# Benefits of Advanced Air Mobility for Society and Environment: A Case Study of Ohio
## 4 Benefit Factors
### BF-6: Cost Savings in Agriculture
#### 4.6.1 Increased Crop Yield  
Drones equipped with sensors and digital imaging capabilities provide farmers with a more detailed view of their fields, allowing them to maximize agricultural yields and farm efficiency [70]. Drones have revolutionized the spraying applications to determine which parts of the field require more or less water, pesticide, and fertilizer. They also have advantages for assessing storm damage, crop monitoring by assessing health, density, and also locating and possibly treating a disease before it spreads. Drones are boosting precision agriculture to new heights, and combining with other technology, farmers can gain the most out of their crops, regardless of the resources [71]. For evaluating the value of increased crop yield or production and cost savings, we found soybeans as the most grown crop in Ohio. In addition, corn and wheat, grown for both animal feed and human consumption, were found in the top ten agriculture in Ohio [72]. Moreover, these are the most widely planted crops in the USA. Thus, we kept our focus on these crops for our analysis.  
Although many farmers in the United States were among the first to exhibit interest in the possibilities of sUAS, the expenses and time required to master the systems proved to be larger than expected because many farmers were still suffering from the financial impacts of the 2008 financial crisis when the technology began to take hold [31]. For this reason, we calculated a factor to compensate for the savings generated from this benefit factor. According to Teal's estimation, only farmers who earn more than $100,000 a year are likely to employ this technology [31]. According to the United States Department of Agriculture (USDA), the number of farms in Ohio is 77,805, and the number of farms that make $100,000 or more is 13,893 [73]. The Factor expressing the ratio (\(F_{\text{ag}}\)) of farmers who will take on the technology is 13,893/77,805 or 0.18.  
From Ohio Agricultural Statistics [74], we collected the historical crop data for 69 years. We used the harvested area (acre), yield (bushel), and price (dollar) data of soybean, corn, and wheat to forecast data for the next 11 years using the ARIMA model (see Section 3), and put the forecasted data in the corresponding vectors (see Table 1). Wheat yields would increase by \(x=3.3\%\) for using drones, while soybean and corn yields would rise by \(y=2.5\%\)[72]. Using Equation (37), we estimated the annual Increased Production of Soybean (\(IP_{S}\)):  
\[IP_{S_{(i)}}=(Y_{S_{(i)}}+Y_{S_{(i)}}\times y^{\prime\prime}\!\!_{\alpha}) \times A_{S_{(i)}}\qquad i\in\{2022,2023,...,2032(year)\} \tag{37}\]  
Then, we obtained the yearly estimated Value of Increased Production for Soybean (\(V_{S}\)) using Equation (38):  
\[V_{S_{(i)}}=IP_{S_{(i)}}\times p_{S_{(i)}}\times F_{ag}\qquad i\in\{2022,2023,...,2032(year)\} \tag{38}\]  
Similarly, we calculated estimated Value of Increased Production for Corn (\(V_{C_{(i)}}\)) and Value of Increased Production for Wheat \(V_{W_{(i)}}\)) for year \(i\). Using Equation (39), we computed the total estimated Value of Increased Production (\(VIP_{i}^{T}\)) in year \(i\) from soybean, corn and wheat fields:  
\[VIP_{i}^{T}=V_{S_{(i)}}+V_{C_{(i)}}+V_{W_{(i)}}\qquad i\in\{2022,2023,...,2032 (year)\} \tag{39}\]