Increasing Sugarcane Production Eco-Efficiency: A DEA Analysis with Different Sugarcane Varieties
Engineering Department, Pontifical Catholic University of Goiás, Av. Universitária 1440, Goiânia 74605-010, GO, Brazil
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Author to whom correspondence should be addressed.
Sustainability 2023, 15(14), 11201; https://doi.org/10.3390/su151411201
Submission received: 1 November 2022
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Revised: 25 November 2022
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Accepted: 9 December 2022
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Published: 18 July 2023
Abstract
:The development of new sugarcane varieties affects crop production positively. However, only some studies have investigated how the use of different sugarcane varieties reacts to the impact of climate change, and how to improve sugarcane production efficiency considering the use of different sugarcane varieties in the field. This research hypothesizes that it is possible to mitigate climate change’s impact on sugarcane production and improve eco-efficiency if the proper sugarcane varieties are chosen. The main objective is to analyze the influence of different sugarcane varieties on production eco-efficiency. An econometric study unveiled the main elements affecting sugarcane production in this research. Afterward, a data envelopment analysis determined the sugarcane varieties with more efficient production. The results indicated that climate impact on production was irrelevant when controlling for different sugarcane varieties. Furthermore, it was found that through correct variety choosing, it is possible to improve harvest efficiency. The outcome of this research contributes to achieving the United Nations SDGs 1, 2, 7, 9, and 15.
1. Introduction
Many authors have analyzed climate change’s impact on sugarcane productivity recently. Ahmad et al. [1], Abdoulaye et al. [2], Singh et al. [3], Santos et al. [4], and Akbar and Gheewala [5], for example, agree that climate change has caused a significant impact on agriculture, including sugarcane production. One of the strategies to mitigate climate change’s impact on sugarcane is the development of new varieties. Santos et al. [4], Mourice [6], Silva et al. [7], Sonkar et al. [8], Dias and Sentelhas [9], and Guga et al. [10] suggested that new sugarcane varieties benefit the crop in resisting adverse climatic situation, including high or low temperatures, and drought issues.
Despite its possible positive effects, developing new sugarcane varieties demands further crop variety research [11,12]. Nevertheless, as far as we are aware, only a few researchers, such as Santos et al. [4], Dias and Sentelhas [9], Dias et al. [11], Linnenluecke et al. [12], and Silva [13], performed analyses concerning sugarcane variety modifications. The need for more studies regarding crops with different sugarcane varieties indicates a notable research gap once the varieties’ specificities may impact the final production efficiency.
Efficiency is an essential aspect related to sugarcane production. One way to analyze sugarcane farms’ efficiency is through Data Envelopment Analysis (DEA). DEA is a non-parametric linear programming-based technique developed by Charnes, Cooper, and Rhodes [14] that assesses the relative performance of a set of units. DEA has been used in agriculture to analyze crop production systems’ sustainability [15,16]. Ullah, Singh, and Singh [16], Chaitip, Chaiboonsri, and Inluang [17], Pereira and Silveira [18], Ye et al. [19], and Asghar et al. [20] have used DEA to investigate sugarcane production efficiency. However, they did not consider the influence of using different sugarcane varieties in the field.
Sugarcane production and research are critical for achieving the United Nations Sustainable Development Goals (SDGs) 2 and 15. SDG 2 proposes ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture. SDG 15 proposes protecting, restoring, and promoting sustainable use of terrestrial ecosystems, sustainability management, combating forest desertification, halting and reversing land degradation, and stopping biodiversity loss [21]. Nevertheless, Aguilar-Rivera [22] states that improving sugarcane production also collaborates to reach SDGs 1 (ending poverty in all its forms everywhere), 3 (ensuring healthy lives and promoting well-being for all at all ages), 7 (ensuring access to affordable, reliable, sustainable, and modern energy for all), and 9 (building resilient infrastructure, promoting inclusive and sustainable industrialization, and fostering innovation). Finally, Lipper et al. [23] also argue that improving agriculture productivity and sustainability is crucial to achieving SDG 1.
This research’s primary objective is to analyze the influence of different sugarcane varieties on production efficiency. Additionally, the research investigates how climate change impacts sugarcane production and examines the most critical variables affecting sugarcane production efficiency.
2. Literature Review
2.1. Climate Change and Sugarcane Production
A critical approach for increasing sugarcane productivity is investigating each input’s influence on production, enabling more reliable prediction models.
Singh et al. [3], Swami, Dave, and Parthasarathy [24] argue that climate change has become a significant threat to sustainable agriculture. Santos et al. [4] suggest climate change promoted greater crop demand, including higher water scarcity resistance and soil conditions difficulties. Khan, Shah, and Iftikhar-Ul-Husnain [25] assert that climate change has led to global warming, unpredicted storms, drought, flood, and crop losses, affecting agricultural productivity and causing food insecurity. Akbar and Gheewala [5] and Mulinde et al. [26] discuss the possible menaces offered by climate change to agriculture and food production. Bakhsh et al. [27] warn that climate change impacts rainfall and raises temperatures, influencing food production, which may lead to conflicts.
Silva et al. [28], Kelkar, Kulkarni, and Rao [29] analyzed the impact of climate change on sugarcane production in two dry places—Paraíba, Brazil, and Maharashtra, India, respectively—and came to a similar conclusion. The authors assert that the amount of rain is related to higher productivity, while the increase in temperature negatively influenced sugarcane production.
Singh et al. [3], Swami, Dave, and Parthasarathy [24] investigated sugarcane production in Maharashtra (India) fields. Singh et al. [3] investigated how temperature variation impacted the Indian sugarcane production in that region over the past 68 years, while Swami, Dave, and Parthasarathy [24] investigated distinct and combined impacts concerning threshold temperature on sugarcane production. The two research groups agree that rising temperatures harm sugarcane yield. Finally, Jyoti and Singh [30] investigated the climatic change impact on sugarcane production in the whole Indian country and reported a negative impact of temperature rise on sugarcane production.
Linnenluecke et al. [12] investigated how climate change impacted sugarcane output in different regions of Australia. The authors considered the impact of sugarcane varieties on production using econometric models. They stated that increased atmospheric carbon concentration and maximum temperatures harmed sugarcane production after 1995. Additionally, concerning the Oceania region, McGree et al. [31] stated that the rise in mean and extreme temperatures is negatively related to Fiji’s sugarcane production. The authors claim that a rise in rain days in the late-growing sugarcane season increases sugarcane production.
