The early explanatory power of NDVI in crop yield modelling

The objective of this paper is to study, on a weekly basis, the explanatory power of one satellite‐based measurement, the Normalized Difference Vegetation Index (NDVI), for wheat yield modelling in 40 census agricultural regions (CAR) in the Canadian Prairies during the whole growing season using 16 years of NOAA AVHRR satellite data (between 1987 and 2002). We also explore the relative value of NDVI compared with a land‐based measurement, the Cumulative Moisture Index (CMI). By developing a series of weekly wheat yield models over the course of the growing season, we are able to determine the accuracy of different models. Our findings indicate that NDVI possesses explanatory power 4 weeks earlier in the season than CMI.     

Airborne multi-spectral monitoring of agricultural crop status: effect of time of year,crop type and crop condition parameter

Airborne optical multispectral imagery was acquired in conjunction with contemporaneous ground-based measurements of various crops (leaf area index, canopy temperature, plant height) at a test site in southern Alberta, Canada. Data were acquired on three occasions in July 1994 for a variety of crops and irrigation practices. A large number of crop condition-spectral relations were examined to determine whether the imagery could be used to measure the various crop condition parameters. It was found that a number of statistically significant correlations exist between the imagery and the crop condition parameters and that these correlations vary as a function of crop type, time of year, and crop condition parameter. The results suggest that in many cases, multi-spectral optical imagery can be used to monitor variations in crop condition parameters across the growing season for a variety of crop types.     

Ergonomics investigation of retail ice cream operations

A comprehensive ergonomics evaluation of retail ice cream shops, including field and laboratory data collection, was conducted using a human:workplace model approach to ergonomics practice. The goal of the evaluation was to provide recommendations to enhance the health, safety, and productivity of shop employees. Active and passive surveillance and facility walk-throughs were used to guide the selection of analyses. A primary focus of the investigation was quantifying the task demands of scooping ice cream, which have not been documented in the literature. This goal was accomplished through the use of a custom-designed instrumented ice cream scoop. Data were collected at an ice cream shop under typical conditions, while the laboratory experiment investigated task demands of ice cream scooping over a range of realistic temperatures. Manual materials handling task analyses and anthropometric evaluations comprised the majority of other analyses performed. Recommendations are presented that are applicable to the operation of retail ice cream shops that serve hard (i.e., scooped) ice cream.     

The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan

We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems.     

包装厂商解决冰激凌难题

系统集成商对驱动系统进行了重新设计，安装了PLC和HMI，帮助Norse乳业系统公司减少了机器停机和切削损耗。     

The cream of other men's wit: Plagiarism and misappropriation in cyberspace

It began in 1816 when in a landmark privacy case, Lord Byron successfully enjoined Johnston from circulating a ‘bad poem’ which Johnston had falsely credited to him. Henceforth Byron v Johnston established what is known in privacy torts as ‘false light’ privacy. The concept of ‘false light’ privacy broadened privacy tort law by providing protection from “publicity which place[d] the plaintiff in a false light in the public eye”. Since then it has been successfully used to protect plaintiffs from having their names linked to literary works, political statements, and legal documents which they did not author.     

The significance of crop co-states for receding horizon optimal control of greenhouse climate

While a tomato crop grows on the time-scale of weeks, the greenhouse climate changes on a time-scale of minutes. The economic optimal control problem of producing good quality crops against minimum input of resources is tackled by a two time-scale decomposition. First, the sub-problem associated to the slow crop evolution is solved off-line, leading to a seasonal pattern for the co-states of the amount of assimilates produced by photosynthesis, and the fruit and leaf weights. These co-states can be interpreted as the marginal prices of a unit of assimilate, leaf and fruit. Next, they are used in the goal function of an on-line receding horizon control (RHOC) of the greenhouse climate, thus balancing costs of heating and CO₂-dosage against predicted benefits from harvesting, while profiting as much as possible from the available solar radiation. Simulations using the time-varying co-states are compared to experimental results obtained with fixed co-states. It appears that the on-line control is sensitive to the time evolution of the co-states, suggesting that it is advantageous to repeat the seasonal optimisation from time to time to adjust the co-states to the past weather and realised crop state.     

