مطالعه ارتباط مکانی رشد و عملکرد گندم زمستانه با علف‌های هرز با استفاده از روش‌های زمین آماری و سنجش از دور

به منظور بررسی همبستگی توزیع مکانی رشد و عملکرد گندم زمستانه با علف های هرز با استفاده از روش های زمین آماری و سنجش از دور، آزمایشی در سطح یک مزرعه گندم واقع در دانشکده کشاورزی دانشگاه صنعتی شاهرود در سال زراعی 97-1396 انجام شد. بدین منظور عملکرد بیولوژیک و دانه گندم منطبق بر محل اندازه‌گیری تراکم و زیست توده علف های هرز در 110 نقطه در سطح مزرعه در محل تقاطع سیستم شبکه‌ای 8 × 8 متر اندازه‌گیری و ثبت شد. به منظور بررسی عملکرد بیولوژیک و دانه گندم با استفاده از سنجش از دور نیز عکس هوایی با کوادکوپتر از ارتفاع 120 متری از سطح مزرعه تهیه شد و سپس ارزش عددی پیکسل های تصویر هوایی استخراج و مورد تجزیه و تحلیل قرار گرفت. نتایج نشان داد جمعیت و زیست توده علف های هرز با عملکرد بیولوژیک گندم همبستگی مکانی منفی 89/75 و 50/62 درصدی و با عملکرد دانه گندم همبستگی مکانی منفی 9/91 و 25/56 درصدی نشان داد. داده‌های حاصل از عکس هوایی نیز همبستگی مکانی قوی 23/88 و 12/78 درصدی به‌ترتیب با عملکرد بیولوژیک و دانه گندم نشان داد. براساس نتایج این پژوهش روش های زمین آماری و داده‌های تصویر هوایی به خوبی نشان داد که توزیع ناهمگون تراکم و زیست توده علف های هرز در سطح مزرعه می‌تواند سبب توزیع مکانی ناهمگون عملکرد بیولوژیک و دانه گندم گردد.

Spatial Dependence of Growth and Yield of Winter Wheat and Weeds Using Geostatistical and Remote Sensing Methods

Introduction: Understanding the spatial relationship of weed populations and crop growth and yield within fields is increasingly important as methods are being developed for the sitespecific management of weeds. Knowledge of spatial variability helps to improve weeds and crop management efficiency. It is well known that weed populations have a patchy distribution and a weed patch is considered stable if is consistent with density and location over time. Most of the spatial variability studies about weed populations have provided very precise information for sitespecific recommendations and they are referred to as density data. The knowledge of spatial patterns within a field is critical not only to farmers for potential variable rate applications, but also to select homogenous zones within the field to run crop models with sitespecific input to better understand and predict the impact of weeds, soil and landscape characteristics on spatial and temporal patterns of crop yields to enhance resource use efficiency at field level. Remote sensing and geostatistic methods have been widely explored as a tool for detection and mapping of weeds distribution in agricultural crops. Remote sensing is the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation. Thus, on the basis of different types of platforms carrying specific sensors, agricultural remote sensing could generally be categorized into satellite, aerial, and handheld or tractor mounted remote sensing. The crosssemivariogram analysis is one of geostatistical methods to describe the joint spatial dependence, or continuity between two variables such as crop yield and density of weeds and other pairs of variables that are influenced by the same local conditions or spatial processes. The objective of this study was to describe the spatial relationship between winter wheat and weeds using geostatistic and remote sensing.Material and Methods: Geostatistic and remote sensing techniques were used to describe spatial relationship between weeds distribution and wheat biological and grain yield on a winter wheat fields located in Faculty of Agriculture, Shahrood University of Technology in growing season 20172018. Wheat traits such as biological and grain yield, weed density, and biomass were measured at 110 points of field based on an 8×8 m grid. Also, in order to investigation of biological and grain yield of wheat using remote sensing, an aerial image was taken by a quadcopter from the height of 120 m and then numerical values of aerial image pixels extracted and analyzed using geostatistics methods. Before subjecting data to spatial statistical analysis, they were logtransformed and detrended by a median polishing procedure as described previously. To describe the cocontinuity of two variables (wheat yield and weeds populations) crosssemivariance was used. Semivariograms and crosssemivariograms were calculated both using GS+ and Variowin (Software for spatial data analysis in 2D, Spring Verlag, New York, USA) softwares, respectively.Results and Discussion: Semivariogram analysis showed that total weed density and biomass had patchy patterns on the field. Crosssemivariograms showed weed density and biomass had negative spatial correlation of 75.89 to 62.50% with wheat biological yield and negative spatial correlation of 91.9 and 56.25 with grain yield of wheat, respectively. Remote sensing data showed a strong correlation of 88.23 and 78.12% with biological and grain yield of wheat, respectively. High spatial dependence was observed between the maps of biological and grain yield of wheat. Also, weed density and biomass patterns on the maps of weeds were similar to patterns of wheat biological and grain yield on the maps. Competition for resources among plants has long been considered to generate stress for plants and to be important for determining the distribution of species, as well as their evolution. Weeds reduce crop yields primarily by competing with the crop for limited resources (light, water, nutrients, etc.). Knowledge of the distribution of weeds across the field creates an opportunity for sitespecific weed management. One potential way to utilize this information is through variablerate herbicide application (VRA). While mapping weeds via field scouting or remote sensing for precisely varying application can be expensive, photographs taken from an airplane at times when weedcrop discrimination is good may be a more affordable method of roughly mapping weed patches to allow for some reduction in herbicide use.Conclusion: Base on this experiment results, weed density and biomass had a patchy distribution and so they can, depending on their impact degree, affect the growth and yield patterns of wheat in a nonuniform manner. Also geostatistical and remote sensing data has great potential for study of plant distribution and their relationships on the fields.