Determining the within-field yield variability from seasonally changing soil conditions

Crop yield variations are strongly influenced by the spatial and temporal availabilities of water and nitrogen in the soil during the crop growth season. To estimate the quantities and distributions of water and nitrogen within a given soil, process-oriented soil models have often been used. These models require detailed information about the soil characteristics and profile architecture (e.g., soil depth, clay content, bulk density, field capacity and wilting point), but high resolution information about these soil properties, both vertically and laterally, is difficult to obtain through conventional approaches. However, on-the-go electrical resistivity tomography (ERT) measurements of the soil and data inversion tools have recently improved the lateral resolutions of the vertically distributed measurable information. Using these techniques, nearly 19,000 virtual soil profiles with defined layer depths were successfully created for a 30 ha silty cropped soil over loamy and sandy substrates in Central Germany, which were used to initialise the CArbon and Nitrogen DYnamics (CANDY) model. The soil clay content was derived from the electrical resistivity (ER) and the collected soil samples using a simple linear regression approach (the mean R² of clay = 0.39). The additional required structural and hydrological properties were derived from pedotransfer functions. The modelling results, derived soil texture distributions and original ER data were compared with the spatial winter wheat yield distribution in a relatively dry year using regression and boundary line analysis. The yield variation was best explained by the simulated soil water content (R² = 0.18) during the grain filling and was additionally validated by the measured soil water content with a root mean square error (RMSE) of 7.5 Vol%.     

A flexible approach to managing variability in grain yield and nitrate leaching at within-field to farm scales

We up-scaled the APSIM simulation model of crop growth, water and nitrogen dynamics to interpret and respond to spatial and temporal variations in soil, season and crop performance and improve yield and decrease nitrate leaching. Grain yields, drainage below the maximum root depth and nitrate leaching are strongly governed by interaction of plant available soil water storage capacity (PAWC), seasonal rainfall and nitrogen supply in the water-limited Mediterranean-type environment of Western Australia (WA). APSIM simulates the interaction of these key system parameters and the robustness of its simulations has been rigorously tested with the results of several field experiments covering a range of soil types and seasonal conditions in WA. We used yield maps, soil and weather data for farms at two locations in WA to determine spatial and temporal patterns of grain yield, drainage below the maximum root depth and nitrate leaching under a range of weather, soil and nitrogen management scenarios. On one farm, we up-scaled APSIM simulations across the whole farm using local weather and fertiliser use data and the average PAWC values of soil type polygons. On a 70 ha field on another farm, we used a linear regression of apparent soil electrical conductivity (EC_a) measured by EM38 against PAWC to transform an EC_a map of the field into a high resolution (5 m grid) PAWC map. We then used regressions of simulated yields, drainage below the maximum root depth and nitrate leaching on PAWC to upscale the APSIM simulations for a range of weather and fertiliser management scenarios. This continuous mapping approach overcame the weakness of the soil polygons approach, which assumed uniformity in soil properties and processes within soil type polygons. It identified areas at greatest financial and environmental risks across the field, which required focused management and simulated their response to management interventions. Splitting nitrogen applications increased simulated wheat yields at all sites across the field and decreased nitrate leaching particularly where the water storage capacity of the soil was small. Low water storage capacity resulted in both low wheat yields and large leaching loss. Another management option to decrease leaching may be to grow perennial vegetation that uses more water and loses less by drainage.Paper from the 5th European Conference on Precision Agriculture (5ECPA), Uppsala, Sweden, 2005     

An approach for assessing the effects of site-specific fertilization on crop growth and yield of durum wheat in organic agriculture

Precision agriculture (PA) technologies allow us to assess field variability and support site-specific (SSP) application of inputs. The joint application of PA and organic farming practices might be synergetic. The objective of this 3-year study was to propose a multivariate statistical and geostatistical approach, to evaluate the effects of SSP nitrogen (N) fertilization on durum wheat in transition to organic farming. Soil parameters were measured to assess soil fertility level before the SSP fertilization on wheat, which was carried out by management zones in the third year. Radiometric measurements were performed with a hyperspectral spectroradiometer and N-uptake at anthesis and grain yield were determined. The expected values and 95 % confidence intervals of the soil parameters, N-uptake and yield data were estimated with polygon kriging for each management zone. Reflectance data were reduced through principal component analysis and the retained principal components were submitted to factorial co-kriging analysis to estimate orthogonal scale-dependent factors. Comparisons between N-uptake and yield and between the retained regionalized factors (F1) and yield were performed. The spatial pattern of F1 at shorter scales was mostly reproduced in the N-uptake map, suggesting the predictive capacity of hyperspectral data for crop N-status. Within-cluster variance for yield was reduced, quite probably as a combined effect of meteorological pattern and management. The preliminary results seem to be promising in the perspective of PA. Moreover, an inverse relationship between grain yield and crop N-status was observed.     