Khan, Shah, and Iftikhar-Ul-Husnain [25] and Ali, Zubair, and Hussain [32] studied the impact of climate change on sugarcane production in Pakistan. Both authors concluded that rainfall is positively correlated to sugarcane yield. However, Khan, Shah, and Iftikhar-Ul-Husnain [25] argue that only summer rainfall leads to higher production. Concerning temperature, only Khan, Shah, and Iftikhar-Ul-Husnain [25] claim to have found a positive impact on sugarcane production. According to the authors, winter temperature rise increases agricultural productivity. Finally, Ali, Zubair, and Hussain [32] argued that central and local governments should support crop mechanization to ensure sugarcane crop yield increases. Moreover, the authors stated that farmers should be instructed on better use of fertilizers.
In Pakistan, Ahmad et al. [1] concluded that irrigation, rainfall, and soil condition positively and significantly impact sugarcane production decrease in Pakistan. Heureux et al. [33] studied how climate trends will impact Pakistan’s agricultural production in the Indus River Basin. The authors concluded that temperatures rising caused by climate change would negatively affect the primary crop production in the region, including sugarcane production. Rehman et al. [34] analyzed how Pakistan’s agricultural crop is related to CO2 emissions and discovered that sugarcane is positively associated with this gas, which is correlated to global warming.
Chandio et al. [35] studied how climate change impacted cereal production in Bangladesh from 1988 to 2014. The authors concluded that while rainfall improves cereal production in the country, CO2 and temperature have the opposite effect.
Guga et al. [10] demonstrate that due to the increase in temperatures, which are associated with climate change in the past 60 years, the total suitable area for sugarcane crops in Guangxi Province, the largest sugarcane producer region in China, has increased. Ncoyini, Savage, and Strydom [36] state that climate change and the lack of access to climate change information by small-scale farmers has decreased sugarcane production in South Africa.
Some researchers predicted how climate change would impact sugarcane production in the future. Santos et al. [4] simulated several scenarios through the DSSAT/CANEGRO model for production based in Alagoas, located in Brazil’s northeast region. The authors concluded that temperature rise will cause energy sugarcane (a sugarcane genetically modified variety) yield to increase until 2060. After that year, energy cane yield will decrease as air temperature rises above 40 °C. Da Silva et al. [37] warn that due to the lack of rainfall caused by climate change, in the future, a significant part of the sugarcane crop in the state of São Paulo will require irrigation, which may lead to conflicts over water resources. It is worth noting that São Paulo is the biggest sugarcane producer in Brazil. Therefore, if the scenario presented by Da Silva et al. [37] becomes true, Brazilian sugarcane could face a production shortage.
Costa, Sant’Anna, and Young [38] argue that drought shocks caused by climate change will lead to loss of crop area and value of agricultural output in the semi-arid Brazilian region.
Analyzing the impact of climate change on the southeast region of Punjab, Pakistan, Akbar and Gheewala [5] concluded that the evapotranspiration rate is expected to increase in the future, leading to an increase in sugarcane and cotton crop water requirements. The authors argue that increasing the amount of rain would be necessary to compensate for the rising temperatures and the higher evapotranspiration on crop fields.
2.2. Sugarcane Production in Goiás
In 2021, Brazil produced more than 657 million tons of sugarcane. Goiás produced 74 million tons of sugarcane, making the state the second-largest Brazilian sugarcane producer [39]. According to the Brazilian National Food Supply Company, CONAB [40], over 96% of the sugarcane produced in Goiás occurs by mechanized processes.
Goiás sugarcane production is mainly located in the southern region. Nevertheless, there are some sugarcane mills in the middle region of the state. Figure 1 presents how sugarcane production is distributed in Goiás.
According to the Brazilian Sugarcane and Bioenergy Industry Union, UNICA [39], in 2021, Brazil produced over 41.5 thousand tons of sugar and more than 32.5 million cubic meters of ethanol. Goiás produced 2.3 thousand tons of sugar and 5.2 thousand cubic meters of ethanol. In 2020, the sugarcane sector in Goiás employed almost 59 thousand workers, of which more than 33 thousand were farm workers [39].
2.3. Sugarcane Varieties
Sugarcane characteristics related to the adverse climatic situation, including high or low temperatures and drought issues, have already been observed by several authors [4,6,7,8,9,10,42].
Different crop varieties require different crop variety-specific research [11,43]. Nevertheless, regarding sugarcane research, only Linnenluecke et al. [12] have researched this subject from a panel data model. Santos et al. [4], Dias and Sentelhas [9], Dias et al. [11], and Silva [13] researched a specific sugarcane variety. According to the Brazilian Agricultural Research Company/Technological Information Agency, EMBRAPA-AGEITEC [44], the sugarcane genetic improvement promoted by different laboratories aiming to better respond to soil and climate adversities and diseases was paramount for increased sugarcane productivity in the past years.
Oliveira, Barbosa, and Daros [45] state that the main target of sugarcane genetic upgrading programs is productivity improvement. The Agronomic Institute of Campinas, IAC [46], asserts that new varieties are being developed to guarantee better productivity, focusing on regional specificities. The Sugarcane Technology Center in Brazil, CTC [47], registered that even in 2020 when the climatic adversities were harsh, farmers who had planted new sugarcane varieties did not lose their harvests. The CTC [47] and EMBRAPA-AGEITEC [44] suggest that several different varieties must be used in the field to obtain better production results. Oliveira, Barbosa, and Daros [45] present the main varieties used in Brazil in the 2019/2020 harvest. This information is replicated in Table A1 in Appendix A.
According to the EMBRAPA-AGEITEC [44], the main sugarcane varieties used in Brazil come from the Brazilian Interuniversity Network for the Sugar Energy Sector Development (RIDESA), COPERSUCAR, the Sugarcane Technology Center (CTC), and the Campinas Agronomic Institute of Campinas (IAC). Each sugarcane variety possesses different qualities and necessities, making it adequate for different regions. The EMBRAPA-AGEITEC [44] presents the differences among the main sugarcane varieties used in Brazil. Table 1 summarizes the critical information.
The different sugarcane varieties also have different harvest periods. Figure 2 exhibits the harvest period for 31 different sugarcane varieties.
The information presented in Table 1 and Figure 2 indicate that it is essential to choose the suitable variety according to the region and the different characteristics of each farm. Additionally, it is also imperative to establish the significant desired outcome before choosing which sugarcane varieties will be planted.
The awareness of the importance of planting different sugarcane varieties, aiming for higher productivity, is also verified in other countries apart from Brazil. Kennedy [49] describes the development of new sugarcane varieties in the Caribbean by different institutes. Other countries also have their institutes, such as the South African Sugarcane Research Institute (SASRI), the Kenya Agriculture and Livestock Research (KALRO), and Sugar Research Australia (SRA), which also develop different sugarcane varieties. Considering the significant dissimilarity between different varieties, it becomes evident that sugarcane research must consider which variety is being studied to avoid spurious results.
