Spatio-temporal analysis of yield and weather data for defining site-specific crop management zones

Precision Agriculture - Understanding yield potential and yield-limiting factors is essential for improving profitability and grain yields while avoiding adverse environmental effects. In the USA,...     

A technical opportunity index based on mathematical morphology for site-specific management: an application to viticulture

The aim of this paper is to provide a method that enables a farmer to: (i) decide whether or not the spatial variation of a field is suitable for a reliable variable-rate application, (ii) to determine if a particular threshold (field segmentation) based on the within-field data is technically feasible with respect to the equipment for application, and (iii) to produce an appropriate application map. Our method provides a Technical Opportunity index (TO_i). The novelty of this approach is to process yield data (or other within-field sources of information) with a mathematical morphological filter based on erosions and dilations. This filter enables us to take into account how the machine operates in the field and especially the minimum area (kernel) within which it can operate reliably. Tests on theoretical fields obtained by a simulated annealing procedure and on a real vineyard showed that the TO_i was appropriate for assessing whether the spatial variation in a field was technically manageable.     

An integrated approach to site-specific management zone delineation

Dividing fields into a few relatively homogeneous management zones (MZs) is a practical and cost-effective approach to precision agriculture. There are three basic approaches to MZ delineation using soil and/or landscape properties, yield information, and both sources of information. The objective of this study is to propose an integrated approach to delineating site-specific MZ using relative elevation, organic matter, slope, electrical conductivity, yield spatial trend map, and yield temporal stability map (ROSE-YSTTS) and evaluate it against two other approaches using only soil and landscape information (ROSE) or clustering multiple year yield maps (CMYYM). The study was carried out on two no-till corn-soybean rotation fields in eastern Illinois, USA. Two years of nitrogen (N) rate experiments were conducted in Field B to evaluate the delineated MZs for site-specific N management. It was found that in general the ROSE approach was least effective in accounting for crop yield variability (8.0%–9.8%), while the CMYYM approach was least effective in accounting for soil and landscape (8.9%–38.1%), and soil nutrient and pH variability (9.4%–14.5%). The integrated ROSE-YSTTS approach was reasonably effective in accounting for the three sources of variability (38.6%–48.9%, 16.1%–17.3% and 13.2%–18.7% for soil and landscape, nutrient and pH, and yield variability, respectively), being either the best or second best approach. It was also found that the ROSE-YSTTS approach was effective in defining zones with high, medium and low economically optimum N rates. It is concluded that the integrated ROSE-YSTTS approach combining soil, landscape and yield spatial-temporal variability information can overcome the weaknesses of approaches using only soil, landscape or yield information, and is more robust for MZ delineation. It also has the potential for site-specific N management for improved economic returns. More studies are needed to further evaluate their appropriateness for precision N and crop management.     

A technical opportunity index based on the fuzzy footprint of a machine for site-specific management: an application to viticulture

This paper describes a method that allows farmers to (i) decide whether or not the spatial variation of a field allows a reliable variable-rate application, (ii) discover if a particular threshold (field segmentation) based on within-field data is technically feasible according to the application machinery and (iii) make an appropriate application map. Our method aims to improve on a previous technical opportunity index (Oi) with a fuzzy technical opportunity index (FTOi). The FTOi considers (i) a fuzzy footprint model of a variable-rate application controller (VRAC), which describes the area within which the VRAC can operate reliably, (ii) the location inaccuracy of the data and (iii) the ability (accuracy) of the VRAC to perform distinct levels of treatments. The originality of our approach is based on the use of a fuzzy estimation process to decide if a level of treatment is reliable or not for each area over which the VRAC operates. A unique feature of the approach is that it does not require data on a regular grid. Only characteristics of the machinery and the treatment to be applied are necessary; interpolation of the data and geostatistics are not required by the end user. Tests on theoretical fields, obtained from a simulated annealing procedure, showed that the FTOi was able to assess the technical manageability of variation in the field. Tests also showed that our approach could take into account problems related to low resolution data. Finally, the approach has been applied to a real situation in a vineyard block. This has highlighted the practical implementation and the ability to generate useful information for managing the within-field variation (optimal thresholding, and application and error maps).     

