A floating sensing system to evaluate soil and crop variability within flooded paddy rice fields

Continuous paddy rice cultivation requires fields to be flooded most of the time limiting seriously the collection of detailed soil information. So far, no appropriate soil sensor technology for identifying soil variability of flooded fields has been reported. Therefore, the primary objective was the development of a sensing system that can float, acquire and process detailed geo-referenced soil information within flooded fields. An additional objective was to determine whether the collected apparent electrical conductivity (EC_a) information could be used to support soil management at a within-field level. A floating sensing system (FloSSy) was built to record EC_a using the electromagnetic induction sensor EM38, which does not require physical contact with the soil. Its feasibility was tested in an alluvial paddy field of 2.7 ha located in the Brahmaputra floodplain of Bangladesh. The high-resolution (1 × 1 m) EC_a data were classified into three classes using the fuzzy k-means classification method. The variation among the classes could be attributed to differences in subsoil (0.15–0.30 m below soil surface) bulk density, with the smallest EC_a values representing the lowest bulk density. This effect was attributed to differences in compaction of the plough pan due to differential puddling. There was also a significant difference in rice yield among the EC_a classes, with the smallest EC_a values representing the lowest yield. It was concluded that the floating sensing system allowed the collection of relevant soil information, opening potential for precision agriculture practices in flooded crop fields.     

Strategies to evaluate goodness of reference strips for in-season, field scale, irrigated corn nitrogen sufficiency

The nitrogen (N) sufficiency approach to assess plant N status for in-season N management requires a non-N-limiting reference to make N recommendations. Use of reference strips in fields with spatially variable soils and the impact this variability has within N enriched reference strips are not well understood. Consequently three strategies were investigated to evaluate the impact of spatially variable sandy soils within reference strips in two commercial center pivot-irrigated corn fields. Evaluation strategies were: (i) ignore soil spatial variability throughout the reference strips, (ii) account for soil variability in the reference strips based on second-order NRCS soil map units, and (iii) account for soil variability based on apparent electrical conductivity (EC_a) data as a surrogate for soil texture differences in the reference strips. A sufficiency index (SI) calculated from radiometer measured canopy reflectance data (SI_sensor) and from SPAD chlorophyll meter data (SI_meter) at two growth stages during corn vegetative growth were used to assess N sufficiency within the N enriched reference strips. By ignoring soil spatial variability in the reference strips, corn in the sandier soils was designated N deficient. Accounting for soil spatial variability using NRCS soil mapping units improved N sufficiency designations of corn in the reference strip for the different soil types contained within the reference strip but tended to designate corn in lighter texture areas within a mapping unit as N deficient. Use of EC_a as a surrogate for soil texture typically performed best for classifying corn N sufficiency throughout the reference strip and is recommended as a method to obtain reference strip normalizing values in fields with spatially variable sandy soils.     

Evaluating unoccupied aerial systems (UAS) imagery as an alternative tool towards cotton-based management zones

Unoccupied aerial system (UAS) imagery may serve as an additional tool towards management zone delineation. This is because UAS data collection is relatively flexible. However, it is unclear how useful UASs can be towards generating management zones, relative to preexisting tools (e.g. apparent soil electrical conductivity or EC_a). The purpose of this study, therefore, was to evaluate UAS imagery, relative to EC_a, in terms of their ability to: 1) predict cotton traits (i.e. height, seed cotton yield), and 2) define cotton management zones based on these traits. Single-season UAS images from multispectral/thermal sensors were collected and processed into Normalized Difference Vegetation Index (NDVI) and radiometric surface temperature (T_r), respectively. Management zones were also delineated using digital camera (RGB) imagery collected at periods before planting and near harvest. RGB management zones were delineated by a novel open boll mapping approach. In-season NDVI and T_r layers were significant (P?<?0.01) predictors of canopy height. Additionally, NDVI and T_r maps produced statistically different management zones during flowering and boll filling growth stages in terms of yield (P?=?0.001 or less). Open boll layers were all more accurate predictors of cotton seed yield than EC_a data—these two layers also produced statistically distinct management zones. ANOVA tests revealed that, given EC_a alone, adding UAS information via the RGB open boll map resulted in a significantly different yield prediction model (P?<?0.001). These results suggest that UAS imagery can offer valuable information for cotton management zone delineation that other techniques cannot.