2.4. Summary
Many authors have analyzed climate change’s impact on sugarcane productivity. However, few studies have considered that many farms use different sugarcane varieties.
Brazil is the largest sugarcane producer in the world. In the 2020/2021 harvest, more than 654 million tons were produced in Brazil. Goiás, a state located in the midwest region of the Brazilian country, produced 74.04 million tons of sugarcane [50]. These data put Goiás in second place in Brazilian production, only behind the state of São Paulo. Brazil has a long history of developing sugarcane varieties, investigating the subject for over 30 years. Oliveira, Barbosa, and Daros [45] state that in 2020, genetically modified sugarcane varieties were used in more than 60% of Brazilian sugarcane plantations.
Considering this research investigation, as it will be possible to observe in the next section, the sugarcane cooperative, which supplied the data for the analysis, uses several sugarcane varieties in its production. Moreover, the cooperative presented productive rates (median) of 92.71 tons/ha for the 2020/2021 harvest. In 2021, the state of Goiás presented an effective rate of 74.67 tons/ha, and Brazil, in turn, 75.97 tons/ha [51]. This productivity rate may indicate that using a suitable sugarcane variety can overcome the impact of climate changes. However, despite all research conducted in Brazil and other countries aiming to develop new sugarcane varieties, it has yet to be investigated how those varieties respond to climate change. Therefore, this work outcome is essential for those who aim to foster sugarcane production. Since the cooperative database concerns the data of 41 farmers producing individually, the conclusion presented by this research can be exported to other farms in Brazil and other countries that present sugarcane production in the same climate region as the one we investigated in this paper.
3. Research Methodology
3.1. Methodology
The initial stage of the research methodology was based on the investigation performed by Silva et al. [28]. As stated in Section 2, the authors performed a panel data analysis, investigating the climatic change impacts on sugarcane production in Paraíba, Brazil, and controlled for the region in which data had been collected. In trying to perform a similar analysis in Goiás, some modifications to the adopted model were made. In this sense, initially, this research performed a cross-section analysis due to data availability. Data used in the research were obtained from a sugarcane cooperative and comprehended the 2020/2021 harvest. Considering that the research focused on a specific southern region of Goiás, the soil interference in sugarcane production was neutralized. Therefore, adding dummies indicating sugarcane production, as Silva et al. performed [28], was unnecessary.
As stated in the previous section, different sugarcane varieties impact sugarcane production distinctively. Therefore, the amount produced by each sugarcane variety used by each farm was added to the econometric model. Following Ali, Zubair, and Hussain [32], the total cost of fertilizer and machinery was considered per farm. However, different from Ali, Zubair, and Hussain [32], this work used the total crop expenditure and total fixed cost of each farmer. Those numbers are provided by CONAB [52] and represent an average estimation of the farmers’ expenditure, separated by the size of the farm. CONAB [52] classifies the farmers into familiar agriculture and company farms, based on Brazilian law 11.326/2006 [53], which provides unique benefits to small farmers. Therefore, before collecting data from CONAB [52], the cooperative sugarcane farms were grouped according to size.
Contrary to Linnenluecke et al. [12], Rehman et al. [34], and Chandio et al. [35], it was decided not to include other climatic variables, such as CO2 and humidity, due to multicollinearity problems. Apart from multicollinearity, a heteroskedasticity test was also performed. No problems regarding this issue were found.
3.2. Database
The studied variables were classified according to the farm where data were collected. Regarding sugarcane production, each variety’s total produced separately was used. Farms in the studied region used twenty-seven varieties. Table 2 presents the sugarcane varieties used by the members of the sugarcane cooperative, how many times they appeared in the sugarcane data (occurrence), the frequency, the percentual share of the production, and the cumulative percentage.
In Table 2, despite the considerable number of sugarcane varieties, more than 90% of the total production is concentrated in eleven varieties. Thus, aiming to avoid collinearity problems, only this group of sugarcane varieties was considered in the analysis.
All climate data were separated according to the region where the farm was located. Data concerning climate, such as precipitation and average temperature, were collected at the Brazilian National Institute of Meteorology—INMET [55]. CO2 emissions were collected at the Brazilian Greenhouse Gases Emissions and Removals Estimation System, SEEG [56], and were related to 2018, the last year with available data in the consulted database. According to Linnenluecke et al. [12], it is expected that CO2 emissions present a positive impact on the total sugarcane production. Along with Silva et al. [28], the mean temperature is expected to present a negative relationship, while precipitation presents a positive relationship on the total sugarcane production.
Regarding cost data, as stated in item 3.1, crop cost data and fixed costs were collected at CONAB [52]. The detailed production cost data are presented in Table 3.
The data provided by CONAB [52] represent an average estimation of the farmers’ expenditure, separated by the farm size.
According to Ali, Zubair, and Hussain [32], crop cost is expected to present a positive relationship with the total sugarcane production.
Descriptive Statistical Analysis
Table 4 presents the descriptive statistics of the analyzed variables. The descriptive analysis and the regression analysis were performed using Stata Software, version 17.
Regarding the climatic variables, Table 4 indicates that although precipitation and mean temperature present slight variations across the region, the same cannot be said regarding CO2 emissions. Crop cost also presents significant variation. However, it must be considered that crop area varies across the farms, impacting crop cost. As expected, fixed cost values are smaller than crop costs. Fixed costs proportionally reduce as farm size increases.
Concerning the sugarcane variables analyzed, it can be observed that all of them present a minimum value equal to zero because farms use different combinations of varieties in the field. Therefore, only a tiny amount of the sugarcane varieties is used by each farm, resulting in several zero values.
A scatterplot with each climatic variable and its relationship with the total sugarcane production variable was also performed. The results are presented in Appendix B. Only mean temperature presented homogeneous results. Precipitation and CO2 emissions do not have a persistent pattern when combined with the total sugarcane production.
Aiming to analyze the sugarcane varieties planted by the sugarcane members, a cluster analysis was performed. The dendrogram was performed after Ward’s linkage clustering with the Euclidean dissimilarity measure. Figure 3 exhibits the dendrogram.
Each sugarcane variable represents one cluster. Stata looks for similarities and dissimilarities as we move upwards using Ward’s linkage method. This way, according to the database used, Stata clusters each variety according to their similarities. The first cluster that is formed unifies the varieties CTC4 and CTC9003, indicating that they possess very similar characteristics. Just above, Stata clusters RB965902 and RB966928, IACSP95-5000 and IACSP95-5094 and, finally, RB855156 and RB855453. The more the dendrogram goes up, the higher the distance between the groups formed, and the more different or dissimilar the clusters are from each other.
