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.     

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.     

Delineating productivity zones in a citrus grove using citrus production,tree growth and temporally stable soil data

The productivity of a citrus grove with variation in tree growth was mapped to delineate zones of productivity based on several indicator properties. These properties were fruit yield, ultrasonically measured tree canopy volume, normalized difference vegetation index (NDVI), elevation and apparent electrical conductivity (EC_a). The spatial patterns of soil series, soil color and EC_a, and their correspondence with the variation in yield emphasized the importance of variation in the soil in differentiating the productivity of the grove. Citrus fruit yield was positively correlated with canopy volume, NDVI and EC_a, and yield was negatively correlated with elevation. Although all the properties were strongly correlated with yield and were able to explain the productivity of the grove, citrus tree canopy volume was most strongly correlated (r = 0.85) with yield, explaining 73% of its variation. Tree canopy volume was used to classify the citrus grove into five productivity zones termed as ‘very poor’, ‘poor’, ‘medium’, ‘good’ and ‘very good’ zones. The study showed that productivity of citrus groves can be mapped using various attributes that directly or indirectly affect citrus production. The productivity zones identified could be used successfully to plan soil sampling and characterize soil variation in new fields.     

Relevance of sink-size estimation for within-field zone delineation in vineyards

Source to sink size ratio, i.e.: the relative abundance of photosynthetically active organs (leaves) with regards to photosynthate demanding organs (mainly bunches), is widely known to be one of the main drivers of grape oenological quality. However, due to the difficulty of remote sink size estimation, Precision Viticulture (PV) has been mainly based on within-field zone delineation using vegetation indices. This approach has given only moderately satisfactory results for discriminating zones with differential quality. The aim of this work was to investigate an approach to delineate within-vineyard quality zones that includes an estimator of sink size in the data-set. The study was carried out during two consecutive seasons on a 4.2 ha gobelet-trained cv. ‘Tempranillo’ vineyard. Zone delineation was performed using Normalized Difference Vegetation Index (NDVI), soil apparent electrical conductivity (EC_a) and bunch number (BN) data. These variables were considered separately, in pairs, or the three altogether, using fuzzy k-means cluster analysis for combinations. The zones delineated based on single variables did not allow a sufficient discrimination for grape composition at harvest, NDVI being the only variable that by itself resulted in zones that to some extent differed in grape composition. On the contrary, when two variables were combined, discrimination in terms of grape composition improved remarkably, provided the sink size estimation variable (BN) was included in the combination. Lastly, the combination of the three variables yielded the best discriminating zoning, improving slightly on those provided by NDVI + BN and EC_a + BN combinations. Thus, the relevance of including a variable related to sink size (in this case the number of bunches per plant) has been confirmed, which makes its consideration highly advisable for any PV work aiming at zone delineation for grape quality purposes.     

Management zones delineation using fuzzy clustering techniques in grapevines

Precision viticulture aims at managing vineyards at a sub-field scale according to the real needs of each part of the field. The current study focused on delineating management zones using fuzzy clustering techniques and developing a simplified approach for the comparison of zone maps. The study was carried out in a 1.0 ha commercial vineyard in Central Greece during 2009 and 2010. Variation of soil properties across the field was initially measured by means of electrical conductivity, soil depth and topography. To estimate grapevine canopy properties, NDVI was measured at different stages during the vine growth cycle. Yield and grape composition (must sugar content and total acidity) mapping was carried out at harvest. Soil properties, yield and grape composition parameters showed high spatial variability. All measured data were transformed on a 48-cell grid (10 × 20 m) and maps of two management zones were produced using the MZA software. Pixel-by-pixel comparison between maps of electrical conductivity, elevation, slope, soil depth and NDVI with yield and grape composition maps, set as reference parameters, allowed for the calculation of the degree of agreement, i.e. the percentage of pixels belonging to the same zone. The degree of agreement was used to select the best-suited parameters for final management zones delineation. For the year 2009 soil depth, early and mid season NDVI were used for yield-based management zones while for quality-based management zones ECa, early and mid season NDVI were utilized. For the year 2010 ECa, elevation and NDVI acquired during flowering and veraison were used for the delineation of yield-based management zones while for quality-based management zones ECa and NDVI acquired during flowering and harvest were utilized. Results presented here could be the basis for simple management zone delineation and subsequent improved vineyard management.     