     

Evaluation of methods to determine residual soil nitrate zones across the northern Great Plains of the USA

A four-year study was conducted from 2000 to 2004 at eight field sites in Montana, North Dakota and western Minnesota. Five of these sites were in North Dakota, two were in Montana and one was in Minnesota. The sites were diverse in their cropping systems. The objectives of the study were to (1) evaluate data from aerial photographs, satellite images, topographic maps, soil electrical conductivity (EC_a) sensors and several years of yield to delineate field zones to represent residual soil nitrate and (2) determine whether the use of data from several such sources or from a single source is better to delineate nitrogen management zones by a weighted method of classification. Despite differences in climate and cropping, there were similarities in the effectiveness of delineation tools for developing meaningful residual soil nitrate zones. Topographic information was usually weighted the most because it produced zones that were more correlated to actual soil residual nitrate than any other source of data at all locations. The soil EC_a sensor created better correlated zones at Minot, Williston and Oakes than at most eastern sites. Yield data for an individual year were sometimes useful, but a yield frequency map that combined several years of standardized yield data was more useful. Satellite imagery was better than aerial photographs at most locations. Topography, satellite imagery, yield frequency maps and soil EC_a are useful data for delineating nutrient management zones across the region. Use of two or more sources of data resulted in zones with a stronger correlation with soil nitrate.     

根层调控措施对甜玉米-黄瓜设施蔬菜轮作体系土壤硝态氮的影响

【目的】以甜玉米作为填闲作物,探讨不同的根层调控措施对消减土壤剖面累积硝态氮及下茬黄瓜生长的影响。【方法】在华北平原传统棚室蔬菜的休闲季种植甜玉米,针对甜玉米设置添加土壤调理剂和秸秆还田2种根层调控措施,以甜玉米传统种植作为对照,进行田间小区试验。试验于2008年5月至2011年5月进行,共3次甜玉米-黄瓜轮作,6季作物。每年6月初至9月底种植甜玉米,10月初至次年1月底扣棚育黄瓜苗,当年2月初种植黄瓜。在甜玉米季,共3个处理,随机排列,重复3次。小区面积为4 m×2 m,小区间隔0.3 m,区组之间布设1 m的保护行。【结果】甜玉米种植季,调理剂处理的玉米籽粒产量最高,2008、2009和2010年的产量分别为6.2、7.4和7.9 t·hm^-2;土壤调理剂和秸秆还田2种根层调控处理的甜玉米总吸氮量高于传统种植。秸秆还田和调理剂处理能够促进20-60 cm土层根系的生长发育,促使根系吸收更深层的土壤养分。2种根层调控措施均能降低土壤剖面NO₃^--N的累积,尤其对100-200 cm的作物根区NO₃^--N的消减能力更强,NO₃^--N消减趋势大致为：调理剂>秸秆还田>传统种植。3季黄瓜种植季,不同前茬处理的黄瓜产量、生物量和吸氮量差异均不显著;3季平均土壤NO₃^--N在0-200 cm土层的残留量为秸秆还田<调理剂<传统种植。3个轮作季后,传统种植、调理剂和秸秆还田处理在0-200 cm土层的氮素盈余量分别为1 911.6、1 966.3和1 930.2 kg·hm^-2,调理剂处理显著高于传统种植。【结论】在硝态氮高累积的设施土壤上,随着种植年限的增加,加入土壤调理剂和适当的秸秆还田对100-200 cm的作物根区土壤剖面NO₃^--N的消减能力更强。填闲作物种植第二年对下茬黄瓜土壤NO₃^--N的消减作用最为明显。土壤调理剂和秸秆还田措施能够显著提高甜玉米对土壤剖面NO₃^--N的消减能力,减缓土壤NO₃^--N 的淋失,提高经济效益。     

