pith. sign in

arxiv: 1907.07762 · v1 · pith:KAMLG2BKnew · submitted 2019-07-11 · 💻 cs.CY · cs.SY· eess.SY

Agro 4.0: A Green Information System for Sustainable Agroecosystem Management

Eug\^enio Pacceli Reis da Fonseca , Evandro Caldeira , Heitor Soares Ramos Filho , Leonardo Barbosa e Oliveira , Adriano C\'esar Machado Pereira , Pierre Santos Vilela This is my paper

Pith reviewed 2026-05-24 23:19 UTC · model grok-4.3

classification 💻 cs.CY cs.SYeess.SY
keywords agroecosystem sustainabilitygreen information systemssustainability indicatorsdata science applicationsISA methodologyrural property assessmentsustainable agriculture management
0
0 comments X

The pith

Data from 100 rural properties shows only 7 of 21 ISA indicators are needed to classify agroecosystem sustainability in over 90 percent of cases.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper implements a Green Information System grounded in the Indicators of Sustainability in Agroecosystems methodology. It gathers data across 100 real rural properties and applies data science techniques to isolate the most relevant indicators for the final sustainability score. The central finding is that a subset of seven indicators can correctly identify the sustainability level in more than 90 percent of cases. This result supports the creation of practical tools for collecting, processing, visualizing, and analyzing sustainability data at the property or regional scale. The work also suggests that the full set of 21 indicators could be reduced or that the index formula itself could be adjusted to give greater weight to the retained metrics.

Core claim

Applying data science techniques to ISA data collected from 100 rural properties demonstrates that only seven of the twenty-one indicators suffice to identify the level of sustainability in more than 90 percent of cases. The authors develop an accompanying set of tools for data collection, processing, visualization, and analysis that follow the ISA methodology and allow users to surface best practices across participating agroecosystems.

What carries the argument

The ISA set of 21 sustainability indicators, with a data-science-derived core subset of 7 that accounts for the bulk of the final Sustainability Index Score.

If this is right

  • Sustainability assessments become feasible with substantially reduced data collection requirements.
  • The information system supplies concrete tools that let users monitor and compare properties or regions.
  • The index computation itself may be revised so that the retained indicators carry more weight.
  • Participating agroecosystems can more readily surface and adopt effective sustainable practices.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Lower data demands could make routine sustainability tracking practical for smaller or less-resourced operations.
  • The same selection technique might simplify other multi-metric environmental scoring systems.
  • Pairing the reduced indicator set with sensor networks could shift assessments from periodic to ongoing.
  • Testing the seven-indicator model on farms outside the original sample would clarify its geographic reach.

Load-bearing premise

The values from the 100 sampled rural properties are representative enough that data science methods can reliably extract a general-purpose subset of indicators without overfitting to this particular group.

What would settle it

Running the seven-indicator model on sustainability data from an independent collection of rural properties and obtaining correct classification in fewer than 90 percent of cases.

Figures

Figures reproduced from arXiv: 1907.07762 by Adriano C\'esar Machado Pereira, Eug\^enio Pacceli Reis da Fonseca, Evandro Caldeira, Heitor Soares Ramos Filho, Leonardo Barbosa e Oliveira, Pierre Santos Vilela.

