Vegetation indices for monitoring agronomic performance of potato under combinations of mineral and organic fertilization¹

Índices de vegetação para monitoramento do desempenho agronômico da batata sob combinações de adubação mineral e orgânica

ABSTRACT

Potatoes are one of the main vegetables consumed worldwide. However, dependence on mineral fertilizers compromises producers’ profitability. New alternatives are required to increase agricultural crop yield while sustaining production. Vegetation indices have improved the agility and benefits of experimental evaluations of vegetables; however, there is a scarcity of information on potatoes. Thus, the present study aimed to assess the effectiveness of different vegetation indices for monitoring the agronomic performance of potato crops fertilized with a combination of mineral fertilizers and organic compost. The study was conducted in the municipality of Perdizes, Minas Gerais, Brazil. A randomized block design was used. Each plot was divided into five subplots that received 25 combinations of mineral and organic fertilizer treatments in four replications. Images were acquired using a remote-piloted aircraft, which generated spectral values of vegetation indices in relation to various combinations of fertilizer doses. Mineral fertilizer doses significantly correlated with the normalized green-red difference index (NGRDI), normalized difference vegetation index (NDVI), and green leaf index (GLI) at r = 0.824, 0.836, and 0.833, respectively. Even in an environment with high variability in plant growth, the NGRDI, NDVI, and GLI were significantly correlated with mineral fertilization. The agronomic performance of potato crops under various fertilizer dosages could be monitored using the obtained vegetation indices.

Key words:
Solanum tuberosum L.; sustainable production; high-performance phenotyping; organic farming

RESUMO

A batata é considerada uma das principais hortaliças consumidas no mundo. Contudo, a dependência de fertilizantes minerais tem comprometido a rentabilidade dos produtores. São necessárias alternativas para aumentar o rendimento e sustentar a produção. Os índices de vegetação melhoraram a agilidade e os benefícios das avaliações experimentais de hortaliças, sendo escassas aplicações em batata. Assim, este estudo teve como objetivo validar índices de vegetação para monitoramento do desempenho agronômico no cultivo da batata sob combinações de adubação mineral e orgânico. O presente estudo foi conduzido no município de Perdizes, Minas Gerais, Brasil, adotando delineamento em blocos casualizados. Cada parcela foi dividida em cinco subparcelas, as quais receberam 25 combinações de composto mineral e orgânico, em quatro repetições. As imagens foram obtidas por aeronave remotamente pilotada gerando valores espectrais de índices de vegetação relativos a combinações de doses de fertilizantes. Os índices Normalized Green Red Difference Index (NGRDI), Normalized Difference Vegetation Index (NDVI) e Green Leaf Index (GLI) revelaram correlações significativas com as doses de fertilizantes minerais em r = 0,824, 0,836 e 0,833, respectivamente. O NGRDI, NDVI e GLI correlacionaram-se significativamente com a fertilização mineral mesmo em ambiente com alta variabilidade de vigor. O desempenho agronômico das dosagens de fertilizantes na cultura de batata pôde ser monitorado a partir dos índices de vegetação.

Palavras-chave:
Solanum tuberosum L.; produção sustentável; fenotipagem de alto desempenho; agricultura orgânica

HIGHLIGHTS:

The agronomic performance of potato crops can be monitored through the use of high-throughput phenotyping.

Vegetation indices provide reliable monitoring of the nutritional status of potato crops.

The GLI, NGRDI, and NDVI indices differentiated the higher and lower productivity levels based on the fertilizer used.

Introduction

Potatoes (Solanum tuberosum L.) are Brazil’s most significant crops, covering an area of approximately 10,000 ha with an average annual production of 3.5 million tons (CONAB, 2020). The majority of potato crops in Brazil are cultivated using 100% mineral fertilizer (Girotto et al., 2021). Alternatives that can increase productivity while reducing costs have also been investigated (Mamiya et al., 2020).