Spatial variability and temporal stability of apparent soil electrical conductivity in a Mediterranean pasture

The general objectives of this study were to evaluate (i) the specificity of the spatial and temporal dynamics of apparent soil electrical conductivity (EC_a) measured by a electromagnetic induction (EMI) sensor, over 7 years, in variable conditions (of soil moisture content (SMC), soil vegetation cover and grazing management) and, consequently, (ii) the potential for implementing site-specific management (SSM). The DUALEM 1S sensor was used to measure the EC_a in a 6 ha pasture experimental field four times between June 2007 and February of 2013. Soil spatial variability was characterized by 76 samples, geo-referenced with the global positioning system (GPS). The soil was characterized in terms of texture, moisture content, pH, organic matter content, nitrogen, phosphorus and potassium. This study shows a significant temporal stability of the EC_a patterns under several conditions, behavior that is an excellent indicator of reliability of this tool to survey spatial soil variability and to delineate potential site-specific management zones (SSMZ). Significant correlations were obtained in this work between the EC_a and relative field elevation, pH, silt and soil moisture content. These results open perspectives for using the EMI sensor as an indicator of SMC in irrigation management and of needs of limestone correction in Mediterranean pastures. However, it is interesting to extend the findings to other types of soil to verify the origin of the lack of correlation between the EC_a data measured by DUALEM sensor and properties such as the clay, organic matter or phosphorus soil content, fundamental parameters for establishment of pasture SSM projects.     

填闲种植对棚室菜田累积氮素消减及黄瓜生长的影响

【目的】在中国集约化蔬菜种植区,传统的高水肥投入导致土壤氮素大量累积,致使氮素淋洗到土壤深层或进入地下水,造成地下水硝酸盐污染。种植填闲作物可控制和减少土壤深层硝态氮的累积,因此,本研究探讨不同填闲作物种类对消减土壤剖面累积硝态氮及下季作物生长的影响,筛选出适宜的填闲作物种类。【方法】以华北平原传统棚室黄瓜菜田为对象,在蔬菜休闲期通过种植深根型填闲作物,利用其根系发达、生长迅速、吸氮量大的特点,促使土层中硝态氮大量消耗,以消减土壤剖面根层NO₃^--N累积和降低土壤剖面NO₃^--N淋失。以此为目标,设置甜玉米、苋菜、甜高粱及休闲田间小区试验,采集测定土壤、植株及根系样品,分析不同填闲作物的消减效果。【结果】在这3种填闲作物中,甜玉米的生物量和吸氮量最大,整体根长密度大于其它填闲种类。从对土壤剖面NO₃^--N的消减能力来说,甜玉米的消减能力最高。2008、2009及2010年,甜玉米对0-200 cm土层土壤NO₃^--N的消减量分别为153.8、605.7和56.3 kg·hm^-2。3年休闲期后,第一季前茬休闲处理的黄瓜产量、生物量及吸氮量均最高,在产量、吸氮量上与其他处理差异显著;第二季、第三季,前茬休闲的产量、生物量和吸氮量与其他处理差异不显著;填闲作物的种植并没有对黄瓜产量造成影响,并且黄瓜收获后土壤NO₃^--N含量明显降低。氮素表观平衡中0-200 cm土层,甜玉米-黄瓜的氮素亏缺量较大,说明甜玉米能显著降低土壤NO₃^--N的残留。种植填闲作物能够达到经济效益和生态效益的双赢,甜玉米、苋菜与甜高粱可分别为农民带来39 467、497和16 522元/hm²的净收入。【结论】棚室菜田夏季种植填闲作物不仅可以消减土壤剖面根层NO₃^--N累积,而且对下茬黄瓜产量未造成显著影响,黄瓜收获后土壤NO₃^--N含量也会明显降低;在设施蔬菜轮作体系中引入填闲作物具有可行性,甜玉米为较佳的填闲作物。     

