Modelling fuzzy combination of remote sensing vegetation index for durum wheat crop analysis
مدل سازی ترکیب فازی از شاخص پوشش گیاهی سنجش از دور برای تجزیه و تحلیل محصول گندم دوروم-2019
The application of new technologies (e.g. Internet of Things, mechatronics, remote sensing) to the primary sector will reduce the production costs, limit the waste of primary materials, and reduce the release of polluting compounds into the environment. Precision agriculture (PA) has been growing in the last years thanks to industry efforts and development of applications for diagnostic purposes. Many applications in PA use vegetation indices to measure phenology parameters in terms of Leaf Area Index (LAI). In this context, the correlation of some vegetation indices were analyzed with respect to the durum wheat canopy, evaluating two different phenological stages (elongation and maturity). The results show that for the first stage of growth, the Enhanced Vegetation Index (EVI) was the best-correlated vegetation index with LAI, while the Land Surface Water Index (LSWI) was more reliable for the following stage of growth. Considering trials findings, a fuzzy expert system was developed to combine EVI and LSWI, obtaining a new combined index (Case-specific Fuzzy Vegetation Index) that better represents the LAI in comparison with the single indices. Thus, this approach could give place to a better representative vegetation index of a different biological condition of the plant. It may also serve as a reliable method for wheat yield forecasting and stress monitoring.
Keywords: Precision agriculture | LAI | Remote sensing | Crop management | Landsat images | Ecosystem services
Fuzzy control system for variable rate irrigation using remote sensing
سیستم کنترل فازی برای آبیاری با سرعت متغیر با استفاده از سنجش از دور-2019
Variable rate irrigation (VRI) is the capacity to spatially vary the depth of water application in a field to handle different types of soils, crops, and other conditions. Precise management zones must be devel- oped to efficiently apply variable rate technologies. However, there is no universal method to determine management zones. Using speed control maps for the central pivot is one option. Thus, this study aims to develop an intelligent fuzzy inference system based on precision irrigation knowledge, i.e., a system that can create prescriptive maps to control the rotation speed of the central pivot. Satellite images are used in this study because remote sensing offers quick measurements and easy access to information on crops for large irrigation areas. Based on the VRI-prescribed map created using the intelligent decision- making system, the pivot can increase or decrease its speed, reaching the desired depth of application in a certain irrigation zone. Therefore, considering the spatial variability in the crop has made the strategy of speed control more realistic than traditional methods for crop management. The intelligent irrigation system pointed out areas with lower leaf development, indicating that the pivot must reduce its speed, thus increasing the water layer applied to that area. The existence of well-divided zones could be ob- served; each zone provides a specific value for the speed that the pivot must develop for decreasing or increasing the application of the water layer to the crop area. Three quarters of the total crop area had spatial variations during water application. The set point built by the developed system pointed out zones with a decreased speed in the order of 50%. From the viewpoint of a traditional control, the relay from pivot percent timer should have been adjusted from 70% to 35% whenever the central pivot passed over that specific area. The proposed system obtained values of 37% and 47% to adjust the pivot percent timer. Therefore, it is possible to affirm that traditional control models used for central-pivot irrigators do not support the necessary precision to meet the demands of speed control determined by the developed VRI systems. Results indicate that data from the edaphoclimatic variables when well-fitted to the fuzzy logic can solve uncertainties and non-linearities of an irrigation system and establish a control model for high-precision irrigation.
Keywords: Fuzzy control | Variable rate irrigation | Speed control | Remote sensing | Decision support system
Evaluation of the energy budget and energy use efficiency in wheat production under various crop management practices in China
ارزیابی بودجه انرژی و کارایی مصرف انرژی در تولید گندم تحت شیوه های مختلف مدیریت محصول در چین-2018
Developing energy-saving and high-efficiency crop managements is required for food and energy se curity. Huang-Huai-Hai Plain produces more than 60% of the wheat in China. However, no published studies have investigated the energy budget of wheat production in this area. Using an input-output energy analysis from 2012 to 2014, we assessed energy flow of various managements. The average en ergy input and output ranged from 37.5 to 57.4 and 170.9e263.9 GJ ha1, respectively. Compared with rainfed wheat (W1), irrigated wheat presented significantly higher energy inputs and outputs. Compared with farmers practices (W2), optimized management based on soil water content (SWC) at depths of 0 e20 (W3) and 0e40 cm (W4) reduced energy use; however, this reduction did not occur at soil depths of 0e60 cm (W5). Among four irrigated managements, W4 produced the highest energy output with the lowest energy input. Moreover, W4 presented the highest net energy, energy use efficiency, and energy productivity, which were 8.2%, 15.6%, and 14.9% higher than those presented by W2, respectively. The lowest specific energy in W4 indicated that the energy consumed to produce 1 kg of wheat can be substantially reduced via efficient water management. SWC-based water management had a significant
Keywords: China ، Crop management ، Energy analysis ، Energy use efficiency ، Wheat
Identifying the factors that determine ecosystem services provision in Pampean agroecosystems (Argentina) using a data-mining approach
شناسایی عوامل موثر بر ارائه خدمات اکوسیستم در کشاورزی کشاورزی Pampean (آرژانتین) با استفاده از یک روش داده کاوی-2018
Ecosystem services (ES) have become a key concept in the assessment of natural resources, as a way to connect human well-being and ecosystems degradation. However, ES quantification is considered a basic problem because provision varies considerably as a result of land use change and site-specific characteristics (i.e. climate, soil, topography, and time). Thus, more detailed studies are needed to assess whether these changes affect ecological variables. We explored the use of environmental and crop management variables in predicting the provision of four ES (soil C balance, soil N balance, N2O emission control and groundwater contamination control) in three agroecosystems located in the Pampa region (Argentina). Data-mining, represented by k-means cluster and classification trees, was used to identify the dependence of ES provision on the variation of both environmental and crop management factors. We used plot level crop man agement and environmental field information stored in a large database during a 10-year period. The k-means method selected five different clusters. The final configuration showed two con trasting clusters: one with the lowest ES provision, and another one with the highest ES provision. The five clusters were represented in the terminal nodes of the final classification tree. Regarding the predictive power of the variables, crop and year were the most important predictors. Then, differences observed in ES provision resulted from changes in land use (variable “crop”) and crop season (variable “year”). These results are meant to enlighten stakeholders in terms of how to manage Pampean agroecosystems in order to positively influence ES provision.
Keywords: Ecosystem services ، Cluster ، Classification trees ،Land use ، Crop season ، Pampean agroecosystems