1. Introduction

To calculate and integrate NDVI: collecting NDVI data at different stages of sugarcane growth and integrating these values over time to obtain a cumulative measure of vegetation activity.

To evaluate the efficacy of amino acid treatment: comparing VAI values between areas treated with L-alpha amino acids and control (untreated) areas to determine whether there is a significant positive impact on green biomass and plant health.

To validate results with field measurements: performing additional field measurements to validate NDVI data and ensuring accuracy of analyses by correlating them with traditional agronomic parameters.

To analyze vegetation dynamics: observing and analyzing temporal variations in VAI to better understand how amino acid treatment affects the dynamics of sugarcane growth and development throughout the crop cycle.

To develop a robust methodology: proposing a robust methodology to use NDVI and its temporal integration as an efficient and non-invasive tool to assess vegetative state and manage agricultural crops.

2. Material and Methods

2.1. Evaluation of Product Use

The studied product is a biostimulant developed to improve vegetative growth and recovery of plants subjected to abiotic and biotic stresses. Its formulation includes a combination of macro and micronutrients complexed with L-alpha amino acids and seaweed extracts. This composition aims to accelerate plant metabolism, promoting rapid and balanced nutrition at the cellular level, which is essential for a healthy plant growth. presents the composition of the biostimulant.

Table 1. L-alpha amino acid composition.

Table 1. L-alpha amino acid composition.

Nutrient	%	g/L	Source
Water soluble nitrogen	1.00	11.0	Urea
Water soluble phosphorus	10.40		Food phosphoric acid
Water soluble potassium	2.00		Potassium sulphate
Total organic carbon	6.00		Glucose syrup
Amino acids	0.63		Plant sources

The dosage used was 2.0 l.ha^-1 and the mode of application was by aerial spraying; its marketing price is 65.00 R$.l^-1.

For the product evaluation, 9 portions were selected according to and detailed in . All portions are on the 4th cut, were harvested in June 2023, and are grown with the RB855453 variety.

Figure 1. Digital relief model of the area where the experiment was conducted.

Figure 1. Digital relief model of the area where the experiment was conducted.

Table 2. Identification of the plots that make up the experiment.

Table 2. Identification of the plots that make up the experiment.

Plot	Area
Treated
01	34.06
02	15.20
03	17.72
04	26.41
Total plots treated	93.39
Control
05th	6.00
05B	3.05
05C	4.74
07	28.87
08	33.96
Total plots control	76.62

2.5. Data Preparation

NDVI data were prepared in a tabular format, in which each row represents a specific date and the corresponding column presents the NDVI value for that date - .

Table 3. Organization of NDVI data.

Table 3. Organization of NDVI data.

Date	NDVI
t1	NDVI1
t2	NDVI2
t3	NDVIn

2.12. Assessment of Economic Viability of the Product

The integration between biological and economic assessment not only overcomes the limitations of traditional methodologies, but also contributes to more sustainable and productive agriculture by providing valuable insights for researchers, producers, and decision makers (Pretty et al., 2018; Calicioglu et al., 2019).

By combining the VAI approach with detailed economic assessment, it is possible to demonstrate in a clear and quantifiable way the economic benefits of different agricultural practices, such as the use of the amino acid L-alpha in sugarcane. Therefore, it is necessary to build an agro-industrial model that determines the economic result of the use of L-alpha, considering parameters such as application costs, processing costs, sugarcane productivity, efficiency of sugar and ethanol production processes, and the calculation of economic variables such as gross revenue (GR), net revenue (NR), and return on investment (ROI).

From the productivity (SCTH - sugarcane tons per hectare) and the amount of TRS (total recoverable sugars) per ton of sugarcane, the amount of sugar and ethanol produced per hectare is calculated. The revenue generated from the sale of sugar, ethanol, and energy (GR) is calculated based on market prices and production per hectare. The total cost of operations and treatment with the biostimulant is subtracted from the revenue to obtain the net revenue (NR).

ROI is calculated by comparing the NR of the biostimulant treated areas and of the control areas. A positive ROI indicates that the use of the biostimulant provides a significant economic gain, justifying its use.

For the determination of the economic result of the product, an agroindustrial modeling was constructed, considering the parameters exposed in .

Table 4. Parameters for agroindustrial modeling.