However, excessive mineral fertilizers increase the cost of crop production (Alvarenga et al., 2021). Thus, one way to improve fertilization efficiency is to improve the root system using organic compost, which typically contains humic substances and has the potential to promote root growth (Salamba et al., 2021).

Investigating various mineral and organic formulations in the field requires extensive areas and many plots as well as subplots (Mamuye et al., 2021). However, studies involving large areas may not be feasible and require lengthy evaluations (Maciel et al., 2020). Therefore, novel alternative methods are required to evaluate the results of studies with larger sample sizes.

The behavior of crop variability has been investigated to ascertain the relationship between images obtained from remote-piloted aircraft, vegetation indices (VIs), and field nutritional information relative to soil fertilization (Kavvadias et al., 2017; Liu et al., 2018; Peng et al., 2021). Among the many indicators, leaf area is directly related to crop productivity (Brito et al., 2021). However, few studies have indirectly and nondestructively associated these parameters with the spectral responses of vegetation indices.

Therefore, the present study aimed to determine the value of various vegetation indices in order to monitor the agronomic performance of potato crops that were fertilized with a combination of mineral and organic fertilizers.

Material and Methods

The growth of the potato crop cultivar Asterix was investigated in Fazenda Água Santa, in the municipality of Perdizes, Minas Gerais, Brazil, at a geolocation of latitude 19° 22’ 1’’ S and longitude 47° 23’ 12.57” W, at an altitude of 965 m (Figure 1).

Figure 1
Location of the experiments: Fazenda Água Santa, in the municipality of Perdizes, Minas Gerais, Brazil

The experiments were performed in a commercial cultivation area. A central pivot was used for irrigation, by adjusting the water depth according to the water demand. Cultural treatments recommended for potato cultivation were followed (Filgueira, 2013).

The soil of the experimental area was classified as Oxisol (United States, 2014), which corresponds to Latossolo Vermelho-Amarelo in the Brazilian soil classification system (EMBRAPA, 2018). The soil was sampled at a depth of 0 to 20 cm for chemical and physical analysis prior to the experimentation, which yielded the following results: medium texture (22% clay); pH in H₂O = 5.9; P Mehl = 37.9 mg dm^-3; K = 1.3 mmol_c dm^-3; Ca = 42 mmol_c dm^-3; Mg = 10.16 mmol_c dm^-3; H+Al= 31.94 mmol_c dm^-3; SB = 55.57 mmol_c dm^-3; T = 8.7 mmol_c dm^-3; V = 63%; B = 0.58 mg dm^-3; Cu = 1.58 mg dm^-3; Fe = 62.64 mg dm^-3; Mn = 19.89 mg dm^-3; Zn = 10.79 mg dm^-3.

The experimental design was a randomized block, consisting of five plots subdivided into five subplots with four replications. The plots were 25 m long and received mineral fertilizer at five different dosages: 0, 25, 50, 75, or 100% of the recommended dose of 1,000 kg ha^-1 monoammonium phosphate mineral fertilizer, designated as 1, 2, 3, 4, and 5, respectively. The subplots received organic compost in five different dosages (0, 5, 10, 15, and 20 t ha^-1 organic compost designated as 1, 2, 3, 4, and 5, respectively). Thus, 25 different combinations (treatments) of organic and mineral fertilizers were evaluated. For example, the combination 1-1-I (Figure 2) indicates that block I in a plot was fertilized with 0% mineral fertilizer and 0 t ha^-1 organic compost.