作物模型研究进展

作物模型是用来模拟作物生长、发育和产量形成的动态生长过程的计算机软件.自20世纪60年代以来,作物模型已从幼年期发展到成熟期,目前已成为各国农业科学研究中最有力的工具之一.文中对作物模型的研究历程和进展进行了综述,并总结了我国在作物生长模型研究方面的经验和不足,为今后的模型研究和应用提供参考.     

Development of an in-season estimate of yield potential utilizing optical crop sensors and soil moisture data for winter wheat

When utilizing optical sensors to make in-season agronomic recommendations in winter wheat, one parameter often required is the in-season grain yield potential at the time of sensing. Current estimates use an estimate of biomass, such as normalized difference vegetation index (NDVI), and growing degree days (GDDs) from planting to NDVI data collection. The objective of this study was to incorporate soil moisture data to improve the ability to predict final grain yield in-season. Crop NDVI, GDDs that were adjusted based upon if there was adequate water for crop growth, and the amount of soil profile (0–0.80 m) water were incorporated into a multiple linear regression model to predict final grain yield. Twenty-two site-years of N fertility trials with in-season grain yield predictions for growth stages ranging from Feekes 3 to 10 were utilized to calibrate the model. Three models were developed: one for all soil types, one for loamy soil textured sites, and one for coarse soil textured sites. The models were validated with 11 independent site-years of NDVI and weather data. The results indicated there was no added benefit to having separate models based upon soil types. Typically, the models that included soil moisture, more accurately predicted final grain yield. Across all site years and growth stages, yield prediction estimates that included soil moisture had an R² = 0.49, while the current model without a soil moisture adjustment had an R² = 0.40.     

Modeling the biomass of energy crops:Descriptions,strengths and prospective

The assessment of the biomass of energy crops has garnered widespread interest since renewable bioenergy may become a substantial proportion of the future energy supply, and modeling has been widely used for the simulation of energy crops yields. A literature survey revealed that 23 models have been developed or adapted for simulating the biomass of energy crops, including Miscanthus, switchgrass, maize, poplar, willow, sugarcane, and Eucalyptus camaldulensis. Three categories(radiation model, water-controlled crop model, and integrated model with biochemical and photosynthesis and respiration approaches) were addressed for the selected models according to different principles or approaches used to simulate biomass production processes. EPIC, ALMANAC, APSIM, ISAM, MISCANMOD, MISCANFOR, SILVA, DAYCENT, APEX and SWAT are radiation models based on a radiation use efficiency approach(RUE) with few empirical and statistical parameters. The Aqua Crop model is a typical water-crop model that emphasizes crop water use, the expression of canopy cover, and the separation of evapotranspiration to soil evaporation and plant transpiration to drive crop growth. CANEGRO, 3PG, Crop Syst and DSSAT are integrated models that use photosynthesis and respiration approaches. SECRETS, LPJm L, Agro-BGC, Agro-IBIS, and WIMOVAC/Bio Cro, DNDC, DRAINMOD-GRASS, and Ag TEM are integrated models that use biochemical approaches. Integrated models are mainly mechanistic models or combined with functional models, which are dynamic with spatial and temporal patterns but with complex parameters and large amounts of input data. Energy crop models combined with process-based models, such as EPIC in SWAT and CANEGRO in DSSAT, provide good examples that consider the biophysical, socioeconomic, and environmental responses and address the sustainability and socioeconomic goals for energy crops. The use of models for energy crop productivity is increasing rapidly and encouraging; however, relevant databases, such as climate, land use/land cover, soil, topography, and management databases, arescarce. Model structure and design assumptions, as well as input parameters and observed data, remain a challenge for model development and validation. Thus, a comprehensive framework, which includes a high-quality field database and an uncertainty evaluation system, needs to be established for modeling the biomass of energy crops.     