An integrated crop and hydrologic modeling system to estimate hydrologic impacts of crop irrigation demands

The present paper discusses a coupled gridded crop modeling and hydrologic modeling system that can examine the benefits of irrigation and costs of irrigation and the coincident impact of the irrigation water withdrawals on surface water hydrology. The system is applied to the Southeastern U.S. The system tools to be discussed include a gridded version (GriDSSAT) of the crop modeling system DSSAT. The irrigation demand from GriDSSAT is coupled to a regional hydrologic model (WaSSI). GriDSSAT and WaSSI are coupled through the USDA NASS CropScape data to provide crop acreages in each watershed. The crop model provides the dynamic irrigation demand which is a function of the weather. The hydrologic model responds to the weather and includes all other anthropogenic competing uses of water. Examples of the system include an analysis of the hydrologic impact of future expansion of irrigation and the real-time impact of short-term drought.     

Object-based crop identification using multiple vegetation indices, textural features and crop phenology

Crop identification on specific parcels and the assessment of soil management practices are important for agro-ecological studies, greenhouse gas modeling, and agrarian policy development. Traditional pixel-based analysis of remotely sensed data results in inaccurate identification of some crops due to pixel heterogeneity, mixed pixels, spectral similarity, and crop pattern variability. These problems can be overcome using object-based image analysis (OBIA) techniques, which incorporate new spectral, textural and hierarchical features after segmentation of imagery. We combined OBIA and decision tree (DT) algorithms to develop a methodology, named Object-based Crop Identification and Mapping (OCIM), for a multi-seasonal assessment of a large number of crop types and field status.In our approach, we explored several vegetation indices (VIs) and textural features derived from visible, near-infrared and short-wave infrared (SWIR) bands of ASTER satellite scenes collected during three distinct growing-season periods (mid-spring, early-summer and late-summer). OCIM was developed for 13 major crops cultivated in the agricultural area of Yolo County in California, USA. The model design was built in four different scenarios (combinations of three or two periods) by using two independent training and validation datasets and the best DTs resulted in an error rate of 9% for the three-period model and between 12 and 16% for the two-period models. Next, the selected DT was used for the thematic classification of the entire cropland area and mapping was then evaluated applying the confusion matrix method to the independent testing dataset that reported 79% overall accuracy. OCIM detected intra-class variations in most crops attributed to variability from local crop calendars, tree-orchard structures and land management operations. Spectral variables (based on VIs) contributed around 90% to the models, although textural variables were necessary to discriminate between most of the permanent crop-fields (orchards, vineyard, alfalfa and meadow). Features extracted from late-summer imagery contributed around 60% in classification model development, whereas mid-spring and early-summer imagery contributed around 30 and 10%, respectively. The Normalized Difference Vegetation Index (NDVI) was used to identify the main groups of crops based on the presence and vigor of green vegetation within the fields, contributing around 50% to the models. In addition, other VIs based on SWIR bands were also crucial to crop identification because of their potential to detect field properties like moisture, vegetation vigor, non-photosynthetic vegetation and bare soil. The OCIM method was built using interpretable rules based on physical properties of the crops studied and it was successful for object-based feature selection and crop identification.     

Variability of high-resolution crop reflectance spectra

Abstract

High-spectral-resolution reflectance spectra from ground and helicopter measurements of agricultural crops and soils were analyzed to determine spectral variability in the visible and near-infrared (0 4-2 38 μm), using a procedure previously applied to thermal infrared emittance spectra of the atmosphere and to reflectance spectra of soil samples. Five spectral basis functions were sufficient to describe separately the ground and helicopter data, six or, at most, seven are sufficient to describe the pooled data. Thus, five to seven relatively broad band measurements, together with basis functions developed in this analysis, are sufficient to describe the variability of both data sets, to within differences that are probably associated with the measurement process and instrument noise.     