Maximizing the relationship of yield to site-specific management zones with object-oriented segmentation of hyperspectral images

Quick and low cost delineation of site-specific management zones (SSMZ) would improve applications of precision agriculture. In this study, a new method for delineating SSMZ using object-oriented segmentation of airborne imagery was demonstrated. Three remote sensing domains—spectral, spatial, and temporal- are exploited to improve the SSMZ relationship to yield. Common vegetation indices (VI), and first and second derivatives (\(\rho^{\prime}\), \(\rho^{\prime\prime}\)) from twelve airborne hyperspectral images of a cotton field for one season \(\rho^{\prime}\) were used as input layers for object-oriented segmentation. The optimal combination of VI, SSMZ size and crop phenological stage were used as input variables for SSMZ delineation, determined by maximizing the correlation to segmented yield monitor maps. Combining narrow band vegetation indices and object-oriented segmentation provided higher correlation between VI and yield at SSMZ scale than that at pixel scale by reducing multi-resource data noise. VI performance varied during the cotton growing season, providing better SSMZ delineation at the beginning and middle of the season (days after planting (DAP) 66–143).The optimal scale determined for SSMZ delineation was approximately 240 polygons for the study field, but the method also provided flexibility enabling the setting of practical scales for a given field. For a defined scale, the optimal single phenological stage for the study field was near July 11 (DAP 87) early in the growing season. SSMZs determined from multispectral VIs at a single stage were also satisfactory; compared to hyperspectral indices, temporal resolution of multi-spectral data seems more important for SSMZ delineation.     

涂层缓释肥和氨基酸水溶肥在棉花上的应用研究

利用新肥料,构建高效、简便的施肥管理措施,对于提高棉花生产的生态和经济效益具有重要的现实意义。该文采用田间试验方法,研究了基施涂层缓释肥与叶面施肥相结合,不同用量基施缓释肥与花期追肥氮肥相结合的不同管理措施对棉花结铃数、单铃重和产量的影响。结果表明,当地农民习惯施肥没有增产效果;基施涂层缓释肥,花期追施尿素增产效果显著,以基施1.2t/hm2涂层缓释肥,花期追施尿素187.5kg/hm2单产最高,较不施肥增产39%;叶面喷施氨基酸水溶性肥料没有显著增产效果。     

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.     

The measurement,prediction, and development of soil management zones in low-relief sodic soils

Inexpensive, accurate, and rapid measurements of sodicity are required to identify the restoration options for degraded sites. This study determined the spatial variability of the percent of ammonium acetate extractable Na (%Na), apparent electrical conductivity (EC_a), pH_1:1, elevation and topographic wetness index, and used this information to create %Na management zones. In an 8.1 ha North Dakota field that contained Natraquolls and Calciaquolls, 1088 soil samples from the 0–0.3 and 0.3–0.6 m were collected from a 12.2 by 12.2 m geo-referenced grid. At each grid point, the elevation and ECa was determined using a differential corrected global positioning system and EM38m, respectively. Soil samples were analyzed for the %Na, EC_1:1, pH_1:1, and soil dispersion. Exponential semi-variogram models explained 96.7% of the ln-transformed %Na data in the 0–0.3 m soil depth, and %Na was correlated to EC_1:1 (r = 0.54), pH_1:1 (r = 0.68), clay dispersion (r = 0.68), ECav (r = 0.49), and ECah (r = 0.57). Forward stepwise regression models based on elevation, EC_1:1, pH_1:1, and ECah explained 64 and 74% of the %Na variability in the surface 0.3 m and subsurface 0.3–0.6 m, respectively. Management zones were identified that reduced the %Na variability up to 82%.     