4. Results and Discussion
4.1. Regression Analysis
After the above-presented analysis, an econometric model was structured in order to scrutinize the impact of climatic variables on sugarcane production, considering the different sugarcane varieties used by the farmers and control variables. The econometric model used in the regression analysis is presented by Equation (1):
where ϒ is sugarcane production in the period and is the coefficient of the constant of the equation. Precipitation, mean temperature, and CO2 refer to the climatic information of each city where the farm is located. The variables β4–β14 refer to the production of each sugarcane variety. Crop cost and fixed cost refer to the production cost. Finally, µ is the error term of the equation.
ϒ = β0 + β1 Precipitation + β2 Mean Temperature + β3 CO2 + β4 GCTC4 + β5 CTC9003 + β6 IAC911099 + β7 IACSP955000 + β8 IACSP955094 + β9 RB855156 + β10 RB855453 + β11 RB867515 + β12 RB965902 + β13 RB966928 + β14 SP801816 + β15 Crop Cost + β16 Fixed Cost + µ
Table 5 presents the regression results.
Table 5 shows that neither rainfall, mean temperature, or CO2 emissions impact sugarcane production. Precipitation result is in line with Linnenluecke et al. [12] and Pipitpukdee, Attavanich, and Bejranonda [57]. This work also confirms the temperature results of Pipitpukdee, Attavanich, Bejranonda [57], and Ali, Zubair, and Hussain [32]. Table 5 shows that climate variables lose their importance in final production when using the appropriate sugarcane variety.
The insertion of more control variables in the model guarantees that the variables related to climate presented their real impact without aggregating missing variables’ results. The studies of Linnenluecke et al. [12], Pipitpukdee, Attavanich, and Bejranonda [57], and Ali, Zubair, and Hussain [32] presented more control variables than the other articles formerly discussed in Section 2. For example, Silva et al. [28] only presented control variables for the different state regions. McGree et al. [31], in turn, analyzed only climatic variables in their model without control variables. The same can be said regarding Kelkar, Kulkarni, and Rao’s [29] research.
As we controlled the different sugarcane variables, the climate variables lost strength. Therefore, their results did not present significance.
Table 6 presents the regression results replacing the mean temperature variable by maximum and minimum temperatures.
Table 6 shows that neither rainfall, temperature, nor CO2 emissions impact sugarcane production. Table 6 confirms Table 5’s results, indicating that climate variables lose their importance in final production when using the appropriate sugarcane variety. Moreover, as in Table 5, Table 6 presented significative results for crop cost, demonstrating that total production is strongly linked to the amount expended to increase sugarcane yield, confirming Ali, Zubair, and Hussain’s [32] results.
Table 5 and Table 6 show that, despite climate change impacts, it is possible to reach SDGs when the correct sugarcane varieties are chosen. That is an important conclusion from the results presented in Table 5 and Table 6 because it shows that deforesting is not necessary to improve final production, which would help to reach SDG 15. Moreover, choosing the correct sugarcane variety and improving the total production would help to reach SDGs 2 and 7.
4.2. Data Envelopment Analysis
After the econometric analysis, to verify how to improve the productivity of the studied region, a Data Envelopment Analysis (DEA) was performed with the variables, which presented significant results in Table 5 and Table 6. The DEA software used was the Efficiency Measurement System (EMS).
DEA is non-parametric linear programming (LP)-based technique that assesses the relative performance of a set of units used in many areas of science and engineering [58,59,60]. The outcome of the DEA models is a ranking based on the efficiency level of the analyzed data, named Decision Making Units (DMU) [59]. One of the advantages of DEA is the flexibility of its functional form and its use in calculating weights, which allows it to be applied in several fields, such as education, banking, farming, and manufacturing, among others [61,62].
The DEA analysis can be conducted by minimizing inputs and maximizing outputs. Both options were used in the analysis. We analyzed how inputs could be reduced without modifying the output and how much the output could be improved with the same inputs. We considered a constant return to scale (CRS) scenario, suggesting that a given increase in inputs would result in a proportionate increase in outputs. The CRS model was chosen based on [63,64]. According to Banker [64], if the DMUs operate in a competitive market, they are expected to operate at their most productive scale size. The results for the DEA input oriented are presented in Appendix C.
Table A2 in Appendix C shows the DEA results for only 40 DMUs (farms). Since DMU number 40 had only zeros in its data, it was decided to pull it out from the dataset to not interfere with the DEA results.
Column 2 of Table A2 shows the percentage score of each DMU analyzed. Those considered efficient have a 100% score. Regarding the inefficient DMUs, the lower the score value, the more inefficient the DMU is. Table A2 indicates that 25 out of the 40 analyzed farms are efficient. Table A2 also indicates that the higher possible variation among the analyzed data refers to the crop cost. As seen in Table A2, some DMUs could diminish their crop cost by up to 63%, which could mean a possible saving between USD 4000 and USD 292,000 (exchange rate of 31 December 2021), varying according to the size of the farm and the total value expended in crop cost.
EMS software also sets the benchmark the inefficient DMU could target, as seen in the last column. For those DMUs targeted as efficient, the benchmark column displays how many other DMUs the efficient one appointed as a possible benchmark. The efficient DMU, which presented the higher possible target benchmark, was DMU number 8, which was indicated as an achievable benchmark for the other 9 DMUs. This result means that if those nine indicated farms follow DMU 8′s agricultural structure, they can become efficient and improve their earnings despite climatic issues.
After analyzing how much farmers can save in production, it was studied how much their output can be improved with the same inputs. Table A3 presents the results.
Table A3 indicates that despite a significant number of efficient farmers, many inefficient ones in the studied cooperative still exist, as Table A2 had previously indicated. Table A3 indicates that with the same number of inputs, some sugarcane producers can improve their production from 8 to more than 87 percent, i.e., nearly double their production. Moreover, for the efficient ones, if they decide to improve their production, Table 5 and Table 6 indicate the variables they should consider. According to their results, farmers should increase CTC9003 and RB966928 production once they are the varieties that showed the higher impact on sugarcane yield for that region. RB965902 displayed results close to RB966928. However, considering that the dendrogram analysis (Figure 3) showed that those two sugarcane varieties possess similar characteristics, we understand that the one with a higher impact on total production should be chosen.
Better efficiency of sugarcane production is crucial to prevent small farms to bankrupt and to compete with great producers. In Brazil, the increase in sugarcane cultivation has generated social problems and rural exodus, forcing small farmers to sell their lands once they cannot compete with larger producers [65,66]. Therefore, the analysis presented in this section is vital to reach SDG 1. Moreover, the efficiency boost would also contribute to achieving SDG 9 by producing inclusive and sustainable industrialization, as small farmers can transform sugarcane into biofuel and sell it to other companies.