Remote sensing of rice crop areas

Rice means life for millions of people and it is planted in many regions of the world. It primarily grows in the major river deltas of Asia and Southeast Asia, such as the Mekong Delta, known as the Rice Bowl of Vietnam, the second-largest rice-producing nation on Earth. However, Latin America, the USA, and Australia have extensive rice-growing regions. In addition, rice is the most rapidly growing source of food in Africa. Rice is therefore of significant importance to food security in an increasing number of low-income food-deficit countries. This review article gives a complementary overview of how remote sensing can support the assessment of paddy rice cultivation worldwide. This article presents and discusses methods for rice mapping and monitoring, differentiating between the results achievable using different sensors of various spectral characteristics and spatial resolution. The remote sensing of rice-growing areas can not only contribute to the precise mapping of rice areas and the assessment of the dynamics in rice-growing regions, but can also contribute to harvest prediction modelling, the analyses of plant diseases, the assessment of rice-based greenhouse gas (methane) emission due to vegetation submersion, the investigation of erosion-control-adapted agricultural systems, and the assessment of ecosystem services in rice-growing areas.     

The relations between spectral data and water in a crop production environment

The results of a correlation study between high spatial resolution (1m) Multi-Spectral Scanner (MSS) data and reference data with particular emphasis to soil-plant water parameters are presented. Digital images of the Management Systems Evaluation Area (MSEA) research site in south central Ohio were acquired using MSS mounted on an aircraft. Gravimetric soil water, plant water, and plant residue information relating to MSS data were collected in April, July, August, and September, 1994. It was found that the correlation between MSS data and soil water was changed by the presence of crop residue versus bare soil. The combined water estimates from the soil and plant enhanced the correlation structure between MSS parameters and soil-plant water variables. It was also shown that when using soil water content data below the 0.5-2.0 cm soil depth, the correlation with MSS parameters decreased.     

Simulating wheat crop residue reflectance with the SAIL model

Estimating crop residue is important for soil conservation and tillage management. Remote sensing could provide the potential of estimating amount of crop residue using reflectance measurement and model simulation procedures. The purpose of this study was (1) to use the SAIL (Scattering by Arbitrarily Inclined Leaves) model to simulate crop residue reflectance from wheat, Triticum aestivum (L.), at visible and near-infrared wavelengths; and (2) to compare the simulated reflectance with field-measured reflectance for evaluating the simulation model. Simulated reflectance in visible and near-infrared wavebands was overestimated about 1 to 5 per cent, compared with measured reflectance in the field. However, overestimation was within the experimental errors. Results suggest that the SAIL model can be used to simulate crop residue reflectance in different wheat crop residue covers and that wheat crop residue cover could be estimated by inverting the model.     

作物产量预测的粒度分析法

针对作物产量预测,提出基于商空间粒度计算的分析法。在商空间粒度计算理论思想下,分析作物产量序列中粒度的选取,用属性划分方法对论域X进行颗粒化,对属性f取不同的粒度进行颗粒化。通过属性的粒度变化对论域进行划分,得到新的商空间并应用其解决问题,可以降低问题复杂度。通过商空间理论中的分层与合成技术选取大小合适的粒度,能全面获取产量序列中的信息,也更加符合人类智能特点。冬小麦产量预测实验结果也证明这种粒度分析和选取方法是有效的。     

Scheduling contractors' farm-to-farm crop harvesting operations

The harvesting of renewable resources from an operations scheduling viewpoint is introduced, and a harvesting scenario arising in the agricultural context involving a commercial contracting enterprise that travels from farm‐to‐farm harvesting crops is discussed. This paper is an extension of previous work by two of the authors from the one‐farm to the multi‐farm case. In both cases, the duration of each operation is dependent upon the combination of constrained resources allocated to it, equipment and worker allocation is restricted, and minimum or maximum time lags on the start and completion of operations may be imposed. The present case incorporates harvesting at more than one farm and thus the sequence in which the farms are visited and the inter‐farm travel times must be taken into account. We report on a harvesting scheduling model and solution procedures designed specifically for large‐scale versions of the multi‐farm case. The computational times experienced in solving general instances of the model of small‐to‐medium practical size by a commercial integer programming package are encouraging. Greedy and tabu search heuristics, which are capable of solving problems of relatively large dimensions in reasonable computational time are also included. The authors believe that the model and the solution techniques developed represent a useful addition to the farm crop contractor's tool kit.     