Figure 1
Figure 1. Figure 1: ISA - Sustainability sub-indexes in a sample ISA questionnaire implemented in Microsoft Excel [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: ISA - Environmental Aspects, generated by a sample ISA questionnaire implemented in Microsoft Excel [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: ISA - Soil use occupation generated by a sample ISA questionnaire implemented in Microsoft Excel [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: System architecture the other modules. The other modules, running on the same server, are now able to generate the reports, perform the intelligence analysis solutions, feed the data to the data warehouse module and aggregate the questionnaire’s header data (date, water basin and municipality) in the sets available for user-guided filters. The server also allows the technicians to fill the Adequation Plan … view at source ↗
Figure 5
Figure 5. Figure 5: Agro 4.0 architecture: software stack Reports are generated for each questionnaire that is collected and sent to the server on the go. A user with sufficient permissions can also request dynamically generated reports for a collective of properties: it is possible to filter sets of properties by specific characteristics (location, year of data input, associated institutions, and others) on the fly. Those re… view at source ↗
Figure 6
Figure 6. Figure 6: Agro 4.0’s steps of data processing 4.0 system, those professionals can monitor the work of the technicians by checking the reports and Adequation Plans written for each questionnaire submitted. They can also analyze and find patterns on the reports for the properties of a region or project, and perform other kinds of data interpretation they wish through the dashboards they have access to. Managers also m… view at source ↗
Figure 7
Figure 7. Figure 7: The process of interviewing and sending/editing a questionnaire for a rural property in Agro 4.0, through the desktop client [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The process any human actor executes to query a questionnaire in the Agro 4.0 system, the web component provides this [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: A high level representation of the process the human actors called Managers orchestrate in order to get aggregations of rural [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The process technicians and representatives of rural properties execute to use the Adequation Plan, through Agro 4.0 [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Agro 4.0 : Geographical Visualization Chart, displaying participating properties in Agro 4.0 where Balde Cheio is being [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Methodology set "IndicatorsDS" is generated by formatting in the 21 sustainability indicators computed for each entry of "FeaturesDS"; For the fourth step, Features Selection is applied separately to both "FeaturesDS" and "IndicatorsDS"; The fifth step consists of generating reduced versions of both data sets, filtering out the attributes that did not get picked by the Manuscript submitted to arXiv [PITH… view at source ↗
Figure 13
Figure 13. Figure 13: Categorical Variable: Associative Forms In [PITH_FULL_IMAGE:figures/full_fig_p024_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: it is possible to observe that more than 60 properties have sufficient control of their cash flow [PITH_FULL_IMAGE:figures/full_fig_p024_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Categorical Variable: Certified Products [PITH_FULL_IMAGE:figures/full_fig_p024_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Categorical Variable: Environmental Regulation [PITH_FULL_IMAGE:figures/full_fig_p025_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Categorical Variable: Capacity for Innovation and Leadership [PITH_FULL_IMAGE:figures/full_fig_p025_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Indicators Heatmap indicator of the Sub-Index Economic Balance and the other two indicators are aggregated in the Business Management sub-index. It’s possible to see that those 3 indicators impact the results expressed in [PITH_FULL_IMAGE:figures/full_fig_p026_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Agro 4.0 : Averages Radar Chart of the Sub-Indexes for the 100 properties participating in both Agro 4.0 and Balde Cheio. [PITH_FULL_IMAGE:figures/full_fig_p027_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Agro 4.0: Averages Radar Chart of the first 11 Indicators for the 100 properties participating in both Agro 4.0 and Balde Cheio. [PITH_FULL_IMAGE:figures/full_fig_p027_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Agro 4.0 : Averages Radar Chart of the Environmental Aspects related Indicators for the 100 properties participating in both [PITH_FULL_IMAGE:figures/full_fig_p028_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Agro 4.0 : Averages Radar Chart of the Environmental Aspects related Indicators for the 100 properties participating in both [PITH_FULL_IMAGE:figures/full_fig_p028_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Agro 4.0 : Averages Radar Chart of the Environmental Aspects related Indicators for the 100 properties participating in both [PITH_FULL_IMAGE:figures/full_fig_p029_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Agro 4.0 : Averages Radar Chart of the Environmental Aspects related Indicators for the 100 properties participating in both [PITH_FULL_IMAGE:figures/full_fig_p030_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: The UML diagram for the most relevant human interaction and questionnaires delivery process of the Agro 4.0 system [PITH_FULL_IMAGE:figures/full_fig_p041_25.png] view at source ↗
read the original abstract

Agriculture is one of the most critical activities developed today by humankind and is in constant technical evolution to supply food and other essential products to everlasting and increasing demand. New machines, seeds, and fertilizers were developed to increase the productivity of cultivated areas. It is estimated that by 2050 we will have a population of 9 billion people and the production of food to meet this demand must occur sustainably. To achieve this goal, it is paramount the adoption of sustainable management techniques for agroecosystems. However, this is a complex task due to a large number of variables involved. One of the solutions for the handling and treatment of such diverse data is the use of Green IS. In this work, we adopt a methodology called Indicators of Sustainability in Agroecosystems (Indicadores de Sustentabilidade em Agroecossistemas -- ISA), implement an information system based on it and apply Data Science techniques over the gathered data - from 100 real rural properties - to compute which are the most relevant ISA Indicators for the final ISA Sustainability Index Score. As a result, we have developed a set of tools for data collection, processing, visualization, and analysis of the sustainability of a rural property or region, following the ISA methodology. We also have that with only 7 of the 21 Indicators present in ISA we can identify the level of sustainability in more than 90% of cases, allowing for a new discussion about shrinking the amount of data needed for the computation of ISA, or remodelling the final computation of the Sustainability Index so other Indicators can be more expressive. Users of the solutions developed in this work can identify best practices for sustainability in participating agroecosystems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper describes development of the Agro 4.0 Green IS implementing the ISA (Indicadores de Sustentabilidade em Agroecossistemas) methodology. Data from 100 rural properties are analyzed with data science techniques to identify the most relevant subset of the 21 ISA indicators; the central claim is that only 7 indicators suffice to identify the sustainability level in more than 90% of cases. The work also delivers practical tools for data collection, processing, visualization, and analysis.