Delineating site-specific management zones for pH-induced iron chlorosis

Iron chlorosis can limit crop yield, especially on calcareous soil. Typical management for iron chlorosis includes the use of iron fertilizers or chlorosis tolerant cultivars. Calcareous and non-calcareous soil can be interspersed within fields. If chlorosis-prone areas within fields can be predicted accurately, site-specific use of iron fertilizers and chlorosis-tolerant cultivars might be more profitable than uniform management. In this study, the use of vegetation indices (VI) derived from aerial imagery, on-the-go measurement of soil pH and apparent soil electrical conductivity (EC_a) were evaluated for their potential to delineate chlorosis management zones. The study was conducted at six sites in 2004 and 2005. There was a significant statistical relationship between grain yield and selected properties at two sites (sites 1 (2005) and 3), moderate relationships at sites 2 and 4, and weak relationships at site 5. For sites 1 (2005) and 3, and generally across all sites, yield was predicted best with the combination of NDVI and deep EC_a. These two properties were used to delineate chlorosis management zones for all sites. Sites 1 and 3 showed a good relationship between delineated zones and the selected properties, and would be good candidates for site-specific chlorosis management. For site 5, differences in the properties between mapped zones were small, and the zones had weak relationships to yield. This site would be a poor candidate for site-specific chlorosis management. Based on this study, the delineation of chlorosis management zones from aerial imagery combined with soil EC_a appears to be a useful tool for the site-specific management of iron chlorosis.     

Spatial and Temporal Variability of Corn Grain Yield: Site-Specific Relationships of Biotic and Abiotic Factors

Inadequate information on factors affecting crop yield variability has contributed to the slow adoption of site-specific farming (SSF). This study was conducted to determine the effects of biotic and abiotic factors on the spatial and temporal variability of irrigated corn grain yields and to derive information useful for SSF. The effects of water (80% evapotranspiration (ET) and 50% ET), hybrid (drought-tolerant and -susceptible), elevation, soil index (SI)(texture), soil NO₃–N, arthropods, and diseases on corn grain yield were investigated at Halfway, TX on geo-referenced locations. Grain yields were influenced by interrelationships among biotic and abiotic factors. Grain yields were consistently high under high water treatment, at higher elevations, and on soils with high SI (high clay and silt). Soil NO₃–N increased grain yields when water was adequate. Management zones for variable rate fertilizer and water application should, therefore, be based on information on elevation, SI, and soil NO₃–N. The effects of arthropods, diseases, and crop stress (due to drought and N) on corn grain yield were unpredictable. Spider mite (Oligonychus pratensis) and common smut (Ustilago zeae) damage occurred under hot and dry conditions in 1998. Spider mite infestations were high in areas with high soil NO₃–N. Moderate air temperatures and high relative humidity in 1999 favored southwestern corn borer (Diatraea grandiosella) and common rust (Puccinia maydis) incidences. Knowledge of conditions that favor arthropods and diseases outbreak and crop stress can improve the efficiency of scouting and in-season management of SSF. Management of SSF can be improved when effects of biotic and abiotic factors on grain yield are integrated and evaluated as a system.     

Changes in yield classification in a soybean-rice rotation

For yield based site-specific management to be successful in fields with crop rotations, changes in management zones between crops must be determined. The study objectives were to determine if yield classes change between crops within a rotation and whether soil properties can predict the yield classes or the year-to-year changes. A percentile classification method was used to categorize yearly soybean (Glycine max) and rice (Oryza sativa) yield in two fields with soybean-rice-soybean rotations into low, medium and high yield classes. There was little agreement in yield classifications between years. Yield class based on soil properties was predicted accurately by linear discriminant analysis in Field 1 20–67% of the time and in Field 2 13–83% of the time. Predictions in Field 1 were based on soil available Mg and P, elevation and the deep soil apparent electrical conductivity (EC_a). Predictions in Field 2 were based on soil texture, soil available P, K and Mg, and pH. The linear discriminant analysis was also able to predict year-to-year changes in yield class. Changes in class in Field 1 could be predicted by total soil C and N, silt, and soil available Mg and P depending on the year. Soil texture, soil available P, K and Mg, total soil C and pH, elevation and deep soil EC_a predicted yield changes in Field 2 depending on the year. The results of this study indicate only limited success at management zone definition in a soybean-rice rotation. Further investigation is needed with other crop rotation sequences to verify the findings of this study.     