Reflectance relations in row crop scenes

Abstract

Models that relate composite reflectance to its components are useful for inferring crop growth information from measured scene reflectance. Radiation measurements in Thematic Mapper bands (TM1, TM2, TM3, and TM4) were made from cotton, soybean, sunflower and grain sorghum at three stages of growth and used to evaluate three reflectance models. Two models, AIRM1 and AIRM2, assumed that scene components contribute in an additive independent manner to composite reflectance. The third model, TRIM, assumes that radiation transmitted through the canopy interacts with bare soil in two scene components. The AIRM2 and TRIM models divide the composite reflectance into canopy, bare soil, and shadow components, but AIRM1 considers only canopy and bare soil. Ranking of models in order of decreasing accuracy for predicting composite reflectance in bands TM3 and TM4 was AIRM2, TRIM, and AIRM1. The AIRM1 and AIRM2 models estimated average TM3 reflectance at full plant cover between 1 and 4 per cent for all crops. Their estimations in band TM4 were 60 per cent for cotton, soybean, and sunflower with grain sorghum being 50 percent.

Measured canopy and composite reflectances were graphically compared at the lowest and highest levels of canopy cover observed in each crop. Measured band TM3 canopy reflectance did not change with solar zenith angle, composite reflectance decreased with increasing zenith angle at the lowest canopy cover levels but was invariant at the highest canopy cover levels. Measured band TM4 canopy reflectance increased linearly with increasing solar zenith angle in all crops, but for composite reflectance this pattern was observed only at the highest canopy cover levels of cotton and soybean. The absence of a uniform pattern between band TM4 composite reflectance and solar zenith angle in grain sorghum is presumably due to large horizontal leaf angles and in sunflower to long vertical spacings between leaves. Predicted compared to measured band TM3 and TM4 composite reflectances of the AIRM1 and AIRM2 models were insensitive but the TRIM model was overly sensitive to zenith angle.     

Visualizing soil surfaces and crop residues

A test case for visualizing field and laboratory data pertaining to soil erosion and infiltration is examined. Several visualization techniques were used to derive image data from an experiment in which the effect of a sequence of five rainfalls on a laboratory-formed soil surface was measured. The base data are millimeter-resolution elevation models generated using a laser-scanning device. The surfaces generated by the laser scanner are visualized as perspective views. Changes in the soil surface with cumulative rainfall are examined by visualizing differences between successive laser-scanned surfaces. The modeling of straw coverage, which can influence infiltration, overland flow, and erosion, using a random process model is discussed     

Global sensitivity analysis measures the quality of parameter estimation: The case of soil parameters and a crop model

One common limitation of the use of crop models for decision making in precise crop management is the need for accurate values of soil parameters for a whole field. Estimating these parameters from data observed on the crop, using a crop model, is an interesting possibility. Nevertheless, the quality of the estimation depends on the sensitivity of model output variables to the parameters. The goal of this study is to explain the results for the quality of parameter estimation based on global sensitivity analysis (GSA). The case study consists of estimating the soil parameters by using the STICS-wheat crop model and various synthetic observations on wheat crops (LAI, absorbed nitrogen and grain yield). Suitable criteria summarizing the sensitivity indices of the observed variables were created in order to link GSA indices with the quality of parameter estimation. We illustrate this link on 16 different configurations of different soil, climatic and crop conditions. The GSA indices were computed by the Extended FAST method and a function of RMSE was computed with an importance sampling method based on Bayes theory (GLUE). The proposed GSA-based criteria are able to rank the parameters with respect to their quality of estimation and the different configurations (especially climate and observation set) with respect to their ability to estimate the whole parameter set. They may be used as a tool for predicting the performance of different observation datasets with regard to parameter estimation.     

Some observations on crop profile modelling

Abstract

LANDSAT data are typically available for a number of overpasses during a growing season. There is currently considerable interest in modelling the so-called crop profile for such multitemporal data by a non-linear profile function of some (spectral) index for the spectral bands. The derived coefficients are used in a subsequent allocation procedure. This paper outlines some results obtained from an evaluation of the approach for crop data from the wheatbelt of Western Australia. Specifically, the degree of separation of crop classes from pasture classes, as measured by the discriminant root, is compared for analyses based on the original bands, on various spectral indices and on fitted coefficients from a crop profile function describing the temporal change in these indices. For the data considered, a marked loss of discrimination is found for analyses based on various spectral indices, when compared with those based directly on the corresponding discriminant functions (where the linear combination is not constrained to be the same for each time). Analysis of the coefficients for the non-linear profile functions fitted to the indices results in further loss of separation.