Developing a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) and its validation over the Northeast China Plain

Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing(RS) approaches limited their applications over broad regions. In this study, a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) was developed to estimate regional maize yield, and it was implemented using eight data-model coupling strategies(DMCSs) over the Northeast China Plain(NECP). Simulations under eight DMCSs were validated against the prefecture-level statistics(2010–2012) reported by National Bureau of Statistics of China, and inter-compared. The 3-year averaged result could give more robust estimate than the yearly simulation for maize yield over space. A 3-year averaged validation showed that prefecture-level estimates by PRYM–Maize under DMCS8, which coupled with the development stage(DVS)-based grain-filling algorithm and RS phenology information and leaf area index(LAI), had higher correlation(R, 0.61) and smaller root mean standard error(RMSE, 1.33 t ha~(–1)) with the statistics than did PRYM–Maize under other DMCSs. The result also demonstrated that DVS-based grain-filling algorithm worked better for maize yield than did the harvest index(HI)-based method, and both RS phenology information and LAI worked for improving regional maize yield estimate. These results demonstrate that the developed PRYM–Maize under DMCS8 gives reasonable estimates for maize yield and provides scientific basis facilitating the understanding the spatial variations of maize yield over the NECP.     

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.     

Estimation of the Importance of Spatially Variable Nitrogen Application and Soil Moisture Holding Capacity to Wheat Production

Crop growth modelling techniques were used to investigate the performance of a wheat crop over a range of weather conditions, nitrogen application rates and soil types. The data were used to predict long term benefits of using spatially variable fertilizer application strategies where fertilizer application rate was matched to the soil type, against a strategy of uniform fertilizer application. The model was also run with modified soil properties to determine the importance of soil moisture holding capacity in the variability of crop yield. It was found that the benefits of spatially variable nitrogen management when fertilizer was applied at the beginning of the season were modest on average. The range of results for different weather conditions was much greater than the average benefit. A large proportion of the variability of crop performance between soil types could be explained by differing soil moisture holding capacity. Devising techniques for managing this variability was concluded to be important for precision farming of cereals.Agricultural Engineering     

Assessment and visualization of spatial interpolation of soil pH values in farmland

Site-specific farming entails fine-scale detection of field parameters that affect yield coupled with directing appropriate management inputs to select areas that improve field-scale cropping system profitability. Currently, limited technologies are available to evaluate spatial variability in soil properties on a fine scale (submeter resolution). Therefore, information is typically generated by collecting discrete samples and utilizing spatial interpolation to estimate data for the unsampled locations. In this study, soil pH samples were collected from a 12.15 ha agricultural field in northwest Missouri using two grid-sampling regimes: 0.11 ha with 110 samples and 0.98 ha with 12 samples. Three spatial interpolation methods (inverse distance weighted, spline and kriging) were tested to evaluate the effects of interpolation on unsampled locations. In addition to quantitative validation evaluations, results were also assessed by 2D visualization and 3D visualization. Although each assessment approach provided useful information, the inverse distance weighted technique overall better-estimated soil pH values as determined by a combination of all three approaches.     

中国玉米遥感估产区划研究

农作物遥感估产区划是大面积农作物遥感估产研究和实践的基础。根据农作物遥感估产技术的具体要求,结合农作物区划理论,重点考虑春玉米和夏玉米估产,提出了我国玉米遥感估产区划的原则和依据,针对玉米遥感估产中的各项工作,分别作出了全国玉米遥感估产最佳时相、信息源和土地利用结构分类方案,并在此基础上,设计区划指标,把全国分为13个估产区和28个估产亚区。     

淮北砂姜黑土地力贡献与培肥技术研究

对杨柳点长期定位培肥试验和相关小麦施肥试验资料进行分析,结果表明:地力贡献与小麦施肥产量、地力贡献率之间呈线性正相关,高产田块地力贡献在小麦施肥产量中占主要地位。砂姜黑土地力贡献主要受土壤有机质和速效磷含量的影响,速效钾含量尚不是限制因子。长期连续施肥条件下．不同的土壤养分指标受有机无机配比的影响不同。有机质和氮素养分含量偏低的土壤,要通过增施有机肥加以培肥,磷素或钾素养分偏低的土壤可以通过增施有机肥或化肥来培肥。     

Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model

Understanding relationships of soil and field topography to crop yield within a field is critical in site-specific management systems. Challenges for efficiently assessing these relationships include spatially correlated yield data and interrelated soil and topographic properties. The objective of this analysis was to apply a spatial Bayesian hierarchical model to examine the effects of soil, topographic and climate variables on corn yield. The model included a mean structure of spatial and temporal co-variates and an explicit random spatial effect. The spatial co-variates included elevation, slope and apparent soil electrical conductivity, temporal co-variates included mean maximum daily temperature, mean daily temperature range and cumulative precipitation in July and August. A conditional auto-regressive (CAR) model was used to model the spatial association in yield. Mapped corn yield data from 1997, 1999, 2001 and 2003 for a 36-ha Missouri claypan soil field were used in the analysis. The model building and computation were performed using a free Bayesian modeling software package, WinBUGS. The relationships of co-variates to corn yield generally agreed with the literature. The CAR model successfully captured the spatial association in yield. Model standard deviation decreased about 50% with spatial effect accounted for. Further, the approach was able to assess the effects of temporal climate co-variates on corn yield with a small number of site-years. The spatial Bayesian model appeared to be a useful tool to gain insights into yield spatial and temporal variability related to soil, topography and growing season weather conditions.

大别山南坡土壤钙素状况及含钙肥料施用效果初步研究

大别山南坡土壤以发育于花岗片麻岩的酸性粗质土为主,含钙量低,在充分补给氮、磷、钾等大量元素肥料的情况下,作物缺钙的症状日渐显露。对于钙敏感作物及时补给钙素肥料,可显著提高其产量并改善其品质。     

Independence of yield potential and crop nitrogen response

Crop yield level and nitrogen (N) responsiveness influence the demand for fertilizer. If they were found to be unrelated, this would justify using a combination of both for determining fertilizer N requirements. Failure to understand the independence of crop response to N and yield level has led to confusion as to what theory is appropriate for making N fertilizer rate recommendations. The sufficiency approach applies a fixed rate of N at a computed sufficiency level, regardless of yield potential. Alternatively, mid-season optical sensor estimates of yield potential and crop response to additional N provide a physiological basis to estimate N removal and a biologically based N application rate. This study investigated the relationship between grain yield and response to N in long-term wheat and corn experiments. No relationship between response to N and grain yield was found. There was also no relationship between yield and year at two of three sites. Finally, there was no relationship between response to N and year at any site. Because yield and response to N were consistently independent of one another, and as both affect the demand for fertilizer N, estimates of both should be combined to calculate realistic in-season N rates.     

村域农田氮素排放特征研究——以新安村为例

为揭示村域氮素排放特征,以河北正定新安村为例,通过2016—2018年两个轮作周期对农户作物管理、作物产量等信息实地调研、取样分析,运用NUFER-Farm模型系统,研究了新安村氮素时空排放特征及其与作物种类、施氮量、灌溉、土壤质地等的关系。结果表明,两个轮作周期单位面积农田氮素总排放量和氧化亚氮排放量差异不显著,而硝态氮淋洗量和氨挥发量差异显著,第二个轮作周期单位面积农田硝态氮淋洗量和氨挥发量分别比第一个轮作周期增长60.1%和减少13.8%,造成差异显著的原因主要是气象条件和作物种类。季节上,两年单位面积农田氮素总排放量均为秋冬春季显著大于夏季。村内临近田块氮素排放差异较大,空间分布规律不明显;但到村域尺度,不同方位氮素排放空间分布上存在一定规律,如西北、东北、东南、西南方位单位面积农田氮素总排放量平均值分别为66.8、60.2、59.6、52.3 kg N·hm~(-2),其中西南方位氮素排放显著低于其他方位。村域农田氮素排放受到作物种类、施氮量、灌溉次数与土壤质地等因素显著影响。其中当地主要作物冬小麦、夏玉米和大豆单位面积农田氮素总排放平均值分别为40.5、28.5 kg N·hm~(-2)和5.3 kg N·hm~(-2),差异显著;两个轮作体系氮素总排放量均随施氮量、灌溉次数的增加而呈现增加的趋势;土壤质地对农田氮素总排放量也有较大影响,其中砂土、砂壤土和壤土单位面积农田氮素总排放平均值分别为78.2、60.4 kg N·hm~(-2)和51.0 kg N·hm~(-2),依次降低。总之,村域农田氮素排放具有较大的时空差异,更多受到田块作物种类、土壤条件、管理因素的影响,因此,村域氮素减排要针对田块采取优化施氮、节水灌溉、调整作物布局等措施。     