Figure 2
Experimental design. Blue, red, yellow, and gray indicate blocks I to IV, respectively

The organic compost was produced by Fazenda Água Santa and had the following composition as dry weight: 25% compost barn manure (C/N = 18%); 25% tree pruning (C/N = 34.7%); 5% pomace (C/N = 120%); 10% coffee straw (C/N = 16%); 15% boiler ash (C/N = 34%); 5% mashed potatoes (C/N = 6%), and 5% potato stem (C/N = 6%). Before use, the compost was sampled and dried, and its analysis revealed the following results: pH in H₂O = 7.7, C/N = 50/1, N = 0.71%, P₂O₅ = 0.2%, K₂O = 1.15%, Ca = 4.02%, Mg = 0.27%, S = 0.39%, Cu = 33 mg kg^-1, Zn = 67 mg kg^-1, and Mn = 165 mg kg^-1.

Among the field variables, plant nutritional status, tuber classification, and productivity were evaluated. All plants were supplied urea in four fertigations of 60, 100, 60, and 60 kg ha^-1 at 22, 26, 41, and 62 days after planting (DAP), respectively. Weed control was performed manually, when necessary. Additionally, when the incidence of insect pests and diseases was confirmed, chemical control was implemented using products registered for potato cultivation, according to the management strategies outlined by Lopes et al. (2016) and Nava & Diez-Rodríguez (2016). Nutritional status was assessed based on nutrient uptake rate, tuber yield, and productivity, as recommended by Silva & Fontes (2016).

Images were acquired from a remote-piloted aircraft integrated with a 20- megapixel RGB camera and a multispectral camera 110 days after planting (DAP). During this period, the plant tubers were fully mature and ready for harvest, having received all of the photoassimilates. The flight was autonomously controlled by DroneDeploy software (DroneDeploy, San Francisco, CA, USA) at a height of 60 m, with longitudinal and lateral overlaps of 80 and 75%, respectively, and a ground sampling distance (GSD) of 4.5 cm pixel^-1 (Figure 3).

Figure 3
Flowchart of study - methodology, image processing, and data analysis

An orthomosaic from the aerial images was generated using Pix4d Mapper software (Pix4D S.A., Prilly, Switzerland), and the vegetation indices used in this study are presented in Table 1.

Table 1
Vegetation indices: Normalized difference vegetation index - NDVI, Green leaf index - GLI, and Normalized green red difference index - NGRDI used in the study for monitoring the agronomic performance of potato crops fertilized with various combinations of mineral fertilizer and organic compost

Index	Equation	Reference
NDVI	(NIR - R)/(NIR + R)	Rouse et al. (1973)
GLI	(2G - R - B)/(2G + R + B)	Gitelson et al. (2002)
NGRDI	(G - R)/(G + R)	Tucker (1979)

• B: blue; G: green; NIR: near-infrared; R: red. Camera radiometric resolution: RGB, 16 bits; multispectral, 12 bits.

Radiometric indices for each plot were calculated and extracted using ENVI Classic software with the ROI Segmentation tool integrated into the vector data of the plots in Shapefile format and unified with field information in Microsoft. xls spreadsheet (Microsoft Corp., Redmond, WA, USA) using the MiniTab 18 software (Minitab LLC., State College, PA, USA). Spatialization, association, and geographic data classification were analyzed using ENVI Classic (L3Harris Geospatial, Boulder, CO, USA) and ArcMap 10.5 (ESRI, Redlands, CA, USA).

The dissimilarity between fertilizer dosages was assessed by multivariate analyses using Genes v.2015.5.0 (Cruz et al., 2013). Dissimilarity between dosages was represented by a dendrogram created using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) and a heat map based on a dissimilarity matrix produced using the Mahalanobis distance matrix (D2ii), and validated using cophenetic correlation coefficients.

Pearson’s correlation coefficients were used to determine whether or not there was a significant relationship between the vegetation indices and fertilizer dosages in each plot. The behavior of the vegetation indices in relation to fertilizer concentrations was estimated by statistical regression analysis using MiniTab 18 software (Minitab LLC., State College, PA, USA).