Mapping subsoil acidity and shallow soil across a field with information from yield maps, geophysical sensing and the grower

Management decisions, such as subsoil liming or varying fertilizer inputs to take account of soil depth and anticipated yields require knowledge of where subsoil constraints to root growth occur across the field. We used selected yield maps based on criteria derived from crop simulation, apparent soil electrical conductivity (EC_a), gamma-ray emission maps and a soil type map drawn by the grower to predict the spatial distribution of subsoil acidity and shallow soil across a field. Yield maps integrate the effects of variation in soil and climate, and it was only under specific seasonal conditions that subsoil constraints depressed yields. We used crop simulation modelling to select yield maps with a large information content on the spatial distribution of these constraints and to omit those with potentially misleading information. Yield and other spatial data layers were used alone or in combination to develop subsoil mapping options to accommodate differences in data availability, access to precision agriculture techniques and the grower’s aptitude and preference. One option used gamma-ray spectrometry and EM38 survey as a dual-sensing system to improve data interpretation. Gamma-ray spectrometry helped to overcome the inability of current EC_a-based methods to sense soil depth in highly weathered sandy soil over cemented gravel. A feature of the approaches presented here is the use of grower and agronomist knowledge, and experience to help interpret the spatial data layers and to evaluate which approach is most suitable and likely to be adopted to suit an individual.     

Linking Microbial-Scale Findings to Farm-Scale Outcomes in a Dryland Cropping System

Soil biological response to management is best evaluated in field-scale experiments within the context of the soil environment and crop; however, cost-effective methods are lacking to relate these data which span multiple spatial scales. We hypothesized that zones of apparent electrical conductivity (EC_a) could be used to integrate soil properties (sampling-site scale), microbial-scale measures of vesicular-arbuscular mycorrhizal (VAM) fungi, and field-scale wheat yields from yield maps. An on-farm dryland experiment (250 ha) was established wherein two (32-ha) fields were assigned to each phase of a winter wheat (Triticum aestivum L.) – corn (Zea mays L.) – proso millet (Panicum miliaceum L.) – fallow rotation. Each field was mapped and classified into four zones (ranges) of EC_a. Soil samples were collected from geo-referenced sites within EC_a zones and analyzed for multiple soil properties associated with productivity (0–7.5 and/or 0–30 cm). Additionally, VAM fungi were assessed using C16:1(cis)11 fatty acid methyl ester biomarker (C16_vam), glomalin immunoassay, and wet-aggregate stability (WAS) techniques (1–2mm aggregates from 0- to 7.5-cm soil samples). Concentrations of C16_vam and WAS increased among cropping treatments as: fallow < wheat < corn < millet. Glomalin across crops and replicates, C16_vam and WAS in fallow (crop effect removed), soil properties associated with productivity, and wheat yields were negatively correlated with EC_a and different among EC_a zones (P 0.05). Zones of EC_a provide a point of reference for relating data collected at different scales. Monitoring cropping system parameters and profitability, over time, may allow linkage of microbial-scale processes to farm-scale economic and ecological outcomes.     

