food security for the growing global population.Reducing the gap between actual and potential yield is one of the important ways to increase yield per unit area.Potential yield and the yield gap of soybean were determined for Golestan Province,Iran,using Soybean Simulation Model (SSM-i Crop2) and Geographical Information System (GIS).Information from 24 weather stations and soil data of the region were used.Yield gap and production gap were calculated at county and province levels.The average actual yield of soybean in this province was2.28 t ha~(–1) while the province’s potential yield was 4.73 t ha~(–1),so the yield gap was estimated 2.44 t ha~(–1).Thus,there is a great potential for increasing soybean yield in Golestan,which is possible through improving crop management of soybean in farmers’fields.The average water productivity of soybean was estimated to be 0.81 kg m~(–3).Spatial distribution of water productivity in soybean farms showed that the highest and the lowest water productivities (0.99 and 0.44 kg m~(–3)) were in western and eastern regions of the province,respectively,in accordance to vapour pressure deficit.It was concluded that soybean production in the province could increase by 66%(from 109 970 to 182 170 tons) if 80% of the current yield gap could be removed.     

春小麦复播绿肥对连作棉田土壤肥力的影响

为了解决由于棉花长期连作带来的种种弊端,主要通过在连作棉田上进行春小麦复播绿肥(草木樨、油菜、油葵、大豆)试验,研究各种绿肥生物量,养分含量及其绿肥翻压后对棉田土壤有机质,速效养分的影响,来筛选出适应棉田轮作倒茬的最佳绿肥种类.研究结果表明:4种绿肥中草木樨的生物量最高;4种绿肥翻压后对土壤有机质和速效养分含量均有一定的影响,其中草木樨、大豆翻压后土壤有机质分别提高了8.58%,1.01%;4种绿肥翻压后土壤速效氮提高了0.30～5.96%,除大豆外,其他3个处理的土壤速效钾含量平均是对照的1.08倍;研究发现油葵、油菜的富钾作用较为突出.4个处理中春小麦复播草木樨是相对较好的一组轮作作物.     

Suitability of the DNDC model to simulate yield production and nitrogen uptake for maize and soybean intercropping in the North China Plain

Intercropping is an important agronomic practice. However, assessment of intercropping systems using field experiments is often limited by time and cost. In this study, the suitability of using the DeNitrification DeComposition(DNDC) model to simulate intercropping of maize(Zea mays L.) and soybean(Glycine max L.) and its aftereffect on the succeeding wheat(Triticum aestivum L.) crop was tested in the North China Plain. First, the model was calibrated and corroborated to simulate crop yield and nitrogen(N) uptake based on a field experiment with a typical double cropping system. With a wheat crop in winter, the experiment included five treatments in summer: maize monoculture, soybean monoculture, intercropping of maize and soybean with no N topdressing to maize(N0), intercropping of maize and soybean with 75 kg N ha~(–1) topdressing to maize(N75), and intercropping of maize and soybean with 180 kg N ha~(–1) topdressing to maize(N180). All treatments had 45 kg N ha~(–1) as basal fertilizer. After calibration and corroboration, DNDC was used to simulate long-term(1955 to 2012) treatment effects on yield. Results showed that DNDC could stringently capture the yield and N uptake of the intercropping system under all N management scenarios, though it tended to underestimate wheat yield and N uptake under N0 and N75. Long-term simulation results showed that N75 led to the highest maize and soybean yields per unit planting area among all treatments, increasing maize yield by 59% and soybean yield by 24%, resulting in a land utilization rate 42% higher than monoculture. The results suggest a high potential to promote soybean production by intercropping soybean with maize in the North China Plain, which will help to meet the large national demand for soybean.