Table 4. Parameters for agroindustrial modeling.

Variables	Definition	Unit	Premise / Methodology
Dist	Distance between industrial unit and agricultural background	km	28.00
CI	Cost of processing one ton of sugarcane in the industrial unit	R$.t-1	28.25
CAT	Cost of product application in the field	R$.ha-1	35.00
CIT	Cost of product per hectare	R$.ha-1	130.00
CTrat	Cost of treatment	R$.ha-1	41.50
SCTH	Sugarcane productivity	t.ha-1
CHar	Cost of harvesting operation	R$.t-1	Dias Neto et al. (2023)
CTrac	Cost of traction operation	R$.t-1	Dias Neto et al. (2023)
CTransp	Cost of transport operation	R$.t-1	Dias Neto et al. (2023)
CTT	Cost of harvest, traction, and transport operations in area unit	R$.ha-1	Dias Neto et al. (2023)
CProc	Cost of sugarcane processing in area unit	R$.ha-1	(6)
CTot	Total cost for the manufacture of ethanol, sugar, and energy in area unit	R$.ha-1	(7)
F	Insoluble matter contained in sugarcane	%	CONSECANA
Pza	Percentage of sucrose contained in the brix of the broth	%	CONSECANA
PCC	Total apparent sucrose contained in the broth per cent cane	%	CONSECANA
RS	Total reducing sugars contained in the broth per cent cane	%	CONSECANA
SRS	Sugarcane sugars in the form of reducing sugars	%	CONSECANA
TRS	Total recoverable sugar	kg.t-1	CONSECANA
STH	Amount of TRS per hectare	t.ha-1	(8)
E_LBTI	Efficiency of common phase processes for the production of sugar and ethanol	%	92.07%
EFer	Efficiency of fermentation	%	90.60%
EDest	Efficiency of destilation	%	99.50%
PSugar	Purity of sugar	%	99.74
PHoney	Purity of final honey	%	40.00
RSJM	Portion of sucrose that can be crystallized as a function of the purity of final honey and the purity of the material in process	%	(9)
Mixsugar	Amount of sugars contained in sugarcane for the manufacture of sugar	%	51.1%
Mixhydrated	Amount of sugars contained in sugarcane for the manufacture of hydrated ethanol	%	57.3%
GEnergy	Amount of energy exported	kWh.t-1	67.00
VSugar	Price in bags of marketed sugar	R$.bag-1	137.53
VAnhydrous	Price per liter of anhydrous ethanol marketed	R$.l-1	2.63
VHydrated	Price per liter of hydrated ethanol marketed	R$.l-1	2.34
VEnergy	Price in megawatts of energy marketed	R$.MWh-1	140.00
Sugar	Sugar production in bags per hectare	bag.ha-1	(10)
Ethanol	Ethanol production in liters per hectare	l.ha-1	(11)
AEC	Anhydrous ethanol production in liters per hectare	l.ha-1	(12)
HEC	Hydrated ethanol production in liters per hectare	l.ha-1	(13)
Energy	Amount of energy exported in one hectare	MWh.ha-1	(14)
GR	Gross revenue	R$.ha-1	(15)
NR	Net revenue	R$.ha-1	(16)
ROI	Return on investment	%	(17)

The modeling takes into account the equations established by Dias Neto et al. (2023) for the cost of harvesting, traction, and transport operations. Moreover, it incorporates the technological parameters of the quality of the raw material in sugar and ethanol, as defined by Consecana1.

Equations that make up :

$$\mathit{C}\mathit{P}\mathit{r}\mathit{o}\mathit{c}=\mathit{C}\mathit{I}\ast \mathit{S}\mathit{C}\mathit{T}\mathit{H}$$

(6)

$$\mathit{C}\mathit{T}\mathit{o}\mathit{t}=\left(\mathit{C}\mathit{H}\mathit{a}\mathit{r}+\mathit{T}\mathit{T}\mathit{r}\mathit{a}\mathit{c}+\mathit{T}\mathit{r}\mathit{a}\mathit{n}\mathit{s}\mathit{p}+\mathit{C}\mathit{T}\mathit{r}\mathit{a}\mathit{t}\right)\ast \mathit{S}\mathit{C}\mathit{T}\mathit{H}$$