Significance. If the reduced-indicator result holds under rigorous validation, the finding could meaningfully lower the data-collection burden for sustainability assessment while preserving high predictive fidelity for the ISA index, and the accompanying open tools would constitute a concrete contribution to applied Green IS in agroecosystems.

major comments (3)
  1. [Abstract] Abstract: the headline claim that 'with only 7 of the 21 Indicators present in ISA we can identify the level of sustainability in more than 90% of cases' is presented without any description of the feature-selection procedure, the predictive model, the performance metric, handling of missing values, or validation strategy (train/test split, k-fold CV, or external set).
  2. [Methods] Methods/Results: with n=100 properties and p=21 indicators, any pipeline that ranks indicators on the full dataset and then reports accuracy on the same samples risks optimistic bias; the manuscript supplies no evidence that selection occurred inside cross-validation folds or on a held-out test set.
  3. [Results] Results: the post-hoc selection of the 7-indicator subset and the reported >90% identification rate require explicit comparison against a null model or baseline (e.g., random subset or full-indicator model) plus statistical significance assessment to establish that the reduction is not an artifact of the particular sample.
minor comments (2)
  1. [Abstract] The sentence 'We also have that with only 7...' is grammatically awkward and should be revised for clarity.
  2. The manuscript would benefit from an explicit equation or pseudocode for the final ISA Sustainability Index Score so readers can see how the 21 indicators are aggregated.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which identify important gaps in methodological transparency and statistical rigor. We agree that the current manuscript requires substantial revision on these points and outline below how each will be addressed. All changes will be incorporated into a revised version.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim that 'with only 7 of the 21 Indicators present in ISA we can identify the level of sustainability in more than 90% of cases' is presented without any description of the feature-selection procedure, the predictive model, the performance metric, handling of missing values, or validation strategy (train/test split, k-fold CV, or external set).

    Authors: We agree the abstract is insufficiently informative. In the revision we will expand the abstract to briefly state the feature-selection method (recursive feature elimination with a random-forest classifier), the performance metric (balanced accuracy), the handling of missing values (median imputation within folds), and the validation strategy (nested 5-fold cross-validation with an outer held-out test set of 20 properties). The expanded abstract will remain within length limits while directing readers to the Methods section for full detail. revision: yes

  2. Referee: [Methods] Methods/Results: with n=100 properties and p=21 indicators, any pipeline that ranks indicators on the full dataset and then reports accuracy on the same samples risks optimistic bias; the manuscript supplies no evidence that selection occurred inside cross-validation folds or on a held-out test set.

    Authors: The referee is correct that the original analysis performed feature ranking on the full dataset, creating a risk of optimistic bias. We will re-execute the entire pipeline inside nested cross-validation: feature selection will be performed only on training folds, performance will be evaluated on held-out outer-test folds, and the final 7-indicator subset will be the one that most frequently ranks highest across folds. The revised Methods and Results sections will document this corrected procedure and the resulting performance figures. revision: yes

  3. Referee: [Results] Results: the post-hoc selection of the 7-indicator subset and the reported >90% identification rate require explicit comparison against a null model or baseline (e.g., random subset or full-indicator model) plus statistical significance assessment to establish that the reduction is not an artifact of the particular sample.

    Authors: We accept that baselines and significance testing are required. The revision will add (i) performance of the full 21-indicator model, (ii) average performance of 100 randomly chosen 7-indicator subsets, and (iii) a permutation test (10 000 permutations) comparing the selected subset against the null distribution. These results, together with 95 % bootstrap confidence intervals, will be reported in a new table and discussed in the Results section. revision: yes

Circularity Check

0 steps flagged

No circularity detected; central claim is empirical output from external field data

full rationale

The paper collects indicator values from 100 real rural properties, then applies data science techniques (feature selection or importance ranking) to identify a 7-indicator subset that achieves >90% identification of sustainability level. This numerical result is presented as the output of analysis on externally gathered data rather than a quantity defined by the paper's own equations, normalizations, or self-citations. No load-bearing step reduces by construction to the inputs; the ISA index itself is an external methodology, and the reduced-set accuracy is not forced by definition or renaming. The derivation chain is self-contained against the collected sample.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that the ISA indicator set is a valid and stable measure of sustainability whose internal correlations can be exploited by standard feature-selection methods, plus the implicit assumption that the 100-property convenience sample is sufficiently representative for the reported reduction to generalize.

free parameters (1)
  • Indicator subset size (7)
    Chosen post-hoc via data analysis to reach the stated 90% threshold; the exact selection criterion and any regularization parameters are unspecified.
axioms (1)
  • domain assumption ISA methodology provides a valid quantitative measure of agroecosystem sustainability
    The paper adopts the 21-indicator ISA framework without independent validation or sensitivity analysis of its weighting scheme.

pith-pipeline@v0.9.0 · 5871 in / 1375 out tokens · 23384 ms · 2026-05-24T23:19:50.690745+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

79 extracted references · 79 canonical work pages

  1. [1]

    Abu Zafar Abbasi, Noman Islam, Zubair Ahmed Shaikh, et al. 2014. A review of wireless sensors and networks’ applications in agriculture.Computer Standards & Interfaces 36, 2 (2014), 263–270

    work page 2014

  2. [2]

    Prenafeta-Boldú Andreas Kamilaris, Andreas Kartakoullis

    Francesc X. Prenafeta-Boldú Andreas Kamilaris, Andreas Kartakoullis. 2017. A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture 143 (2017), 23–37. https://doi.org/10.1016/j.compag.2017.09.037

    work page doi:10.1016/j.compag.2017.09.037 2017

  3. [3]