Using apparent soil electrical conductivity (EC<Subscript>a</Subscript>) to characterize vineyard soils of high clay content

The adoption of precision viticulture requires a detailed knowledge of variation in soil chemical, physical and profile properties. This study evaluates the usefulness of apparent electrical conductivity (EC_a) data within a GIS framework to identify variations in soil chemical and physical properties and moisture content. The work was conducted in a vineyard located in the Carneros Region (Napa Valley, California). The soil was sampled using 44 boreholes to quantify chemical and physical characteristics and 9 open pits to verify the borehole observations. Moisture content was determined using time domain reflectometry (TDR). To characterize soil EC_a, three campaigns were undertaken using a soil electrical conductivity meter (EM38). Linear regressions between soil EC_a and soil properties were determined. Boreholes and TDR data were interpolated by kriging to characterize the spatial distribution of soil variables. The resulting maps were compared to the results obtained using the best EC_a linear regressions. Using EC_a measurements, soil properties like extractable Na⁺ and Mg²⁺, clay and sand content were well estimated, while best estimates were obtained for extractable Na⁺ (r ²  = 0.770) and clay content (r ²  = 0.621). The best estimates for soil moisture content corresponded to moisture in the deeper soil horizons (r ²  = 0.449). The methods described above provided maps of soil properties estimated by EC_a in a GIS framework, and could save time and resources during vineyard establishment and management.     

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.     

The delineation of agricultural management zones with high resolution remotely sensed data

Remote sensing (RS) techniques have been widely considered to be a promising source of information for land management decisions. The objective of this study was to develop and compare different methods of delineating management zones (MZs) in a field of winter wheat. Soil and yield samples were collected, and five main crop nutrients were analyzed: total nitrogen (TN), nitrate nitrogen (NN), available phosphorus (AP), extractable potassium (EP) and organic matter (OM). At the wheat heading stage, a scene of Quickbird imagery was acquired and processed, and the optimized soil-adjusted vegetation index (OSAVI) was determined. A fuzzy k-means clustering algorithm was used to define MZs, along with fuzzy performance index (FPI), and modified partition entropy (MPE) for determining the optimal number of clusters. The results showed that the optimal number of MZs for the present study area was three. The MZs were delineated in three ways; based on soil and yield data, crop RS information and the combination of soil, yield and RS information. The evaluation of each set of MZs showed that the three methods of delineating zones can all decrease the variance of the crop nutrients, wheat spectral parameters and yield within the different zones. Considering the consistent relationship between the crop nutrients, wheat yield and the wheat spectral parameters, satellite remote sensing shows promise as a tool for assessing the variation in soil properties and yield in arable fields. The results of this study suggest that management zone delineation using RS data was reliable and feasible.     

Effect of Daily Soil Temperature Fluctuations on Soil Electrical Conductivity as Measured with the Geonics® EM-38

Soil electrical conductivity (EC_a) measured by electromagnetic induction (EM) using the EM-38 has shown promise as a soil survey tool. Soil temperature influences EC_a readings, and temperature can fluctuate considerably in the upper 10cm of the soil during a day. EC_a readings were taken in the horizontal and vertical dipole orientations once an hour from 8a.m. to 8p.m. at four sites on three separate days to determine if EC_a values were influenced by diurnal temperature variations. Soil temperature readings were taken at the same times at four depths. EM-38 readings remained steady at all four sites all 3days. Linear regression analysis when temperature in the upper 10cm was plotted against EC_a yielded low r ² values and slopes, indicating no correlation between soil temperature in the upper 10cm and EC_a values. Diurnal changes in soil temperature do not significantly influence soil EC_a readings obtained with the EM-38 under the conditions encountered during the study.     

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.     

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.     

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.     

Soil electrical conductivity as a function of soil water content and implications for soil mapping

Apparent soil electrical conductivity (EC_a) has shown promise as a soil survey tool in the Midwestern United States, with a share of this interest coming from the precision agriculture community. To fully utilize the potential of EC_a to map soils, a better understanding of temporal changes in EC_a is needed. Therefore, this study was undertaken to compare temporal changes in soil EC_a between different soils, to investigate the influence of changes in soil water content on soil EC_a, and to explore the impacts these EC_a changes might have on soil mapping applications. To this end, a 90 m long transect was established. Soil EC_a readings were taken in the vertical and horizontal dipoles at five points once every one to two weeks from June until October in 1999 and 2000. At the same time, soil samples were collected to a depth of 0.9 m for volumetric soil water content analysis. Soil EC_a readings were compared to soil water content. At four of the five sites linear regression analysis yielded r ² values of 0.70 or higher. Regression line slopes tended to be greater in lower landscape positions indicating greater EC_a changes with a given change in soil water content. Two of the soils had an EC_a relationship that changed as the soils became dry. This is an item of concern if EC_a is to be used in soil mapping. Results indicated that soil water content has a strong influence on the EC_a of these soils, and that EC_a has its greatest potential to differentiate between soils when the soils are moist. Soil water content is an important variable to know when conducting EC_a surveys and should be recorded as a part of any report on EC_a studies.     