(7)

$$\mathit{S}\mathit{T}\mathit{H}={\displaystyle \frac{\mathit{T}\mathit{R}\mathit{S}\ast \mathit{S}\mathit{C}\mathit{T}\mathit{H}}{\mathrm{1,000}}}$$

(8)

$${\mathit{R}}_{\mathit{S}\mathit{J}\mathit{M}}=\left(\left({\displaystyle \frac{{\mathit{P}}_{\mathit{S}\mathit{u}\mathit{g}\mathit{a}\mathit{r}}}{{\mathit{P}}_{\mathit{S}\mathit{u}\mathit{g}\mathit{a}\mathit{r}}-{\mathit{P}}_{\mathit{h}\mathit{o}\mathit{n}\mathit{e}\mathit{y}}}}\right)\ast \left(1-{\displaystyle \frac{{\mathit{P}}_{\mathit{h}\mathit{o}\mathit{n}\mathit{e}\mathit{y}}}{\mathit{P}\mathit{z}\mathit{a}-1}}\right)\right)$$

(9)

$$\mathit{S}\mathit{u}\mathit{g}\mathit{a}\mathit{r}=10\ast \mathit{P}\mathit{C}\ast {\mathit{E}}_{\mathit{L}\mathit{B}\mathit{T}\mathit{I}}\ast \mathit{M}\mathit{i}{\mathit{x}}_{\mathit{S}\mathit{u}\mathit{g}\mathit{a}\mathit{r}}\ast {\mathit{R}}_{\mathit{S}\mathit{J}\mathit{M}}$$

(10)

$$\begin{gathered} \mathit{E}\mathit{t}\mathit{h}\mathit{a}\mathit{n}\mathit{o}\mathit{l}=\left(\left({\displaystyle \frac{10\ast \mathit{P}\mathit{C}\mathit{C}\ast {\mathit{E}}_{\mathit{L}\mathit{B}\mathit{T}\mathit{I}}\ast \left(1-\mathit{M}\mathit{i}{\mathit{x}}_{\mathit{S}\mathit{u}\mathit{g}\mathit{a}\mathit{r}}\right)}{0.95}}\right) \\ \ast \left({\displaystyle \frac{10\ast \mathit{P}\mathit{C}\mathit{C}\ast {\mathit{E}}_{\mathit{L}\mathit{B}\mathit{T}\mathit{I}}\ast \mathit{M}\mathit{i}{\mathit{x}}_{\mathit{S}\mathit{u}\mathit{g}\mathit{a}\mathit{r}}\ast \left(1-{\mathit{R}}_{\mathit{S}\mathit{J}\mathit{M}}\right)}{0.95}}\right)+10\ast \mathit{A}\mathit{R}\ast {\mathit{E}}_{\mathit{L}\mathit{B}\mathit{T}\mathit{I}}\right) \\ \ast 0.6475\ast {\mathit{E}}_{\mathit{F}\mathit{e}\mathit{r}}\ast {\mathit{E}}_{\mathit{D}\mathit{e}\mathit{s}\mathit{t}}\ast \mathit{T}\mathit{C}\mathit{H} \end{gathered}$$

(11)

$$\mathit{A}\mathit{E}\mathit{C}={\displaystyle \frac{\mathit{E}\mathit{t}\mathit{h}\mathit{a}\mathit{n}\mathit{o}\mathit{l}-\left(1-\mathit{M}\mathit{i}{\mathit{x}}_{\mathit{h}\mathit{y}\mathit{d}\mathit{r}\mathit{a}\mathit{t}\mathit{e}\mathit{d}}\right)}{0.9957}}$$

(12)

$$\mathit{H}\mathit{E}\mathit{C}={\displaystyle \frac{\mathit{E}\mathit{t}\mathit{h}\mathit{a}\mathit{n}\mathit{o}\mathit{l}-\mathit{M}\mathit{i}{\mathit{x}}_{\mathit{h}\mathit{y}\mathit{d}\mathit{r}\mathit{a}\mathit{t}\mathit{e}\mathit{d}}}{0.9541}}$$

(13)

$$\mathit{H}\mathit{E}\mathit{C}={\displaystyle \frac{{\mathit{G}}_{\mathit{E}\mathit{n}\mathit{e}\mathit{r}\mathit{g}\mathit{y}}\ast \mathit{T}\mathit{C}\mathit{H}}{1.000}}$$