    Benoit A Aubert, Andreas Schroeder, and Jonathan Grimaudo. 2012. IT as enabler of sustainable farming: An empirical analysis of farmers’ adoption decision of precision agriculture technology. Decision support systems 54, 1 (2012), 510–520

    work page 2012

  4. [4]

    Jan Bebbington and Rob Gray. 2001. An account of sustainability: failure, success and a reconceptualization. Critical perspectives on accounting 12, 5 (2001), 557–587

    work page 2001

  5. [5]

    Rodolfo Bongiovanni and Jess Lowenberg-DeBoer. 2004. Precision agriculture and sustainability. Precision agriculture 5, 4 (2004), 359–387

    work page 2004

  6. [6]

    PROJETO BALDE CHEIO

    Marcio Silva Borges, Cézar Augusto Miranda Guedes, and Renato Linhares de Assis. 2011. UM ESTUDO DO" PROJETO BALDE CHEIO" COMO VETOR DE DESENVOLVIMENTO SUSTENTÁVEL DO PEQUENO PRODUTOR DE LEITE. Revista Brasileira de Agropecuária Sustentável 1, 1 (2011)

    work page 2011

  7. [7]

    Watson R

    Boudreau M. Watson R. Chen, A. 2008. Information systems and ecological sustainability. Journal of Systems and Information Technology 10, 3 (2008), 186–201

    work page 2008

  8. [8]

    Jón Atli Benediktsson-Zhongyi Sun Jinsheng She Yangyong Zhu Chi M., Antonio Plaza. 2016. Big Data for Remote Sensing: Challenges and Opportunities. Proc. IEEE 104, 11 (2016), 2207–2219. https://doi.org/10.1109/JPROC.2016.2598228 Manuscript submitted to arXiv 36 Eugênio Fonseca, Evandro Caldeira, Heitor Ramos, Pierre Vilela, Leonardo B. Oliveira, Adriano P...

    work page doi:10.1109/jproc.2016.2598228 2016

  9. [9]

    William W Cohen. 1995. Fast effective rule induction. In Machine Learning Proceedings 1995 . Elsevier, 115–123

    work page 1995

  10. [10]

    Ine Coteur, Fleur Marchand, Lies Debruyne, Floris Dalemans, and Ludwig Lauwers. 2016. A framework for guiding sustainability assessment and on-farm strategic decision making. Environmental Impact Assessment Review 60 (2016), 16 – 23. https://doi.org/10.1016/j.eiar.2016.04.003

    work page doi:10.1016/j.eiar.2016.04.003 2016

  11. [11]

    Evelien M De Olde, Frank W Oudshoorn, Claus AG Sørensen, Eddie AM Bokkers, and Imke JM De Boer. 2016. Assessing sustainability at farm-level: Lessons learned from a comparison of tools in practice. Ecological Indicators 66 (2016), 391–404

    work page 2016

  12. [12]

    Charlene C de Resende, Adriano CM Pereira, Rodrigo TN Cardoso, and AR Bosco de Magalhães. 2017. Investigating market efficiency through a forecasting model based on differential equations. Physica A: Statistical Mechanics and its Applications 474 (2017), 199–212

    work page 2017

  13. [13]

    Jason Dedrick. 2010. Green IS: Concepts and Issues for Information Systems Research. Communications of the Association for Information Systems 27 (2010). https://doi.org/10.17705/1CAIS.02711

    work page doi:10.17705/1cais.02711 2010

  14. [14]

    Nicola Dempsey, Glen Bramley, Sinéad Power, and Caroline Brown. 2011. The social dimension of sustainable development: Defining urban social sustainability. Sustainable development 19, 5 (2011), 289–300

    work page 2011

  15. [15]

    José Mário Lobo Ferreira, João Herbert Moreira Viana, Adriana Monteiro da Costa, Daniel Vieira de Sousa, and Andréia Aline Fontes. 2012. Indicadores de Sustentabilidade em Agroecossistemas. Informe Agropecuário, Belo Horizonte 33, 271 (2012), 12–25

    work page 2012

  16. [16]

    Spyros Fountas, Giacomo Carli, Claus Grøn Sørensen, Z Tsiropoulos, Christos Cavalaris, A Vatsanidou, B Liakos, Maurizio Canavari, Jens Wiebensohn, and B Tisserye. 2015. Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture 115 (2015), 40–50

    work page 2015

  17. [17]

    DM Freebairn and CA King. 2003. Reflections on collectively working toward sustainability: indicators for indicators! Animal Production Science 43, 3 (2003), 223–238

    work page 2003

  18. [18]

    Peter Glavič and Rebeka Lukman. 2007. Review of sustainability terms and their definitions. Journal of cleaner production 15, 18 (2007), 1875–1885

    work page 2007

  19. [19]

    Fritz Häni, Francesco Braga, Andreas Stämpfli, Thomas Keller, Matthew Fischer, Hans Porsche, et al. 2003. RISE, a tool for holistic sustainability assessment at the farm level. International food and agribusiness management review 6, 4 (2003), 78–90

    work page 2003

  20. [20]