作物水分信息采集技术与采集设备

随着社会经济的发展和科学技术的不断进步，农田灌溉正朝着“自动、精准”的方向发展。实现自动、精准灌溉，需要获得及时、准确的作物水分状况信息作为基本依据，而先进、可靠的采集技术与设备则是快速、准确、连续获取作物水分信息的重要保障。作物水分信息，根据其采集部位可分为土壤信息和作物信息两类；而根据采集信息所代表
表的范围，则可分为点源信息和区域信息两类。2种分类结果相组合，可以将作物水分信息分为点源土壤水分状况信息，区域土壤水分状况信息，点源作物水分状况信息和区域作物水分状况信息四大类O目前应用较多的点源土壤水分状况信息快速采集技术主要有中子仪法、时域反射法（TDR）、频域反射法（FDR）、驻波率法（SWR）和张力计法；点源作物水分状况信息的采集技术则主要有红外温度法、叶水势法、光谱法、茎变差法和蒸腾速率法；区域土壤水分状况信息的采集技术主要有遥感法（裸地表层土壤）和墒墒情监测网络法；区域作物水分状况信息则主要通过遥感方法获得，包括热红外遥感和微波遥感等方法。这些技术方法各有优点、缺点和适用范围。从目前的研究和实际应用情况看，基于土壤介电特性的土壤水分信息测量技术（TDR，FD和SWR）和基于植株蒸腾速率、植株茎直径变差和作物冠层红外温度的作物水分状况信息测量技术是具有明显优优势和良好发展潜力的点源水分信息采集技术；以TDR、FDR和SWR为基础，结合GPS和GSM／GPRS无线数据传输系统，适用于区域土壤水分信息的采集；而以热红外遥感和微波遥感为基础的系统则是大面积的区域土壤水分状况信息（裸土表层）和区域作物水分状况信息的主要采集方法。这些作物水分信息采集方法的进一步完善提高，以及相应的精度高、稳定性好、价格适中的各类传感器及配套的数据处理设备的研制将是未来作物需水信息采集领域的重点工作目标。     

An on-farm approach to quantify yield variation and to derive decision rules for site-specific weed management

Grain yield often varies within agricultural fields as a result of the variation in soil characteristics, competition from weeds, management practices and their causal interactions. To implement appropriate management decisions, yield variability needs to be explained and quantified. A new experimental design was established and tested in a field experiment to detect yield variation in relation to the variation in soil quality, the heterogeneity of weed distribution and weed control within a field. Weed seedling distribution and density, apparent soil electrical conductivity (EC_a) and grain yield were recorded and mapped in a 3.5 ha winter wheat field during 2005 and 2006. A linear mixed model with an anisotropic spatial correlation structure was used to estimate the effect of soil characteristics, weed competition and herbicide treatment on crop yield. The results showed that all properties had a strong effect on grain yield. By adding herbicide costs and current grain price into the model, thresholds of weed density were derived for site-specific weed control. This experimental approach enables the variation of yield within agricultural fields to be explained, and an understanding of the effects on yield of the factors that affect it and their causal interactions to be gained. The approach can be applied to improve decision algorithms for the patch spraying of weeds.     

Relationship Between Six Years of Corn Yields and Terrain Attributes

Crop yield, soil properties, and erosion are strongly related to terrain attributes. The objectives of our study were to examine the relationship between six years of corn (Zea mays L.) yield data and relative elevation, slope, and curvature, and to develop a linear regression model to describe the spatial patterns of corn yield for a 16 ha field in central Iowa, USA. Corn grain yield was measured in six crop years, and relative elevation was measured using a kinematic global positioning system. Slope and curvature were then determined using digital terrain analysis. Our data showed that in the four years with less than normal growing season precipitation, corn yield was negatively correlated with relative elevation, slope, and curvature. In the two years with greater than normal precipitation, yield was positively correlated with relative elevation and slope. A multiple linear regression model based on relative elevation, slope, and curvature was developed that predicted 78% of the spatial variability of the average yield of the transect plots for the four dry years. This model also adequately identified the spatial patterns within the entire field for yield monitor data from 1997, which was one of the dry years. The relationship between terrain attributes and corn yield spatial patterns may provide opportunities for implementing site-specific management.