(14)

$$\mathit{G}\mathit{R}=\left({\mathit{V}}_{\mathit{S}\mathit{u}\mathit{g}\mathit{a}\mathit{r}}\ast \mathit{S}\mathit{u}\mathit{g}\mathit{a}\mathit{r}+{\mathit{V}}_{\mathit{A}\mathit{n}\mathit{h}\mathit{y}\mathit{d}\mathit{r}\mathit{o}\mathit{u}\mathit{s}}\ast \mathit{A}\mathit{E}\mathit{C}+{\mathit{V}}_{\mathit{H}\mathit{y}\mathit{d}\mathit{r}\mathit{a}\mathit{t}\mathit{e}\mathit{d}}\ast \mathit{H}\mathit{E}\mathit{C}+{\mathit{V}}_{\mathit{E}\mathit{n}\mathit{e}\mathit{r}\mathit{g}\mathit{y}}\ast \mathit{E}\mathit{n}\mathit{e}\mathit{r}\mathit{g}\mathit{y}\right)$$

(15)

$$\mathit{N}\mathit{R}=\mathit{G}\mathit{R}-\mathit{C}\mathit{T}\mathit{o}\mathit{t}$$

(16)

$$\mathit{R}\mathit{O}\mathit{I}={\displaystyle \frac{{\mathit{N}\mathit{R}}_{\mathit{T}\mathit{r}\mathit{e}\mathit{a}\mathit{t}\mathit{m}\mathit{e}\mathit{n}\mathit{t}}-{\mathit{N}\mathit{R}}_{\mathit{C}\mathit{o}\mathit{n}\mathit{t}\mathit{r}\mathit{o}\mathit{l}}}{\mathit{C}\mathit{T}\mathit{r}\mathit{a}\mathit{t}}}$$

(17)

shows that:

 

0.95: stoichiometric factor for converting sucrose into RST.

 

0.6475: stoichiometric yield for RST production in ethanol.

 

0.9957: alcohol degree m/m to convert ethanol to AEC.

 

0.9541: alcohol degree m/m to convert ethanol to HEC.

 

the NR of the Control group (NRControl) does not consider CTrat, but it is considered in the NR of the Treated group (NRTreated).

3. Results and Discussion

Figure 2. NDVI evolution from 2023/07/31 to 2023/12/02.

Figure 2. NDVI evolution from 2023/07/31 to 2023/12/02.

Figure 3. NDVI evolution from 2023/12/18 to 2024/02/27.

Figure 3. NDVI evolution from 2023/12/18 to 2024/02/27.

Figure 4. NDVI evolution from 2024/03/01 to 2024/05/05.

Figure 4. NDVI evolution from 2024/03/01 to 2024/05/05.

Figure 5. NDVI evolution from 2024/05/12 to 2024/06/16.

Figure 5. NDVI evolution from 2024/05/12 to 2024/06/16.

Table 5. Mean NDVI per plot.

Figure 6. NDVI dispersion and second-order polynomial regression.

Figure 6. NDVI dispersion and second-order polynomial regression.

Table 6. Pearson’s coefficient of determination (R2) and coefficients a, b, and c for second-order polynomial regression curves.

Table 7. Vegetation Activity Index (VAI).

Table 8. Shapiro-Wilk Test for VAI for total and post-treatment period.

Figure 7. VAI QQ plot for total period for Treated group (A) and Control group (B).

Figure 7. VAI QQ plot for total period for Treated group (A) and Control group (B).

Figure 8. VAI QQ plot for post-treatment period for Treated group (A) and Control group (B).

Figure 8. VAI QQ plot for post-treatment period for Treated group (A) and Control group (B).

Table 9. VAI t-test: two samples assuming equivalent variances for total period.

Table 10. Post-harvest results from the area under study.

Table 11. Shapiro-Wilk Test for post-harvest STH.

Figure 9. STH QQ plot for post-treatment period for Treated group (A) and Control group (B).

Figure 9. STH QQ plot for post-treatment period for Treated group (A) and Control group (B).

Table 12. STH t-test: two samples assuming equivalent variances for post-harvest period.

Table 13. Pearson’s correlation between VAI and STH for the Treated and Control groups.

Table 14. Economic feasibility analysis.

4. Conclusions

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