    Wilca Barbosa Hempel. 2009. A importância do ICMS ecológico para a sustentabilidade ambiental no Ceará. REDE-Revista Eletrônica do PRODEMA 2, 2 (2009)

    work page 2009

  21. [21]

    Chen, Richard Watson, Marie-claude Boudreau, and Elena Karahanna

    Adela J. Chen, Richard Watson, Marie-claude Boudreau, and Elena Karahanna. 2009. Organizational Adoption of Green IS & IT: An Institutional Perspective. ICIS 2009 Proceedings - Thirtieth International Conference on Information Systems , 142

    work page 2009

  22. [22]

    Brian R Keeble. 1988. The Brundtland report:‘Our common future’. Medicine and War 4, 1 (1988), 17–25

    work page 1988

  23. [23]

    M.C.Rufino M.T.van Wijk S.Zingore P.Mapfumo-S.Adjei-Nsiah M.Herrero R.Chikowo M.Corbeels E.C.Rowe F.Baijukya A.Mwijage J.Smith E.Yeboah W.J.van der Burg O.M.Sanogo M.Misiko N.de Ridder S.Karanja C.Kaizzi J.K’ungu M.Mwale D.Nwaga C.Pacini B.Vanlauwe K.E.Giller, P.Tittonell

    work page

  24. [24]

    Agricultural Systems 104, 2 (2011), 191–203

    Communicating complexity: Integrated assessment of trade-offs concerning soil fertility management within African farming systems to support innovation and development. Agricultural Systems 104, 2 (2011), 191–203. https://doi.org/10.1016/j.agsy.2010.07.002

    work page doi:10.1016/j.agsy.2010.07.002 2011

  25. [25]

    Yunseop Kim, Robert G Evans, and William M Iversen. 2008. Remote sensing and control of an irrigation system using a distributed wireless sensor network. IEEE transactions on instrumentation and measurement 57, 7 (2008), 1379–1387

    work page 2008

  26. [26]

    MJ Kropff, J Bouma, and JW Jones. 2001. Systems approaches for the design of sustainable agro-ecosystems. Agricultural Systems 70, 2 (2001), 369–393

    work page 2001

  27. [27]

    M.; Marangoni-G.; Keller K.; Garner G

    P. M.; Marangoni-G.; Keller K.; Garner G. G. Lamontagne, J. R.; Reed. 2019. Robust abatement pathways to tolerable climate futures require immediate global action. Nature Climate Change (mar 2019). https://doi.org/10.1038/s41558-019-0426-8

    work page doi:10.1038/s41558-019-0426-8 2019

  28. [28]

    Zuoming Liu, Huaqing Wang, and Ping. Li. 2018. The Antecedents of Green Information System and Impact on Environmental Performance. SSRN (2018). https://doi.org/10.2139/ssrn.3177907

    work page doi:10.2139/ssrn.3177907 2018

  29. [29]

    Arvind Malhotra, Nigel Melville, and Richard T Watson. 2013. Spurring impactful research on information systems for environmental sustainability. Management Information Systems Quarterly 37, 4 (2013), 1265–1274

    work page 2013

  30. [30]

    Kátia Marzall and Jalcione Almeida. 2000. INDICADORES DE SUSTENTABILIDADE PARA AGROECOSSISTEMAS Estado da arte, limites e potencialidades de uma nova ferramenta para avaliar o desenvolvimento sustentável. Cadernos de Ciência & Tecnologia 17, 1 (2000), 41–59

    work page 2000

  31. [31]

    Surabhi Mittal and Mamta Mehar. 2016. Socio-economic factors affecting adoption of modern information and communication technology by farmers in India: Analysis using multivariate probit model. The Journal of Agricultural Education and Extension 22, 2 (2016), 199–212

    work page 2016

  32. [32]

    Mihaela Muntean. 2018. Business Intelligence Issues for Sustainability Projects. Sustainability 10 (01 2018), 335. https://doi.org/10.3390/su10020335

    work page doi:10.3390/su10020335 2018

  33. [33]

    Raimo Nikkilä, Ilkka Seilonen, and Kari Koskinen. 2010. Software architecture for farm management information systems in precision agriculture. Computers and electronics in agriculture 70, 2 (2010), 328–336

    work page 2010

  34. [34]

    Stefanos A Nikolidakis, Dionisis Kandris, Dimitrios D Vergados, and Christos Douligeris. 2015. Energy efficient automated control of irrigation in agriculture by using wireless sensor networks. Computers and Electronics in Agriculture 113 (2015), 154–163

    work page 2015

  35. [35]

    André Monteiro Novo, Maja Slingerland, Kees Jansen, Argyris Kanellopoulos, and Ken E Giller. 2013. Feasibility and competitiveness of intensive smallholder dairy farming in Brazil in comparison with soya and sugarcane: Case study of the Balde Cheio Programme. Agricultural systems 121 (2013), 63–72

    work page 2013

  36. [36]

    Gaio Cesare Pacini, Giulio Lazzerini, and Concetta Vazzana. 2011. AESIS: a support tool for the evaluation of sustainability of agroecosystems. Example of applications to organic and integrated farming systems in Tuscany, Italy. Italian Journal of Agronomy 6, 1 (2011), 3

    work page 2011

  37. [37]

