Advertising is a paid promotion that uses strategic planning to target a certain audience. The seven functions of marketing is promotion, selling, product management, marketing information management, pricing, financing, and distribution. Usually you see common advertising such as digital ads, billboards, newspapers, flyers, and more. Advertising can spread awareness, popularize a brand, increase customer demand, and company profits. Advertising is necessary for most- if not all- companies including media outlets, the television industry, search engine companies, and social media websites.
Formulas is the BEST answer to your question.
Answer:
#include <iostream>
using namespace std;
int main() {
int a[4][5];//declaring a matrix of 4 rows and 5 columns.
for(int i=0;i<4;i++)
{
for(int j=0;j<5;j++)
{
if(i==3)//initializing last row as 0.
{
a[i][j]=0;
}
else//initializing last row as 1.
{
a[i][j]=1;
}
}
}
for(int i=0;i<4;i++)
{
for(int j=0;j<5;j++)
cout<<a[i][j]<<" ";//printing the matrix.
cout<<endl;
}
return 0;
}
Output:-
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
0 0 0 0 0
Explanation:
I have created a matrix of size 4 rows and 5 columns.I have used for loops to fill the array.To fill the last row with 0 i have used if statement.else we are filling it with 1.
Answer:
accounting system
Explanation:
The most common response variable modeled for cropping systems is yield, whether of grain, tuber, or forage biomass yield. This yield is harvested at a single point in time for determinate annual crops, while indeterminate crops and grasslands may be harvested multiple times. Although statistical models may be useful for predicting these biological yields in response to some combination of weather conditions, nutrient levels, irrigation amounts, etc. (e.g., Schlenker and Lobell, 2010, Lobell et al., 2011), they do not predict responses to nonlinearities and threshold effects outside the range of conditions in data used to develop them.
In contrast, dynamic cropping and grassland system models may simulate these biological yields and other responses important to analysts, such as crop water use, nitrogen uptake, nitrate leaching, soil erosion, soil carbon, greenhouse gas emissions, and residual soil nutrients. Dynamic models can also be used to estimate responses in places and for time periods and conditions for which there are no prior experiments. They can be used to simulate experiments and estimate responses that allow users to evaluate economic and environmental tradeoffs among alternative systems. Simulation experiments can predict responses to various climate and soil conditions, genetics, and management factors that are represented in the model. “Hybrid” agricultural system models that combine dynamic crop simulations with appropriate economic models can simulate policy-relevant “treatment effects” in an experimental design of climate impact and adaptation (Antle and Stockle, 2015).