Results and Discussion

The cophenetic correlation coefficient of the clusters formed from the UPGMA dendrogram was 0.790 (p ≤ 0.05, t-test; Figure 4). The dendrogram shows the matrix data and subsequent clusters with significant results. The groups were separated by delimitation on a cutoff of 25.5%, which was fixed in the region of the incidence of abrupt changes in ramifications (Cruz et al., 2013) in the dendrogram (Figures 4 A and B).

Figure 4
Dendrogram of variability among 25 combinations of fertilizer doses and vegetation indices of potatoes (A) and heat map of various vegetation indices among 25 combinations of fertilizer doses and vegetation indices of potatoes (B)

Prior detection of dissimilarity in dendrograms is critical to validating the use of images for agronomic queries (Maciel et al., 2019; 2020; Clemente et al., 2021). This is because multivariate statistics provided by the dendrogram can detect variability in the terrain, indicating its behavior, justifying the use of remote sensing to detect and spectrally record a response to the variability (Maciel et al., 2019; 2020). Thus, the cut-off resulted in the separation of groups I-VII, which comprised 28, 4, 8, 8, 12, 24, and 16% of the combinations, respectively (Figure 4). The formation of the seven groups indicated distinct responses of this magnitude, thus validating the different vegetation indices.

The analysis of variance revealed that all response variables significantly contributed to the variation in data dissimilarity. The agronomic parameters of highly variable plants have been linked to vegetation indices used to characterize vegetation cover and crop phenology (Maciel et al., 2019; Plaza et al., 2021). Among the many agronomic indicators, leaf area is directly related to crop productivity (Brito et al., 2021). However, few studies have associated these parameters indirectly and nondestructively with the spectral responses of vegetation indices in potatoes. The present study associated productivity data measured in situ with calculated vegetation indices, as shown in Table 2.

Table 2
Pearson correlation coefficients between productivity measured in the field and each of GLI, NGRDI and NDVI

Vegetation indices	Pearson (Productivity)	p-value ≤ 0.05
GLI	0.741	0.00010
NGRDI	0.727	0.00012
NDVI	0.745	0.00011

• GLI - green leaf index; NDVI - normalized difference vegetation index; NGRDI - normalized green red difference index

Associating agronomic parameters measured in the field with digital images permitted the verification of significant correlations between the vegetation indices and productivity data measured in the field. The low p value in all correlations at a significance level of p ≤ 0.05 (Table 2) supported these results. Pearson’s correlation coefficients were used to confirm the existence or absence of a relationship between the spectral values of the vegetation indices and the dosages of mineral and organic fertilizers (Figure 5).

Figure 5
Pearson correlation coefficients and p-values between vegetation indices (GLI, NGRDI and NDVI) and combined treatments of mineral and organic fertilizers

Positive and significant (p ≤ 0.05) Pearson correlation coefficients (r > 0.8) were observed between each of the vegetation indices GLI, NGRDI, and NDVI and the plots fertilized with mineral fertilizers. This is justified since mineral fertilizers provide more essential nutrients for plant development than organic composts (Pereira et al., 2020). In contrast, fertilization with organic compost resulted in negative Pearson correlation coefficients with values close to zero (Figure 5). Statistical regression models were generated to understand the behavior of the indices in relation to fertilizer dosages (Figure 6).

Figure 6
Regression models between mineral fertilization and vegetation indices (GLI, NGRDI, and NDVI)

The linear behaviors of the GLI, NGRDI, and NDVI are shown in Figure 6. The coefficient of determination for mineral fertilization was > 67%, and the data’s behavior was optimally explained by NDVI (R² = 69.9%). The increasing linear behavior indicates that the vegetation indices average increased as the proportion (%) of mineral fertilization increased. The vegetative vigor in plots with doses in the range of 75-80% (red circumference) was equivalent to that at 100%. Thus, the data indicated that although the mineral fertilizer’s influence is superior, the combination of organic compost with mineral fertilization yields better responses in terms of vegetative growth and productivity. Thus, combining 75 or 80% mineral fertilizer with 25 or 20% organic compost, would reduce the cost of mineral fertilizer by 20-25% and significantly benefit agriculturists. Combined treatment of organic fertilizers and mineral fertilizers improves crop quality and reduces production costs (Wichrowska & Szczepanek, 2020).