5. Conclusions
Sustainably increasing sugarcane production is challenging, and the impact of climate change has made this outcome more difficult. Due to that, some farmers have deforested and polluted the environment to increase their production.
Looking forward to contributing to those problems and providing ways to achieve some of the SDGs, we developed this study, investigating how to boost sugarcane production eco-efficiency despite climate change.
First, an econometric regression was performed to analyze the impact of climate change on sugarcane production, considering the use of different sugarcane varieties in the field. Afterward, a Data Envelopment Analysis was performed to verify which farms were efficient and how the inefficient ones could improve their production. Results show that despite climatic change issues, it is possible to achieve efficient sugarcane production if the correct sugarcane varieties are chosen and crop cost management is performed correctly.
The analysis demonstrates that it is unnecessary to deforest or pollute to increase sugarcane production. By choosing the correct sugarcane variety and reducing inputs properly, it would be possible to achieve higher production with greater efficiency. Additionally, it would reduce environmental impact. In other words, it would increase sugarcane production eco-efficiency. Moreover, this procedure would contribute to SDGs 9 and 15. In addition, the boost in sugarcane production would make more food and biofuel available, contributing to reaching SDGs 2 and 7. Finally, the suggested procedures would help small producers to increase their production, preventing them from leaving their farms and entering into a poverty situation, contributing to SDG 1.
Sugarcane variety research is well-developed in Brazil, and several institutions promote different sugarcane varieties to surmount different production difficulties. However, this seems to only happen for some sugarcane country producers. Therefore, other countries’ policymakers should elaborate long-term policies supporting the research and adoption of different sugarcane varieties. Using the appropriate sugarcane variety, according to the different regional characteristics, the impact of climate change on sugarcane production will be mitigated, assuring there will not be a shortfall of production in the future.
Despite the originality of this work, it contains some limitations. It is recommended to develop additional studies considering a more extensive database than the one analyzed in this research. Additionally, different countries and regions should be analyzed in future research. Moreover, climate data aggregated annually does not provide a better overview of the relationship with sugarcane production. When precipitation is considered, for example, it must be analyzed if it is raining when sugarcane is planted. Therefore, future research should use a monthly database indicating when sugarcane is planted and when it is harvested.
Author Contributions
Conceptualization, R.L.M. and T.V.D.C.; methodology, R.L.M. and T.V.D.C.; software, T.V.D.C.; validation, R.L.M. and T.V.D.C.; formal analysis, R.L.M. and T.V.D.C.; investigation, R.L.M. and T.V.D.C.; resources, R.L.M.; data curation, T.V.D.C.; writing—original draft preparation, R.L.M. and T.V.D.C.; writing—review and editing, R.L.M. and T.V.D.C.; supervision, R.L.M.; project administration, R.L.M.; funding acquisition, R.L.M. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by FAPEG/CAPES, grant number Edital 18-2020.
Informed Consent Statement
Not applicable.
Data Availability Statement
Due to sugarcane cooperative restrictions, data are not publicly available.
Conflicts of Interest
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Appendix A
Table A1.
Main sugarcane varieties planted in Brazil in the 2019/2020 harvest.
| Classification | Variety | Total (ha) | % |
|---|---|---|---|
| 1 | RB867515 | 1,133,512 | 21.4 |
| 2 | RB966928 | 676,693 | 12.8 |
| 3 | RB92579 | 487,022 | 9.2 |
| 4 | CTC4 | 446,538 | 8.4 |
| 5 | RB855156 | 223,187 | 4.2 |
| 6 | RB855453 | 178,168 | 3.4 |
| 7 | SP83-2847 | 128,980 | 2.4 |
| 8 | CTC9001 | 97,151 | 1.8 |
| 9 | CTC15 | 91,775 | 1.7 |
| 10 | RB855536 | 86,099 | 1.6 |
| 11 | SP80-1816 | 72,753 | 1.4 |
| 12 | IAC95-5000 | 72,472 | 1.4 |
| 13 | SP83-5073 | 72,442 | 1.4 |
| 14 | SP81-3250 | 69,770 | 1.3 |
| 15 | IAC91-1099 | 69,041 | 1.3 |
| 16 | SP80-3280 | 68,418 | 1.3 |
| 17 | CTC2 | 65,586 | 1.2 |
| 18 | SP91-1049 | 62,321 | 1.2 |
| 19 | CTC9003 | 54,990 | 1.0 |
| 20 | RB835054 | 50,204 | 0.9 |
| 21 | CTC20 | 49,462 | 0.9 |
| 22 | CV7870 | 48,589 | 0.9 |
| 23 | RB975201 | 44,691 | 0.8 |
| 24 | RB965902 | 42,942 | 0.8 |
| 25 | SP79-1011 | 42,238 | 0.8 |
| 26 | SP80-1842 | 40,076 | 0.8 |
| 27 | RB928064 | 38,979 | 0.7 |
| 28 | SP78-4764 | 35,103 | 0.7 |
| 29 | CTC9002 | 32,594 | 0.6 |
| 30 | CV6654 | 27,910 | 0.5 |
| Others | 676,934 | 12.8 | |
| Total | 5,286,619 | ||
Source: Oliveira, Barbosa, and Daros [45].
Appendix B
Figure A1.
Climatic variables and the total sugarcane production.
Appendix C
Table A2.
Selected variables for input-oriented data envelopment analysis.