Apparent Electrical Conductivity,Soil Properties and Spatial Covariance in the U.S. Southern High Plains

Site-specific soil and crop management will require rapid low-cost sensors that can generate position-referenced data that measure important soil properties that impact crop yields. Apparent electrical conductivity (EC_a) is one such measure. Our main objective was to determine which commonly measured surface soil properties were related to EC_a at six sites in the Texas Southern High Plains, USA. We used the Veris 3100 and Geonics EM-38 EC mapping systems on 12 to 47 ha areas in six center-pivot irrigation sites. Soil samples were taken from 0–150 mm on a 0.1 to 0.8 ha grid and analyzed for routine nutrients and particle size distribution. At four of the six sites, shallow EC_a measured with the Veris 3100 (EC_a-sh) positively correlated to clay content. Clay content was negatively related with EC_a-sh at one site, possibly due to low bulk density of the shallow calcic horizon at that site. Other soil properties that were often correlated with EC_a included soil extractable Ca²⁺, Mg²⁺, Na⁺, CEC, silt and soluble salts. Extractable K⁺, NO₃^–, SO₄^–, Mehlich-3-P, and pH were not related to EC_a. Partial least squares regression (PLS) of seven soil properties explained an average of 61%, 51% and 37% of the variation in observed shallow EC_a-sh, deep EC_a with the Veris 3100 (EC_a-dp) and EC_a with the Geonics EM-38 (EC_a-em), respectively. Including nugget, range and sill parameters from a spherical semivariance model of the residuals from PLS regression improved the fit of mixed models in 15 of 18 cases. Apparent EC, therefore can provide useful information to land-users about key soil properties such as clay content and extractable Ca²⁺, but that spatial covariance in these relationships should not be ignored.     

Comparing apparent electrical conductivity measurements on a paddy field under flooded and drained conditions

Every growing season, paddy fields are kept both flooded and drained for a significant period of time. As a consequence, these soils develop distinct physico-chemical characteristics. For practical reasons, these soils are mostly sampled under dry conditions, but the question arises how representative the results are for the wet growing conditions. Therefore, the apparent electrical conductivity (EC_a) of a 1.4 ha alluvial paddy field located in the Brahmaputra floodplain of Bangladesh was measured in both dry and wet conditions by a sensing system using the electromagnetic induction sensor EM38, which does not require physical contact with the soil, and compared both surveys. Due to the smooth water surface under wet conditions which ensured increased stability of the sensing platform, the results of the survey showed considerably reduced micro-scale variability of EC_a. Furthermore, the wet survey results more reliably furnished soil-related information mainly due to the absence of soil moisture dynamics. The differences between EC_a under wet and dry conditions were attributed to differences in soil texture, mainly the sand content variation having considerable effect on soil moisture differences when flooded following drainage. Accordingly, the largest differences between EC_a under wet and dry conditions were found in those parts of the field with a large sand content. Hence, the conclusion was that an EC_a survey on flooded fields has an added value to precision soil management.     

Combining leaf fluorescence and active canopy reflectance sensing technologies to diagnose maize nitrogen status across growth stages

Rapid methods allowing for non-destructive crop monitoring are imperative for accurate in-season nitrogen (N) status assessment and precision N management. The objectives of this paper were to (1) compare the performance of a leaf fluorescence sensor Dualex 4 and an active canopy reflectance sensor Crop Circle ACS-430 for estimating maize (Zea mays L.) N status indicators across growth stages; (2) evaluate the potential of N status prediction across growth stages using the reflectance parameters acquired from the canopy sensor at an early growth stage; and, (3) investigate the prospect of combining the active canopy sensor and leaf fluorescence sensor data to estimate N nutrition index (NNI) indirectly using a general model across growth stages. The results indicated that data from both sensors were closely related to NNI across stages. However, using the direct NNI estimation method, among the tested indices, only the N balance index (NBI) could diagnose N status satisfactorily, based on the Kappa statistics. The effect of growth stages on proximal sensing was reduced by incorporating the information of days after sowing. It was found that the leaf fluorescence sensor performed relatively better in estimating plant N concentration whereas the canopy reflectance sensor performed better in aboveground biomass estimation. Their combination significantly improved the reliability of N diagnosis, including NNI prediction. In addition, the study confirmed that N status can be assessed by predicting aboveground biomass at the later stages using the canopy reflectance measurements at an early stage. Furthermore, the integrated NBI was verified to be a more robust and sensitive N status indicator than the chlorophyll concentration index. It is concluded that combining active canopy sensor data, of an early growth stage (e.g. V8), with leaf fluorescence sensor data, modified using days after sowing, can improve the accuracy of corn N status diagnosis across growth stages.