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.     

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.     

An approach to forecast grain crop yield using multi-layered,multi-farm data sets and machine learning

Precision Agriculture - Many broadacre farmers have a time series of crop yield monitor data for their fields which are often augmented with additional data, such as soil apparent electrical...     

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.     

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.     

Protocol to support model selection and evaluation in a modular crop modelling framework: An application for simulating crop response to nitrogen supply

Crop models require different structures for different applications. Modular and flexible crop modelling frameworks, such as the recently developed agricultural production and externalities simulator (APES), support the change of model structure. However, the assembly of different modules to create a model may not always result in the best model structure. We developed and tested a protocol for a systematic selection and evaluation of a crop growth model structure. The novelty of the presented protocol relies on a throughout analysis of the different modelling approaches (modules) and on how to assemble them to create new modelling solutions (i.e. model). We use a case study to demonstrate that we can explicitly express and test the different assumptions behind the choice of a specific modelling approach. Our case study refers to the simulation of crop growth in response to nitrogen management and the importance of an accurate simulation of the nitrogen uptake. Applying the proposed protocol, we identify the need to improve the initially selected nitrogen mineralisation module. We conclude that not only is the protocol suitable to provide guidance for systematic testing of different crop processes modelled, but also its use highlights the importance of the documentation of the modelling process and of the clarification of the uncertainty associated with the model structure.     

作物生长期低温指数演变及对作物产量的影响

【目的】分析1971-2014年黑龙江省作物生长期基于最低气温的低温指数(cold day index,CDI)演变及对作物产量的影响,为揭示作物生长期低温冷害机理、甄别关键致灾因子等提供技术支撑。【方法】利用黑龙江省80个气象观测站1971-2014年气象资料和38个农业气象观测站玉米和水稻单产资料,参考已有研究成果和公认的农业气象指标,构建作物生长期内(05-21-09-30)CDI的计算方法,采用数理统计法研究CDI的变化趋势和周期变化,并分析其与作物产量的相关关系。【结果】(1)1971-2014年,作物生长期内研究区平均CDI呈明显下降趋势,下降速度为1.5d/10年。20世纪70年代为低温事件高发期,80-90年代相对稳定,21世纪以来平原区CDI持续减少,而山区无变化或呈回升趋势。(2)研究期内,研究区CDI的空间分布呈北多南少、山区多平原少的趋势,CDI由北向南、由山区向平原逐渐下降。CDI的气候变率在不同区域存在差异,呈现西高东低特征。(3)在作物生长期内,研究区89%站点的CDI与玉米、水稻单产具有显著或极显著的负相关关系,表明低温对作物产量的影响为负效应。不同区域CDI引起作物产量的下降幅度不同,CDI每增加1d,产量下降92.9~695.5kg/hm2。【结论】CDI对作物生长和产量形成具有显著负效应,其对松嫩平原东部、三江平原和牡丹江半山区玉米、水稻影响较大,且对水稻产量的影响重于玉米。     

基于作物产量指标和聚类分析的立地分类比较研究(英文)

对棉地评价中基于作物产量指标和对土地属性作聚类分析的立地分类进行比较研究。结果表明，从作物产量指标分析中可得出三个等级：适宜级Ⅰ（产量>900kg/hm2），适宜级Ⅱ（产量500-900kg/hm2），适宜级Ⅲ（产量<500kg/hm2）。而聚类分析中通过对86个样本进行统计计算也得出三个等级。两种分类方法的基本结论没有太大分歧，但通过聚类分析得出的立地分类与基于作物产量指标的立地分类并不完全一致。研究结果也清楚地表明，经过适当的修正，作物产量指标分析法可以被可靠地运用于棉地评价的立地分类中。     