| DMU | Score (%) | ProdCTC4 {I} | ProdCTC9003 {I} | ProdIAC911099 {I} | ProdIACSP955000 {I} | ProdRB855156 {I} | ProdRB855453 {I} | PRODRB867515 {I} | ProdRB965902 {I} | ProdRB966928 {I} | Crop Cost {I} | Sugarcane Production {O} | Benchmarks |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 4 |
| 2 | 100.0 | 0 | 0 | 0 | 0 | 0.02 | 0 | 0.36 | 0 | 0 | 0.62 | 1 | 0 |
| 3 | 84.08 | 0.15 | 0 | 0.09 | 0.03 | 0.21 | 0.05 | 0.27 | 0.12 | 0.07 | 0 | 1 | 1, 4, 7, 8, 9, 10, 20, 32 |
| 4 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.83 | 0 | 0.17 | 0 | 1 | 1 |
| 5 | 66.54 | 0 | 0 | 0.14 | 0 | 0.06 | 0,5 | 0.3 | 0 | 0 | 0 | 1 | 8, 10, 12, 20 |
| 6 | 100.0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 7 | 100.0 | 0.43 | 0 | 0 | 0 | 0 | 0 | 0.33 | 0 | 0.24 | 0 | 1 | 1 |
| 8 | 100.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 9 |
| 9 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.05 | 0.95 | 1 | 1 |
| 10 | 100.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 |
| 11 | 100.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 |
| 12 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 3 |
| 13 | 100.0 | 0 | 0 | 0 | 0.09 | 0 | 0 | 0.33 | 0 | 0.09 | 0.49 | 1 | 0 |
| 14 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 0 | 0 | 0.8 | 1 | 0 |
| 15 | 85.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 8 |
| 16 | 75.72 | 0.01 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.06 | 0.92 | 1 | 8, 32, 35 |
| 17 | 84.03 | 0 | 0 | 0 | 0 | 0 | 0 | 0.61 | 0 | 0 | 0.39 | 1 | 1, 18 |
| 18 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.62 | 0 | 0 | 0.38 | 1 | 3 |
| 19 | 78.47 | 0 | 0 | 0 | 0 | 0.04 | 0 | 0.5 | 0 | 0 | 0.46 | 1 | 1, 18, 20 |
| 20 | 100.0 | 0 | 0 | 0 | 0 | 0.93 | 0 | 0 | 0 | 0 | 0.08 | 1 | 3 |
| 21 | 100,0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 22 | 79.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0.58 | 0 | 0 | 0.42 | 1 | 1, 18 |
| 23 | 61.55 | 0.07 | 0 | 0 | 0 | 0 | 0 | 0.24 | 0 | 0 | 0.69 | 1 | 8, 12, 32 |
| 24 | 92.17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 8 |
| 25 | 80.12 | 0.05 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.95 | 1 | 8, 32 |
| 26 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.61 | 0 | 0.02 | 0.37 | 1 | 0 |
| 27 | 71.11 | 0.08 | 0 | 0 | 0 | 0 | 0.92 | 0 | 0 | 0 | 0 | 1 | 8, 28 |
| 28 | 100.0 | 0.24 | 0 | 0 | 0 | 0 | 0.16 | 0 | 0 | 0 | 0.59 | 1 | 1 |
| 29 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 30 | 53.41 | 0 | 0 | 0 | 0 | 0 | 0.49 | 0.51 | 0 | 0 | 0 | 1 | 8, 12 |
| 31 | 100.0 | 0.23 | 0 | 0 | 0 | 0.13 | 0.06 | 0 | 0 | 0 | 0.58 | 1 | 0 |
| 32 | 100.0 | 0.57 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.43 | 0 | 1 | 7 |
| 33 | 88.51 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 32 |
| 34 | 100.0 | 0.5 | 0.01 | 0.04 | 0 | 0 | 0 | 0 | 0 | 0.45 | 0 | 1 | 0 |
| 35 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 4 |
| 36 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0.3 | 0 | 0 | 0 | 0.7 | 1 | 0 |
| 37 | 84.87 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 32 |
| 38 | 100.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 |
| 39 | 100.0 | 0 | 0 | 0.13 | 0.86 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 41 | 73.27 | 0.51 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.49 | 0 | 1 | 11, 32, 35, 38 |
Appendix D
Table A3.
Selected variables for output-oriented data envelopment analysis.
| DMU | Score (%) | ProdCTC4 {I} | ProdCTC9003 {I} | ProdIAC911099 {I} | ProdIACSP955000 {I} | ProdRB855156 {I} | ProdRB855453 {I} | PRODRB867515 {I} | ProdRB965902 {I} | ProdRB966928 {I} | Crop Cost {I} | Sugarcane Production {O} | Benchmarks |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 4 |
| 2 | 100.0 | 0 | 0 | 0 | 0 | 0.02 | 0 | 0.36 | 0 | 0 | 0.62 | 1 | 0 |
| 3 | 118.9 | 0.15 | 0 | 0.09 | 0.03 | 0.21 | 0.05 | 0.27 | 0.12 | 0.07 | 0 | 1 | 1, 4, 7, 8, 9, 10, 20, 32 |
| 4 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.83 | 0 | 0.17 | 0 | 1 | 1 |
| 5 | 150.2 | 0 | 0 | 0.14 | 0 | 0.06 | 0.5 | 0.3 | 0 | 0 | 0 | 1 | 8, 10, 12, 20 |
| 6 | 100.0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 7 | 100.0 | 0.43 | 0 | 0 | 0 | 0 | 0 | 0.33 | 0 | 0.24 | 0 | 1 | 1 |
| 8 | 100.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 9 |
| 9 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.96 | 0.04 | 1 | 1 |
| 10 | 100.0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 |
| 11 | 100.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 |
| 12 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 3 |
| 13 | 100.0 | 0 | 0 | 0 | 0.09 | 0 | 0 | 0.33 | 0 | 0.09 | 0.49 | 1 | 0 |
| 14 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 0 | 0 | 0.8 | 1 | 0 |
| 15 | 116.6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 8 |
| 16 | 132.0 | 0.01 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.06 | 0.92 | 1 | 8, 32, 35 |
| 17 | 119.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.61 | 0 | 0 | 0.39 | 1 | 1, 18 |
| 18 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.62 | 0 | 0 | 0.38 | 1 | 3 |
| 19 | 127.4 | 0 | 0 | 0 | 0 | 0.04 | 0 | 0.5 | 0 | 0 | 0.46 | 1 | 1, 18, 20 |
| 20 | 100.0 | 0 | 0 | 0 | 0 | 0.02 | 0 | 0 | 0 | 0 | 0.98 | 1 | 3 |
| 21 | 100.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 22 | 125.3 | 0 | 0 | 0 | 0 | 0 | 0 | 0.58 | 0 | 0 | 0.42 | 1 | 1, 18 |
| 23 | 162.4 | 0.07 | 0 | 0 | 0 | 0 | 0 | 0.24 | 0 | 0 | 0.69 | 1 | 8, 12, 32 |
| 24 | 108.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 8 |
| 25 | 124.8 | 0.05 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.95 | 1 | 8, 32 |
| 26 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.61 | 0 | 0.02 | 0.37 | 1 | 0 |
| 27 | 140.6 | 0.08 | 0 | 0 | 0 | 0 | 0.92 | 0 | 0 | 0 | 0 | 1 | 8, 28 |
| 28 | 100.0 | 0.24 | 0 | 0 | 0 | 0 | 0.16 | 0 | 0 | 0 | 0.59 | 1 | 1 |
| 29 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 30 | 187.2 | 0 | 0 | 0 | 0 | 0 | 0.49 | 0.51 | 0 | 0 | 0 | 1 | 8, 12 |
| 31 | 100.0 | 0.23 | 0 | 0 | 0 | 0.13 | 0.06 | 0 | 0 | 0 | 0.58 | 1 | 0 |
| 32 | 100.0 | 0.57 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.43 | 0 | 1 | 7 |
| 33 | 112.9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 32 |
| 34 | 100.0 | 0.99 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 35 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 4 |
| 36 | 100.0 | 0 | 0 | 0 | 0 | 0 | 0.3 | 0 | 0 | 0 | 0.7 | 1 | 0 |
| 37 | 117.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 32 |
| 38 | 100.0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 |
| 39 | 100.0 | 0 | 0.07 | 0.21 | 0.63 | 0 | 0 | 0 | 0 | 0 | 0.1 | 1 | 0 |
| 41 | 136.4 | 0.51 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.49 | 0 | 1 | 11, 32, 35, 38 |
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Figure 1.
