2017
DOI: 10.15666/aeer/1503_205219
|View full text |Cite
|
Sign up to set email alerts
|

Cocoa Farmers’ Safety Perception and Compliance With Precautions in the Use of Pesticides in Centre and Western Cameroon

Abstract: Abstract. Although pesticides are necessary inputs in cocoa production, inability of farmers to comply with safety precautions poses significant threat to their health and sustainable cocoa production in Cameroon. This paper analyzed the determinants of compliance with pesticide safety guideliness among cocoa farmers. Data were collected with structured questionnaires administered to 667 cocoa farmers and analyzed with descriptive statistics and Negative Binomial regression. The results showed that majority of… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
7
0

Year Published

2018
2018
2022
2022

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 9 publications
(7 citation statements)
references
References 22 publications
0
7
0
Order By: Relevance
“…For the correlation function diagram of precipitation and soil moisture (Fig. 2), the initial time lag interval is all k = 0, indicating that the soil moisture in different soil layers of fixed dunes increase rapidly after precipitation (Oyekale 2017). It can be judged from figure 2 that the period of the correlation function between the precipitation and the soil moisture in the 0-20 cm, 20-40 cm, 40-60 cm, 60-80 cm, 80-100 cm, and 100-120 cm layers is 1.5, 2, 2, 2, 1 and 3.5 time lag intervals (1 time lag is 10 days), indicating that the values of the peaks and valleys on the precipitation diagram are separated by 1.5, 2, 2, 2, 1 and 3.5 time lag intervals, and reflected by corresponding peaks and valleys on the soil moisture dynamic diagram at the soil depth of 20 cm, 40 cm, 60 cm, 80 cm, 100 cm and 120 cm.…”
Section: Cross-correlation Analysis Between Soil Moisture and Precipimentioning
confidence: 99%
“…For the correlation function diagram of precipitation and soil moisture (Fig. 2), the initial time lag interval is all k = 0, indicating that the soil moisture in different soil layers of fixed dunes increase rapidly after precipitation (Oyekale 2017). It can be judged from figure 2 that the period of the correlation function between the precipitation and the soil moisture in the 0-20 cm, 20-40 cm, 40-60 cm, 60-80 cm, 80-100 cm, and 100-120 cm layers is 1.5, 2, 2, 2, 1 and 3.5 time lag intervals (1 time lag is 10 days), indicating that the values of the peaks and valleys on the precipitation diagram are separated by 1.5, 2, 2, 2, 1 and 3.5 time lag intervals, and reflected by corresponding peaks and valleys on the soil moisture dynamic diagram at the soil depth of 20 cm, 40 cm, 60 cm, 80 cm, 100 cm and 120 cm.…”
Section: Cross-correlation Analysis Between Soil Moisture and Precipimentioning
confidence: 99%
“…After completing the coordinate transformation, the result graph can be obtained. In the triangle of the image, each vertex is corrected separately (Oyekale 2017). The correction coefficient needs to be determined according to the size of the triangle to achieve the geometric correction of the data.…”
Section: Geometric Precision Correction Of Datamentioning
confidence: 99%
“…The main idea of a back propagation algorithm in BP neural network is to divide the learning process into two stages: In the first stage (forward propagation process), the input information calculate the actual output value of each unit layer from the input layer, each layer of neuron state only affects the state of the next layer of neurons; In the second stage (back propagation process), assuming that the desired output value is not obtained at the output layer, the difference between the actual output and the desired output is calculated recursively layer by layer, and the error signal tends to be minimized by modifying the weight of the front layer according to the error. It gradually approximates the target by continuously calculating the network weights and deviation changes in the direc- [15][16][17][18][19][20][21].…”
Section: Fast Recognition Of Moving Video Images Based On Bp Neural Nmentioning
confidence: 99%