‘User friendly’ software for an integrated water-energy management system for center pivot irrigation

Computers are becoming more commonly used by farmers for improving their management. The big need is ‘user friendly’ software for use by farmers. An integrated water-energy management system was developed. It includes ‘user friendly’ software run on a microcomputer which communicates to center pivot irrigation systems via radio. The program provides monitoring, control, irrigation scheduling and electrical load control. The system has been used and accepted by our cooperator for three irrigation seasons. He has been able to improve his management and reduce the amount of overirrigation and the variation between systems of total water applied.     

Modelling impacts of precision irrigation on crop yield and in-field water management

Precision irrigation technologies are being widely promoted to resolve challenges regarding improving crop productivity under conditions of increasing water scarcity. In this paper, the development of an integrated modelling approach involving the coupling of a water application model with a biophysical crop simulation model (Aquacrop) to evaluate the in-field impacts of precision irrigation on crop yield and soil water management is described. The approach allows for a comparison between conventional irrigation management practices against a range of alternate so-called ‘precision irrigation’ strategies (including variable rate irrigation, VRI). It also provides a valuable framework to evaluate the agronomic (yield), water resource (irrigation use and water efficiency), energy (consumption, costs, footprint) and environmental (nitrate leaching, drainage) impacts under contrasting irrigation management scenarios. The approach offers scope for including feedback loops to help define appropriate irrigation management zones and refine application depths accordingly for scheduling irrigation. The methodology was applied to a case study in eastern England to demonstrate the utility of the framework and the impacts of precision irrigation in a humid climate on a high-value field crop (onions). For the case study, the simulations showed how VRI is a potentially useful approach for irrigation management even in a humid environment to save water and reduce deep percolation losses (drainage). It also helped to increase crop yield due to improved control of soil water in the root zone, especially during a dry season.     

Suitability of satellite remote sensing data for yield estimation in northeast Germany

Precision Agriculture - Information provided by satellite data is becoming increasingly important in the field of agriculture. Estimating biomass, nitrogen content or crop yield can improve farm...     

Effects of nitrogen management on the ratoon crop yield and head rice yield in South USA

Ratoon rice cropping is an important component of the rice cropping system in Texas and south Louisiana, USA, and expanded to Asian countries in 1970. Two field studies were conducted with widely planted rice(Oryza sativa L.) cultivars at Eagle Lake, Texas, USA to determine the effects of nitrogen(N) management in main(first) crop(MC) and ratoon(second) crop(RC) on RC yield. In 2012 and 2013, one cultivar(Presidio) was adopted to determine the effects of RC N management on ratoon yield and head rice yield. In 2016 and 2017, CL153, CL163 and CL272 in addition to Presidio were adopted to examine the effect of MC N management on ratoon yield and head rice yield. N applied at preflood after MC harvest considerably improved RC yield. Application of 99 kg N ha–1 at preflood after MC harvest was practically adequate for RC regrowth, development and approaching the yield potential for Presidio. RC could produce quite high average grain yields of 5.90 to 6.53 t ha–1 in 2012 and 2013, respectively. Main crop N rate only significantly affected MC yield; however, given N applied of 99 kg ha–1 at preflood after MC harvest, ratoon yield was not significantly affected by MC N rate. Neither the main nor ratoon crop N management had a significant effect on RC head rice yield. Considerable RC head rice yields(55–65%) were observed in all of the four cultivars and 4 years except for CL272 in 2016. These results indicat that without very high N fertilizer application, rice ratoon crop could produce a considerable grain yield and an expectative head rice yield. Rice ratooning could be a practical way to increase rice yields with the minimal input in south Texas and regions with a similar climate.     

A new localized sampling method to improve grape yield estimation of the current season using yield historical data

Precision Agriculture - This paper proposes a methodology to improve grape yield sampling and yield estimation of the current season by using historical yield data. This approach is based on the...