Sugarcane production in the state of Goiás. Source: INMET [41].
Figure 1.
Sugarcane production in the state of Goiás. Source: INMET [41].
Figure 2.
Harvest period of selected sugarcane varieties. Source: Sorocabana Sugarcane Rural Association Suppliers and Planters—ASSOCANA [48].
Figure 2.
Harvest period of selected sugarcane varieties. Source: Sorocabana Sugarcane Rural Association Suppliers and Planters—ASSOCANA [48].
Figure 3.
Dendrogram of analyzed sugarcane varieties.
Table 1.
Critical differences between the main sugarcane varieties used in Brazil.
| Main Difference between Sugarcane Varieties |
|---|
| Soil Requirement |
| Very Demanding SP77-5181, SP87-396, SP87-344, SP83-5073, RB85-5546. Demanding RB85-5453, RB85-5036, SP80-1816, SP80-1842, SO87-365, SP80-3280, RB85-5536, SP86-155, SP79-1011, SP81-320, SP-911049. Not at All Demanding RB85-5156, RB83-5053, RB83-5486, RB84-5210, RB85-5113, SP86-42. Does not Require RB72-454, RB92-8064, RB83-5089, RB86-7515, RB86-5230, SP83-2847, RB85-5035, SP85-5077. |
| MATURATION |
| Super Early RB85-5156, SP87-396. Early RB83-5054, RB85-5453, SP77-5181, RB85-5035, RB83-5486, SP83-5073, SP80-1842, SP86-155, IAC86-2210. Medium SP81-3250, SP80-1816, RB84-5210, RB85-5536, SP87-365, RB86-5230, RB85-5113, RB92-8064, SP85-3877, SP86-42, SP83-2847. Late RB72-454, RB83-5089, RB86-7515. |
| TRANSPORT YIELD |
| Worse LouRB83-5486, SP80-1842, RB83-5089, RB83-5054, RB85-5156. Regular RB84-5210, SP80-1816. Good BomSP79-1011, SP77-5181, RB72-454, RB85-5113, RB85-5536, RB84-5257, RB85-5453, SP79-2233, RB86-7515, RB92-8064, SP81-3250. |
| MECHANICAL HARVESTING |
| Worst RB83-5054, RB85-5156, RB83-5089. Bad RB83-5486. Good SP79-1011, RB85-5453, SP80-3280, SP80-1816, SP81-3250, RB85-5113, RB72-454, SP-2233, RB86-7515, RB92-8064. |
| HERBICIDE SENSITIVITY |
| Very Sensitive RB85-5036, RB85-5113, SP87-365, RB86-5230, SP85-3877. Sensitive RB83-5089, RB84-5210, SP80-1816, SP80-1842. |
| FLOWERING |
| Every Year RB85-5035, RB85-5156, RB85-5453, RB84-5197, RB86-5230, SP83-2847. Regularly SP80-1842, SP80-3280, RB83-5486, SP81-3250, SP87-365. Scarce RB83-5089, RB80-6043, RB72-454, SP80-1816, RB86-7515, SP85-3877. Does Not Bloom RB83-5054, RB85-5113, RB85-5536, RB84-5210, RB92-8064, SP79-1011, SP83-5073. |
| DROUGHT TOLERANCE |
| RB86-7515, RB75-8540, SP79-1011, RB83-5054, SP80-1842, RB85-5002, RB85-5156, SP83-5073. |
| WATER-DEMANDING |
| SP79-2233, RB85-5453, RB80-1816, RB85-5536, SP87-344, SP85-3877. |
Source: EMBRAPA-AGEITEC [44].
Table 2.
Sugarcane varieties used by cooperative farms.
| Id | Variety | Occurrence | Percentage (%) | Cumulative Percentage (%) |
|---|---|---|---|---|
| 1 | CTC4 | 140 | 18.54 | 18.54 |
| 2 | RB867515 | 101 | 13.38 | 31.92 |
| 3 | RB966928 | 90 | 11.92 | 43.84 |
| 4 | RB855453 | 88 | 11.66 | 55.5 |
| 5 | IACSP95-5000 | 79 | 10.46 | 65.96 |
| 6 | RB855156 | 57 | 7.55 | 73.51 |
| 7 | IAC91-1099 | 39 | 5.17 | 78.68 |
| 8 | CTC9003 | 38 | 5.03 | 83.71 |
| 9 | RB965902 | 22 | 2.91 | 86.62 |
| 10 | SP80-1816 | 17 | 2.25 | 88.87 |
| 11 | IACSP95-5094 | 16 | 2.12 | 90.99 |
| 12 | RB985476 | 15 | 1.99 | 92.98 |
| 13 | RB855536 | 9 | 1.19 | 94.17 |
| 14 | RB92579 | 9 | 1.19 | 95.36 |
| 15 | CTC2 | 8 | 1.06 | 96.42 |
| 16 | SP81-3250 | 5 | 0.66 | 97.08 |
| 17 | CTC9001 | 3 | 0.4 | 97.48 |
| 18 | RB928064 | 3 | 0.4 | 97.88 |
| 19 | RB935744 | 3 | 0.4 | 98.28 |
| 20 | SP80-3280 | 3 | 0.4 | 98.68 |
| 21 | CTC6 | 2 | 0.26 | 98.94 |
| 22 | CTC9002 | 2 | 0.26 | 99.2 |
| 23 | SP83-2847 | 2 | 0.26 | 99.46 |
| 24 | CV0618 | 1 | 0.13 | 99.59 |
| 25 | IAC93-3046 | 1 | 0.13 | 99.72 |
| 26 | RB835054 | 1 | 0.13 | 99.85 |
| 27 | RB975952 | 1 | 0.13 | 99.98 |
Source: Association of Raw Material Producers for the Bioenergy Industries of Goiás [54].
Table 3.
Crop and fixed costs grouped by farm type.
| Familiar Agriculture Farm | Company Farm | ||
|---|---|---|---|
| Crop cost | Fixed cost | Crop cost | Fixed cost |
| Machinery rent | Depreciation | Airplane cost | Depreciation |
| Employees | Maintenance | Own Machinery costs | Maintenance |
| Rural management | Social charges | Employees | Insurance |
| Seeds | Insurance | Management | |
| Fertilization | Fertilization | ||
| Pesticides | Pesticides | ||
Source: CONAB [52].