    Saulo Rodrigues Filho and Antonio José Juliani. 2013. Sustentabilidade da produção de etanol de cana-de-açúcar no Estado de São Paulo. estudos avançados 27, 78 (2013), 195–212. Manuscript submitted to arXiv Agro 4.0 : A Green Information System for Sustainable Agroecosystem Management 37

    work page 2013

  38. [38]

    Tim Rogmans and Mohammad Ghunaim. 2016. A framework for evaluating sustainability indicators in the real estate industry. Ecological Indicators 66 (2016), 603 – 611. https://doi.org/10.1016/j.ecolind.2016.01.058

    work page doi:10.1016/j.ecolind.2016.01.058 2016

  39. [39]

    Ramayah-Alemayehu Mollad Roya Gholamia, Ainin Binti Sulaiman

    T. Ramayah-Alemayehu Mollad Roya Gholamia, Ainin Binti Sulaiman. 2013. Senior managers’ perception on green information systems (IS) adoption and environmental performance: Results from a field survey.Information & Management 50, 7 (2013), 431–438. https://doi.org/10.1016/j.im.2013.01.004

    work page doi:10.1016/j.im.2013.01.004 2013

  40. [40]

    R Shaw, RM Lark, AP Williams, DR Chadwick, and DL Jones. 2016. Characterising the within-field scale spatial variation of nitrogen in a grassland soil to inform the efficient design of in-situ nitrogen sensor networks for precision agriculture. Agriculture, Ecosystems & Environment 230 (2016), 294–306

    work page 2016

  41. [41]

    Murty, S.K

    Rajesh Kumar Singh, H.R. Murty, S.K. Gupta, and A.K. Dikshit. 2009. An overview of sustainability assessment methodologies. Ecological Indicators 9, 2 (2009), 189 – 212. https://doi.org/10.1016/j.ecolind.2008.05.011

    work page doi:10.1016/j.ecolind.2008.05.011 2009

  42. [42]

    Iver Thysen. 2000. Agriculture in the information society. Journal of agricultural engineering research 76, 3 (2000), 297–303

    work page 2000

  43. [43]

    Tilman David, Cassman Kenneth G., Matson Pamela A., Naylor Rosamond, and Polasky Stephen. 2002. Agricultural sustainability and intensive production practices. Nature 418, 6898 (aug 2002), 671–677. https://doi.org/10.1038/nature01014 10.1038/nature01014

    work page doi:10.1038/nature01014 2002

  44. [44]

    Jenkin, Jane Webster

    Lindsay McShane Tracy A. Jenkin, Jane Webster. 2011. An agenda for ‘Green’ information technology and systems research. Information and Organization 21, 1 (2011), 17–40. https://doi.org/10.1016/j.infoandorg.2010.09.003

    work page doi:10.1016/j.infoandorg.2010.09.003 2011

  45. [45]

    N Van Cauwenbergh, K Biala, Charles Bielders, V Brouckaert, L Franchois, V Garcia Cidad, Martin Hermy, Erik Mathijs, Bart Muys, J Reijnders, et al

    work page

  46. [46]

    Agriculture, ecosystems & environment 120, 2-4 (2007), 229–242

    SAFE—A hierarchical framework for assessing the sustainability of agricultural systems. Agriculture, ecosystems & environment 120, 2-4 (2007), 229–242

    work page 2007

  47. [47]

    Marcel van Marrewijk. 2003. Concepts and Definitions of CSR and Corporate Sustainability: Between Agency and Communion. Journal of Business Ethics 44, 2 (01 May 2003), 95–105. https://doi.org/10.1023/A:1023331212247

    work page doi:10.1023/a:1023331212247 2003

  48. [48]

    2016.Data Mining: Practical machine learning tools and techniques

    Ian H Witten, Eibe Frank, Mark A Hall, and Christopher J Pal. 2016.Data Mining: Practical machine learning tools and techniques . Morgan Kaufmann

    work page 2016

  49. [49]

    Martin Stack Xuan Pham. 2018. How data analytics is transforming agriculture. Business Horizons 61, 1 (2018), 125–133. https://doi.org/10.1016/j. bushor.2017.09.011 A ISA QUESTIONNAIRE FIELDS This appendix lists the names and enumeration of the fields in the questionnaire defined by the ISA Methodology. An instance of said questionnaire is fulfilled for e...

    work page doi:10.1016/j 2018

  50. [50]

    IDENTIFYING THE INTERVIEWER: 2.1 Name ; 2.2 CPF

    work page

  51. [51]

    LOCATION AND IDENTIFICATION OF THE RURAL PROPERTY: 3.1 Geographical coordinates of the rural property - GPS (headquarter or an identifiable reference point in the sketch): Degree, Latitude, Longitude, Altitude ; 3.2 Name of the municipality ; 3.3 Name of nearest watercourse ; 3.4 Code of the rural property

    work page

  52. [52]