Effects of plant density and mepiquat chloride application on cotton boll setting in wheat–cotton double cropping system

Sowing cotton directly after harvesting wheat in the Yangtze River Valley of China requires early mature of cotton without yield reduction. Boll-setting period synchronisation and more yield bolls distributed at the upper and middle canopy layers are also required for harvesting. The objective of this study is to quantify the individual and interaction effects of plant density and plant growth regulator mepiquat chloride(MC) on temporal and spatial distributions of yield bolls, as well as yield and yield components. During the 2013–2016 cotton growing seasons, the experiments were conducted on a shortseason cotton cultivar CRRI50 at Yangzhou University, China. Various combinations of plant density(12.0, 13.5 and 15.0 plants m~(–2)) and MC dose(180, 270 and 360 g ha~(–1)) were applied on cotton plants. The combination of 13.5 plants m~(–2) and 270 g ha~(–1) MC resulted in the greatest boll number per unit area, the highest daily boll setting number and more than 90% of bolls positioned within 45–80 cm above the ground. In conclusion, appropriate MC dose in combination of high plant density could synchronize boll-setting period and retain more bolls at the upper and middle canopy layers without yield reduction in the system of direct-seeded cotton after wheat harvest, and thus overcome the labor-intensive problem in current transplanting cropping system.     

磷钾肥对棉花产量构成因素及产量的影响

为探讨磷钾肥用量对棉花产量构成因素及产量的影响,采用二因素二次饱和最优设计方法,分析铃数、铃质量、果节数、脱落率、衣分等与产量的关系。结果表明,不同磷钾肥用量下,籽棉和皮棉分别增产9.27%~17.11%和11.34%~21.70%,衣分提高0.59%~1.94%,单铃质量增加0.12~0.49g,单株铃数增加0.82~3.00,单株果节数增加1.23~6.78,脱落率降低3.97%~7.39%。磷肥效应对铃质量的影响高于钾肥效应,对其他6项指标(单株果节数、单株铃数、衣分、籽棉产量、皮棉产量、脱落率)的影响表现为钾肥效应高于磷肥效应;95%置信区内,籽棉产量≥3 450.0kg/hm2、皮棉产量≥1 335.0kg/hm~2、衣分≥38.7%、单铃质量≥3.85g、单株铃数≥16.0、单株果节数≥40.0、脱落率≤55%时的P_2O_5、K_2O用量为132.11~204.70kg/hm~2和132.11~276.78kg/hm~2,平均值为168.41和204.45kg/hm~2。     

Relationships between temperature-light meteorological factors and seedcotton biomass per boll at different boll positions

Cotton growth and development are determined and influenced by cultivars,meteorological conditions,and management practices.The objective of this study was to quantify the optimum of temperature-light meteorological factors for seedcotton biomass per boll with respect to boll positions.Field experiments were conducted using two cultivars of Kemian 1 and Sumian 15 with three planting dates of 25 April(mean daily temperature(MDT)was 28.0 and 25.4°C in 2010 and 2011,respectively),25 May(MDT was 22.5 and 21.2°C in 2010 and 2011,respectively),and 10 Jun(MDT was 18.7 and 17.9°C in 2010 and 2011,respectively),and under three shading levels(crop relative light rates(CRLR)were 100,80,and 60%)during 2010 and 2011 cotton boll development period(from anthesis to boll open stages).The main meteorological factors(temperature and light)affected seedcotton biomass per boll differently among different boll positions and cultivars.Mean daily radiation(MDR)affected seedcotton biomass per boll at all boll positions,except fruiting branch 2(FB_2)fruting node1(FN_1).However,its influence was less than temperature factors,especially growing degree-days(GDD).Optimum mean daily maximum temperature(MDT_(max))for seedcotton biomass per boll at FB_(11)FN_3 was 29.9–32.4°C,and the optimum MDR at aforementioned position was 15.8–17.5 MJ m~(–2).Definitely,these results can contribute to future cultural practices such as rational cultivars choice and distribution,simplifying field managements and mechanization to acquire more efficient and economical cotton management.