Table 4.
Descriptive statistics.
| Variable | Occurrence | Mean | Standard Deviation | Minimum | Maximum |
|---|---|---|---|---|---|
| Sugarcane Production | 41 | 39,375.99 | 45,007.67 | 3263.51 | 251,536.60 |
| Precipitation | 41 | 1332.698 | 83.67527 | 1267.00 | 1439.20 |
| Mean Temperature | 41 | 24.04024 | 0.9118127 | 19.65 | 24.60 |
| CO2 Emissions | 41 | 1,170,406 | 637,236.9 | 132,689.00 | 2,315,748.00 |
| ProdCTC4 | 41 | 6389.682 | 11,020.58 | 0 | 48,667.96 |
| ProdCTC9003 | 41 | 1674.799 | 6092.384 | 0 | 35,024.9 |
| ProdIAC91-1099 | 41 | 1588.096 | 3730.95 | 0 | 16,847.33 |
| ProdIACSP95-5000 | 41 | 3436.406 | 6379.669 | 0 | 22,761.97 |
| ProdIACSP95-5094 | 41 | 389.5798 | 1681.039 | 0 | 10,481.00 |
| ProdRB855156 | 41 | 4164.885 | 11,158.41 | 0 | 45,965.44 |
| ProdRB855453 | 41 | 5431.3 | 13,820.75 | 0 | 77,722.63 |
| PRODRB867515 | 41 | 6363.475 | 10,418.47 | 0 | 40,272.82 |
| ProdRB965902 | 41 | 1088.889 | 3366.534 | 0 | 12,676.98 |
| ProdRB966928 | 41 | 4347.515 | 8074.646 | 0 | 38,144.2 |
| ProdSP801816 | 41 | 857.3232 | 3233.941 | 0 | 17,634.81 |
| Crop Cost | 40 | 1,397,647 | 1732,421 | 24,064.18 | 9,197,896.00 |
| Fixed Cost | 41 | 124,003 | 14,944.66 | 99,421.11 | 133,322.90 |
Table 5.
The impact of climate change on sugarcane production.
| Variables | Total Sugarcane Production |
|---|---|
| Precipitation | −4.781 |
| (23.63) | |
| Mean Temperature | −2443 |
| (2784) | |
| CO2 Emissions | 0.000988 |
| (0.00218) | |
| ProdCTC4 | 0.693 *** |
| (0.152) | |
| ProdCTC9003 | 1.482 *** |
| (0.373) | |
| ProdIAC911099 | 0.744 * |
| (0.375) | |
| ProdIACSP95-5000 | 0.724 *** |
| (0.213) | |
| ProdIACSP95-5094 | 0.516 |
| (0.468) | |
| ProdRB855156 | 0.667 *** |
| (0.168) | |
| ProdRB855453 | 0.546 *** |
| (0.187) | |
| PRODRB867515 | 0.683 *** |
| (0.146) | |
| ProdRB965902 | 0.928 * |
| (0.490) | |
| ProdRB966928 | 0.958 *** |
| (0.183) | |
| ProdSP80-1816 | −0.0714 |
| (0.672) | |
| Crop Cost | 0.00828 ** |
| (0.00377) | |
| Fixed Cost | −0.0127 |
| (0.0658) | |
| Constant | 67,035 |
| (96,989) | |
| Observations | 40 |
| R-squared | 0.994 |
Standard errors in parentheses *** p < 0.01, ** p < 0.05, and * p < 0.1.
Table 6.
The impact of climate change on sugarcane production considering maximum and minimum temperatures.
Table 6.
The impact of climate change on sugarcane production considering maximum and minimum temperatures.
| Variables | Total Sugarcane Production—Maximum Temperature | Total Sugarcane Production—Minimum Temperature |
|---|---|---|
| Precipitation | 16.35 | −138.9 |
| (15.38) | (172.2) | |
| Max. Temperature | −1414 | |
| (1611) | ||
| Min. Temperature | −8978 | |
| (10,230) | ||
| CO2 Emissions | 0.000988 | 0.000988 |
| (0.00218) | (0.00218) | |
| ProdCTC4 | 0.693 *** | 0.693 *** |
| (0.152) | (0.152) | |
| ProdCTC9003 | 1.482 *** | 1.482 *** |
| (0.373) | (0.373) | |
| ProdIAC911099 | 0.744 * | 0.744 * |
| (0.375) | (0.375) | |
| ProdIACSP95-5000 | 0.724 *** | 0.724 *** |
| (0.213) | (0.213) | |
| ProdIACSP95-5094 | 0.516 | 0.516 |
| (0.468) | (0.468) | |
| ProdRB855156 | 0.667 *** | 0.667 *** |
| (0.168) | (0.168) | |
| ProdRB855453 | 0.546 *** | 0.546 *** |
| (0.187) | (0.187) | |
| PRODRB867515 | 0.683 *** | 0.683 *** |
| (0.146) | (0.146) | |
| ProdRB965902 | 0.928 * | 0.928 * |
| (0.490) | (0.490) | |
| ProdRB966928 | 0.958 *** | 0.958 *** |
| (0.183) | (0.183) | |
| ProdSP80-1816 | −0.0714 | −0.0714 |
| (0.672) | (0.672) | |
| Crop Cost | 0.00828 ** | 0.00828 ** |
| (0.00377) | (0.00377) | |
| Fixed Cost | −0.0127 | −0.0127 |
| (0.0658) | (0.0658) | |
| Constant | 36,718 | 259,516 |
| (63,733) | (314,679) | |
| Observations | 40 | 40 |
| R-squared | 0.994 | 0.994 |
Standard errors in parentheses *** p < 0.01, ** p < 0.05, and * p < 0.1.
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Da Cruz, T.V.; Machado, R.L. Increasing Sugarcane Production Eco-Efficiency: A DEA Analysis with Different Sugarcane Varieties. Sustainability 2023, 15, 11201. https://doi.org/10.3390/su151411201
AMA Style
Da Cruz TV, Machado RL. Increasing Sugarcane Production Eco-Efficiency: A DEA Analysis with Different Sugarcane Varieties. Sustainability. 2023; 15(14):11201. https://doi.org/10.3390/su151411201
Chicago/Turabian StyleDa Cruz, Thiago Vizine, and Ricardo Luiz Machado. 2023. "Increasing Sugarcane Production Eco-Efficiency: A DEA Analysis with Different Sugarcane Varieties" Sustainability 15, no. 14: 11201. https://doi.org/10.3390/su151411201
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