    IDENTIFYING THE INTERVIEWER: 4.1 Name of the interviewee ; 4.2 CPF

    work page

  53. [53]

    PRODUCER PROFILE: Owner’s age

    work page

  54. [54]

    DESCRIPTION OF RURAL PROPERTY: 7.1 Name of the rural property ; 7.2 Description of the land(s) that make up the rural property (launch only contiguous areas = CAR) ; 7.3 Framing of the rural property, Size of the fiscal module in the municipality (ha) ; 7.4 Description of other areas not contiguous to the rural property and / or rental areas that integrat...

    work page

  55. [55]

    DESCRIPTION OF EMPLOYEES / PARTNERSHIPS: Workers / partnerships (Number): Permanent, Temporary, Sharecropper (including family members with direct link with production), Service exchange

    work page

  56. [56]

    Manuscript submitted to arXiv 38 Eugênio Fonseca, Evandro Caldeira, Heitor Ramos, Pierre Vilela, Leonardo B

    RESIDENCES IN THE RURAL PROPERTY: 9.1 Number of family residences of the producer ; 9.2 Number of employee residences and / or sharecroppers. Manuscript submitted to arXiv 38 Eugênio Fonseca, Evandro Caldeira, Heitor Ramos, Pierre Vilela, Leonardo B. Oliveira, Adriano Pereira, et al

    work page

  57. [57]

    SOIL USE AND OCCUPANCY ON THE RURAL PROPERTY: 10.1 Specifications of production areas in the rural property (description, area, irrigation systems): Permanent crops, Temporary crops, Pastures, Forestry ; 10.2 Current and historical land use and occupation: Description, Current use Area (ha), proportion to total area (%), Historic Area (ha) for Permanent c...

    work page

  58. [58]

    ESTIMATED GROSS INCOME INSIDE AND OUT OF RURAL ENTERPRISE: 11.1 Estimated annual gross income (from the rural enterprise), Activities / Products (values) ; 11.2 Estimated annual gross income (outside the rural enterprise), amounts: Pension, retirement, financial aid (grants and others) / Other activities / services

    work page

  59. [59]

    ASSESSMENT OF RURAL PROPERTY (Quantities, values, historical values): 12.1 Facilities and other betterments ; 12.2 Machinery and Equipment ; 12.3 Animals (bulls) Bulls, Oxen, Cows, Tourinhos and / or clubs, Heifers, Calves, Heifers, Equines, Muares, Pigs, Goats / Sheep, Poultry ; 12.4 Irrigation (table with name and area)

    work page

  60. [60]

    ESTIMATE OF THE VALUE OF THE RURAL PROPERTY: 13.1 Land reference value in the region (R$ / ha) ; 13.2 History - Land value in the region ; 13.3 Total estimated value of the rural property

    work page

  61. [61]

    WATER RESOURCES: 14.1 Number of springs and perennial water eyes in the rural property ; 14.2 Number of natural lakes and ponds in the rural property ; 14.3 Number of dams in rural property ; 14.4 Number of water courses in rural property ; 14.5 Source of water used in the establishment ; 14.6 Problems of water availability (for human consumption and activities)

    work page

  62. [62]

    RESIDUES AND EFFLUENTS GENERATED IN THE RURAL PROPERTY: 15.1 Destination of sewage generated in residences ; 15.2 Destination of garbage (domestic and activities)

    work page

  63. [63]

    ENVIRONMENTAL REGULARIZATION OF THE RURAL PROPERTY: 16.1 Has regularization of water use (grant or insignificant use) ; 16.2 Has environmental license or non-passable certificate or AAF ; 16.3 It has regularization of the Legal Reserve and Permanent Preservation Areas

    work page

  64. [64]

    CRITICAL POINTS OF THE ENTERPRISE

    work page

  65. [65]

    A.2 Part Two: Geoprocessing

    EVALUATION OF WATER QUALITY IN THE RURAL PROPERTY: 18.1 Type of occupation of the banks of the water body (main activity) ; 18.2 Antropic changes ; 18.3 Shading from the vegetal cover in the bed (from the margins) ; 18.4 Near erosion and / or on the banks of the body of water and silting in its bed ; 18.5 Water transparency ; 18.6 Odor of water ; 18.7 Wat...

    work page

  66. [66]

    2.2 - Administrative easement

    USE AND OCCUPATION OF SOIL IN THE RURAL PROPERTY: 2.1 - Land use and occupation according to CAR (ha) sketch: Cropland / Grassland / Forestry / non-agricultural area, Fallowing Area, Water Mirror (reservoirs) and watercourses, Remnant of Native Vegetation. ; 2.2 - Administrative easement

    work page

  67. [67]

    AREAS OF PERMANENT PRESERVATION (APPs) IN THE RURAL PROPERTY: 3.1 - Wet and dry APPs Area (ha) (%), wet APPs, dry APPs, TOTAL ; 3.2 - Land use in PPAs: Area (ha), proportion to total area (%): Native vegetation, Area to be reclaimed, Area of use consolidated, Area to be reclaimed for real estate up to 4MF

    work page

  68. [68]