The vegetation index results confirmed the variability of the values defined in the five classes. This classification can be visualized as a color scheme that indicates the variation in vegetation intensity and growth in the studied region (Figure 7).

Figure 7
Vegetation indices in the studied region and mineral fertilization rates in experimental plots

In Figure 7, red indicates the absence of vegetation and poor vegetative growth, while darker shades of green indicate greater vegetative growth. Visual analysis verified that the GLI, NGRDI, and NDVI clearly expressed variability in plots of the same magnitude. Poor vegetative vigor was found in regions with low mineral content. Thus, even with considerable variations in plant growth across treatments, the vegetation indices were efficient and demonstrated a relevant correlation (Figure 6). The relationships between vegetation indices and productivity measured in the field are shown in Figure 8. The GLI, NGRDI, and NDVI distinguished between the highest and lowest productivity values.

Figure 8
The relationship between vegetative growth and field productivity (t ha^-1) expressed as the spatialization of vegetation indices in the study area

The vegetation indices revealed the best performance in plots with 75 and 100% mineral fertilization. This reaffirms and validates the hypothesis that organic fertilization reduces the cost of mineral fertilizer. Therefore, combined treatments comprising 20-25% organic compost are equally efficient and less expensive than 100% mineral fertilization. In contrast, the plots fertilized exclusively with organic compost yielded poor crop productivity. Therefore, the vegetation indices demonstrate associations with soil fertilization parameters and can serve as tools for agronomic research and decision-making by producers (Caturegli et al., 2019). It is evident from the findings that more than one vegetation index should be used to reduce uncertainties in decision-making (Maresma et al., 2016). The present study shows that the cost of producing potatoes could be reduced by 25% by applying 75% mineral fertilizer with compost.

Remote sensing has gained popularity as the interest in using image data to extract agricultural variables and monitor fields has increased, as was the objective of this study. Researchers have used remote-piloted aircrafts to identify superior genotypes of Lactuca sativa and Cucurbita pepo plants by comparing consistent vegetation indices to field results (Maciel et al., 2019, 2020; Beloti et al., 2020; Clemente et al., 2021).

Remote sensing provides many benefits to investigators, breeders, and agricultural crop producers. From a financial perspective, we used an inexpensive remote pilot aircraft camera to obtain detailed information. Compared to field studies that require an average of four analysts, only one investigator or operator is required for acquiring and processing remote sensing data, thus confirming its applicability, efficiency, and cost effectiveness. Remote-controlled aircraft minimize operating costs and offer a rapid and accurate means of evaluating agricultural development cycles.

Conclusions

1. The vegetation indices, NGRDI, NDVI, and GLI, revealed significant correlations with mineral fertilization even in an environment with high variability for plant growth.

2. Vegetation indices efficiently distinguished the effects of fertilizer combinations and revealed the appropriate dosages of mineral and organic fertilizers for optimal agronomic performance.

3. The NGRDI, NDVI, and GLI indices facilitated the monitoring of the agronomic performance of potato crops fertilized with different combinations of mineral and organic compounds.

Acknowledgements

The authors thank the Univesidade Federal de Uberlândia (UFU), the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), and the Fundação Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).

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• ¹ Research developed at Universidade Federal de Uberlândia, Uberlândia, MG, Brazil

Financing statement

• This research was funded by Fazenda Água Santa, Grupo Rocheto.

Edited by

Editors: Lauriane Almeida dos Anjos Soares & Hans Raj Gheyi

Publication Dates

• Publication in this collection
  26 Aug 2024
• Date of issue
  Dec 2024

History

• Received
  31 Aug 2023
• Accepted
  16 June 2024
• Published
  15 July 2024