Cacao Crop Management Zones Determination Based on Soil Properties and Crop Yield

Carvalho, Perla Silva Matos de; Franco, Laís Barreto; Silva, Samuel de Assis; Sodré, George Andrade; Queiroz, Daniel Marçal de; Lima, Julião Soares de Souza

doi:10.1590/18069657rbcs20150520

Cited by 11 publications

(5 citation statements)

References 24 publications

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“…Both attributes with the highest contribution in 4 had no agreement with the zones delineated for that principal component. Results similar as those of this work were obtained by Carvalho (2016) regarding the Kappa index variation. The author obtained Kappa indexes with a classification ranging from not significant to significant.…”

Section: Outlining Of Management Zonessupporting

confidence: 90%

Using multivariate analysis to design management zones

et al. 2020

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Soil chemical and physical attributes are important in any agricultural cropping system, but in precision agriculture they are more relevant due to the possibility of application using different management practices along a production field. However, the correlation between these attributes has been little explored in the delineation of management zones. This work aims to maximize the use of joint spatial variability for soil attributes. Its secondary objectives were 1) reduction of spatial variability dimensionality among all attributes and 2) assessment of agreement between univariate and multivariate management zones. The management zones resulting from the interpolation of attribute values, as well as from the scores of each of the three main components, were delineated using the Fuzzy c-means algorithm. The fuzzy performance and modified partition entropy indexes were used to determine the optimal number of management zones. The Kappa index was used to evaluate the agreement of management zones obtained from attributes with those obtained from principal components. By using principal component analysis, it was possible to reduce the dimensionality of the number of variables that contribute to the joint spatial variability existing in the study area. There was no complete agreement between the uni-and multivariate management zones outlined, which is why further studies on the subject are needed.

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Section: Outlining Of Management Zonessupporting

confidence: 90%

Using multivariate analysis to design management zones

et al. 2020

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“…In precision agriculture research, there are several statistical approaches to studying site‐specific responses. One consists of quantifying sources of variation in soil and crop responses, often in the form of yield maps, and using a general function or algorithm for the entire field to examine the effects of different input application rates (de Carvalho et al, 2016). Alternatively, different areas with common site characteristics can be considered when assigning treatments and identifying differential responses (Ruffo et al, 2006).…”

Section: Difm Field Trial Methodologymentioning

confidence: 99%

The Data‐Intensive Farm Management Project: Changing Agronomic Research Through On‐Farm Precision Experimentation

et al. 2019

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The Data-Intensive Farm Management (DIFM) project works with participating farmers, using precision technology to inexpensively design and run randomized agronomic field trials on whole commercial farm fields, to provide data-based, site-specific farm input management guidance, thus providing economic and environmental benefits. This article lays out a conceptual framework used by the multidisciplinary DIFM research team to facilitate collaboration and then presents details of DIFM's procedures for what it calls on-farm precision experimentation (OFPE), which includes field trial design and implementation, data generation, processing, and management, and analysis. It is argued that DIFM's data and the agricultural "Big Data" currently being collected with remote and proximal sensors are complementary; that is, more of either increases the value of the other. In 2019, DIFM and affiliates conducted over 120 trials, ranging from 10 to 100 ha in size, on maize, wheat, soybeans, cotton, and barley in eight US states, Argentina, Brazil, and South Africa. The DIFM project is developing cyberinfrastructure to "scale up" its activities, to permit researchers and crop consultants worldwide to work with farmers to conduct trials, then process and manage the data. In Addition, DIFM is in the early stages of developing a software system for semiautomatic data analytics, and a cloud-based farm management aid, the purpose of which is to facilitate conversations between agronomists and farmers about implementing data-driven input management decisions. The proposed framework allows researchers, agronomists, and farmers to carry out on-farm precision experimentation using novel digital tools.

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“…During the growth period of crop, the management measures adopted and climatic conditions are reflected as yield of the crop [12,13]. This is further realized that, this output of crop yield when, shown in the form of spatial map shows variability of crop yield in each field [14]. This yield monitoring helps the decision makers in strengthening the supply chain and storage of crop yield [15].…”

Section: Necessity Of Yield Sensingmentioning

confidence: 96%

Review on Yield Sensing Technologies for Horticultural crops

Lohare

Nair

Sharma

et al. 2023

IJPSS

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The economic production capacity of a farm could be effectively ascertained with the aid of crop map. With the help of yield maps farmer could benefit by increasing the effective placement input management and better map their profitability. While this yield sensing technology is being used for field crops but very few examples are observed for horticultural crops. So, in spite of many conventional technologies new inventions have been done for sensing yields of horticultural crops. This paper presents different review for examining yields that can be bifurcated in Proximal and Space Borne Sensors Sensing System. It gives us a better prediction about the yield of different crops prior to harvest. For yield sensing two methods i.e. Proximal sensing and Space borne sensors sensing system has been discussed in this review paper for crops like Capsicum, Potato, Carrot, Tomato, Melon, Apple, Pear etc. This review also demonstrated the need for more solutions to be explored in future for yield sensing of different crops. Proximal and space borne sensing system based yield sensing technology has been studied in different countries like Chile, Turkey, India, China, USA, UK etc. and their results has been discussed.

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Cacao Crop Management Zones Determination Based on Soil Properties and Crop Yield

Cited by 11 publications

References 24 publications

Using multivariate analysis to design management zones

Using multivariate analysis to design management zones

The Data‐Intensive Farm Management Project: Changing Agronomic Research Through On‐Farm Precision Experimentation

Review on Yield Sensing Technologies for Horticultural crops

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