     

N-catch crops affect soil profile nitrate-N accumulation during vegetable cultivation

To reduce nitrate leaching, the effects of three N-catch crops of sweet corn (Zea mays L.), amaranth (Amaranthus spp.), and sweet sorghum (Sorghum Linn.) on nitrate-N accumulation in the soil profile were examined using an incubation experiment. Results showed that the biomass and N absorbed by sweet corn were the largest compared with the other two N-catch crop treatments. Root length density for sweet corn, amaranth, and sweet sorghum in the 0–150 cm soil layer was 0.66, 0.34 and 0.46 cm/cm³, respectively, and root dry weight was 0.065, 0.021 and 0.038 mg/cm³, respectively. In the 0–200 cm soil layer, nitrate-N accumulation for fallow, mature sweet sorghum, amaranth, and sweet corn was 1124.7, 899.4, 867.4 and 794.2 kg/hm², respectively, where the treatment of sweet corn had the smallest N-accumulation. The nitrate-N leachability of fallow, sweet corn, amaranth, and sweet sorghum treatment was 3.6, 1.9, 2.4 and 2.6 kg/hm², respectively, indicating that cropping of sweet corn, amaranth, and sweet sorghum could reduce the leachability by 47%, 35% and 28% in comparison with fallow treatment. Therefore, the cultivation of N-catch crops can reduce nitrate leaching in seasonal soil, and the sweet corn might be the most suitable catch crop.     

玉米草木樨间作效应分析

为探讨农牧结合种植方式进行了５年的玉米草木樨２∶１间作试验。结果表明，玉米间作草木樨可改善玉米群体透光水平；增加饲用蛋白质产量７５％；而且对土壤供肥状况和后作也有良好的影响，在作物生育期间玉米和草木樨无明显争水争肥现象；包括草木樨所占面积，间作玉米仍可获得相当于清种９３．６％的籽粒产量。     

A cost-effective canopy temperature measurement system for precision agriculture: a case study on sugar beet

Increasing agricultural efficiency in a sustainable manner will contribute to feed a growing population under limited land, nutrient and water resources. Water scarcity and the increasing social concern for this resource are already requiring more sophisticated irrigation and decision-support systems. To address the heterogeneity in crop water status in a commercial field, precision irrigation requires accurate information about crops (e.g., crop water status), soil (e.g., moisture content) and weather (e.g., wind speed and vapor pressure deficit). Numerous studies have shown that plant canopy temperature can be used to derive reliable plant water stress indicators, thus making it a promising tool for irrigation water management. However, efficient and cost-effective measurement techniques are still lacking. This paper assesses the potential of infrared thermometry and thermal imaging for monitoring plant water stress in a commercial sugar beet field by comparing canopy temperature data acquired from a conventional thermal camera with an inexpensive infrared sensor, both mounted on a rotary-wing unmanned aerial vehicle (UAV). Measurements were taken at various phenological stages of the sugar beet growing season. Laboratory tests were performed to determine the key features for accurate temperature measurements and flight altitude. Experiments were conducted in 2014 and 2015 in experimental and commercial sugar beet fields in Southwestern Spain to (i) develop an affordable infrared temperature system suitable for mounting on a UAV to obtain thermal information, (ii) compare sugar beet canopy temperature measurements collected with the low-cost platform with those obtained from a conventional thermal camera, both mounted on a rotary-wing UAV, (iii) identify the factors that will limit the use of the low-cost system to derive temperature-based water stress indices. To accomplish these objectives, well-watered and deficit irrigated plots were established. Results indicated that the lightweight canopy temperature system was robust and reliable, although there were some constraints related to weather conditions and delimitation of the area covered by the infrared sensor.     

Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds

LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from r = 0.56 to r = 0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e., height, width and volume) and on canopy structure (i.e., light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.