    Manuscript submitted to arXiv Agro 4.0 : A Green Information System for Sustainable Agroecosystem Management 39 A.3 Part three: Indicators

    REMANESCENTS OF NATIVE VEGETATION IN THE RURAL PROPERTY: 4.1 - Native vegetation outside APPs, Native vegetation area (ha) ; 4.2 - Area with native vegetation exceeding the area required for RL in the rural property, Area of native vegetation (ha). Manuscript submitted to arXiv Agro 4.0 : A Green Information System for Sustainable Agroecosystem Management...

    work page

  69. [69]

    PRODUCTIVITY INDICES AND CLEARING SALES, Main activities of the establishment: 1.1 Description of activities ; 1.2 Units of measurement ; 1.3 Current average productivity ; 1.4 Average selling price (R$ / un.) ; 1.5 Average productivity in the region ; 1.6 Average price of the region (R$ / un.)

    work page

  70. [70]

    INCOME DIVERSITY: Income share (%) Weighting factor: 2.1. Agricultural, livestock and forestry activities ; 2.2 Other activities in the establishment: tourism, handicrafts, agribusiness ; 2.3 Other off-premises activities ; 2.4 Retirement; Pension; Financial help; Other sources of income ; 2.5 Verification - occurrence of concentration of agricultural inc...

    work page

  71. [71]

    RURAL PROPERTY ASSETS DEVELOPMENT: Evolution in the period (3.1 Land value in the region ; 3.2 Improve- ments ; 3.3 Equipment ; 3.4 Livestock ; 3.5 Extension of the area of irrigated agriculture and / or agriculture ; 3.6 Balance Sheet ; 3.7 Balance sheet not counting on the valuation of land

    work page

  72. [72]

    DEGREE OF INDEBTEDNESS: 4.1 Debt value in relation to equity (%)

    work page

  73. [73]

    Check the surroundings of the residences (own family consumption): Vegetables, grains and tubers, Orchard, Breeding of animals

    BASIC SERVICES AVAILABLE FOR RURAL PROPERTY / FOOD SAFETY: 5.1 Basic services available in the residences: Availability of water (quantity and quality), Access to electricity, Regular access to production and receipt of inputs, Access to health service, Regular access to school transport, Field security (patrol for rural policing), Telephone (cellular, ru...

    work page

  74. [74]

    SCHOOLING / COURSES ADDRESSED TO THE MAIN ACTIVITIES IN THE RURAL PROPERTY: 6.1 Number of people in the establishment ; 6.2 Less than 5 years of study ; 6.3 5 to 9 years of study ; 6.4 Over 9 years of study ; 6.5 Upper course ; 6.6 Training short season ; 6.7 Long-term training ; 6.8 Attend school network

    work page

  75. [75]

    QUALITY OF OCCUPATION AND EMPLOYMENT GENERATED: 7.1 Total of people in the rural property ; 7.2 Employee registration (work permit) ; 7.3 Overtime payment (or hour bank) ; 7.4 Over 1 minimum wage ; 7.5 Feeding aid ; 7.6 Housing assistance ; 7.7 Education and transport aid ; 7.8 Profit Sharing ; 7.9 Accident insurance ; 7.10 Access to leisure ; 7.11 Space ...

    work page

  76. [76]

    BUSINESS MANAGEMENT: 8.1 Cash flow (income / expense) ; 8.2 Cost of production of activities ; 8.3 Access to technical assistance (private or public) ; 8.4 Participation - associative forms - active (1) or passive (0.5) ; 8.5 Environmental regulation (water use, RL, APP and licensing) ; 8.6 Use credit for investment ; 8.7 Use credit for costing ; 8.8 Uses...

    work page

  77. [77]

    INFORMATION MANAGEMENT: 9.1 Seeks information for commercialization of the production / seeks diversifi- cation of buyers ; 9.2 Generates certified products and / or institutional market ; 9.3 Adoption of Innovative Techniques ; 9.4 Capacity for innovation or leadership in the community

    work page

  78. [78]

    10.5 Treatment of gaseous effluents (generated in boilers, biodigesters, charcoal)

    MANAGEMENT OF RESIDUES AND EFFLUENTS GENERATED IN THE RURAL PROPERTY: 10.1 Proper collection and disposal of waste (household and business waste, recyclable and non-recyclable) ; 10.2 Appropriate disposal of domestic sewage ; 10.3 Composting and / or reuse of organic solid waste ; 10.4 Adequate disposal and treatment of liquid effluents (generated by crea...

    work page

  79. [79]

    Oliveira, Adriano Pereira, et al

    WORK SAFETY / MANAGEMENT OF THE USE OF AGROCHEMICALS AND VETERINARY PRODUCTS: 11.1 How many people handle agrochemicals and / or veterinary products ; 11.2 How Many People Use PPE Properly ; 11.3 Manuscript submitted to arXiv 40 Eugênio Fonseca, Evandro Caldeira, Heitor Ramos, Pierre Vilela, Leonardo B. Oliveira, Adriano Pereira, et al. Proper storage of ...

    work page