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School of Geography, Earth and Enviornmental Sciences, University of Birmingham, Birmingham, UK
Adriane Esquivel-Muelbert & Thomas A. M. Pugh

School of Geography, University of Leeds, Leeds, UK
Adriane Esquivel-Muelbert, Oliver L. Phillips, Roel J. W. Brienen, Martin J. P. Sullivan, Timothy R. Baker, Emanuel Gloor, Aurora Levesley, Simon L. Lewis, Karina Liana Lisboa Melgaço Ladvocat, Gabriela Lopez-Gonzalez, Nadir Pallqui Camacho, Julie Peacock, Georgia Pickavance & David Galbraith

Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
Adriane Esquivel-Muelbert & Thomas A. M. Pugh

School of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth, UK
Sophie Fauset

Department of Natural Sciences, Manchester Metropolitan University, Manchester, UK
Martin J. P. Sullivan

International Master Program of Agriculture, National Chung Hsing University, Taichung, Taiwan
Kuo-Jung Chao

Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK
Ted R. Feldpausch

Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil
Niro Higuchi, Adriano José Nogueira Lima & Carlos Quesada

School of Mathematics, University of Leeds, Leeds, UK
Jeanne Houwing-Duistermaat & Haiyan Liu

Faculty of Natural Sciences, Department of Life, Imperial College London Sciences, London, UK
Jon Lloyd

Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK
Yadvinder Malhi & Simone Matias de Almeida Reis

UNEMAT – Universidade do Estado de Mato Grosso PPG-Ecologia e Conservação, Campus de Nova Xavantina, Nova Xavantina, MT, Brazil
Beatriz Marimon, Ben Hur Marimon Junior, Paulo Morandi, Edmar Almeida de Oliveira & Simone Matias de Almeida Reis

Jardín Botánico de Missouri, Oxapampa, Peru
Abel Monteagudo-Mendoza, Victor Chama Moscoso, Luis Valenzuela Gamarra & Rodolfo Vasquez Martinez

Forest Ecology and Forest Management Group, Wageningen University and Research, Wageningen, Netherlands
Lourens Poorter, Frans Bongers, Marielos Peña-Claros & Pieter Zuidema

Centro de Ciências Biológicas e da Natureza, Universidade Federal do Acre, Rio Branco, AC, Brazil
Marcos Silveira

Instituto de Investigaciones para el Desarrollo Forestal (INDEFOR), Universidad de Los Andes, Mérida, Venezuela
Emilio Vilanova Torre & Julio Serrano

University of California, Berkeley, CA, USA
Emilio Vilanova Torre

Escuela de Ciencias Agropecuarias y Ambientales, Universidad Nacional Abierta y a Distancia, Boyacá, Colombia
Esteban Alvarez Dávila

Fundación ConVida, Medellín, Colombia
Esteban Alvarez Dávila

Instituto de Investigaciones de la Amazonia Peruana, Iquitos, Peru
Jhon del Aguila Pasquel, Gerardo A. Aymard C., Nallaret Davila Cardozo & Eurídice Honorio Coronado

Instituto de Biodiversidade e Florestas, Universidade Federal do Oeste do Pará, Santarém, Brazil
Everton Almeida

Center for Tropical Conservation, Nicholas School of the Environment, University in Durham, Durham, NC, USA
Patricia Alvarez Loayza

Projeto Dinâmica Biológica de Fragmentos, Instituto Nacional de Pesquisas da Amazônia Florestais, Manaus, AM, Brazil
Ana Andrade & José Luís Camargo

National Institute for Space Research (INPE), São José dos Campos, SP, Brazil
Luiz E. O. C. Aragão

Museo de Historia Natural Noel Kempff Mercado, Universidad Autónoma Gabriel Rene Moreno, Santa Cruz de la Sierra, Bolivia
Alejandro Araujo-Murakami & Marisol Toledo

Wageningen Environmental Research, Wageningen University and Research, Wageningen, Netherlands
Eric Arets

Dirección de la Carrera de Biología, Universidad Autónoma Gabriel René Moreno, Santa Cruz de la Sierra, Bolivia
Luzmila Arroyo

INRAE, UMR EcoFoG, CNRS, Cirad, AgroParisTech, Université des Antilles, Université de Guyane, Kourou, France
Michel Baisie, Damien Bonal, Benoit Burban, Aurélie Dourdain, Maxime Rejou-Machain & Clement Stahl

Department of Biological Sciences, International Center for Tropical Botany, Florida International University, Miami, FL, USA
Christopher Baraloto

Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, Piracicaba, Brazil
Plínio Barbosa Camargo

Universidade Federal do Acre, Campus Floresta, Cruzeiro do Sul, Brazil
Jorcely Barroso

UR Forest & Societies, CIRAD, Montpellier, France
Lilian Blanc

Department of Biology, Utrecht, Netherlands
René Boot

Woods Hole Research Center, Falmouth, MA, USA
Foster Brown

Laboratório de Botânica e Ecologia Vegetal, Universidade Federal do Acre, Rio Branco, AC, Brazil
Wendeson Castro

Laboratoire Evolution et Diversite Biologique, CNRS, Toulouse, France
Jerome Chave

Inventory and Monitoring Program, National Park Service, Fort Collins, CO, USA
James Comiskey

Proyecto Castaña, Madre de Dios, Peru
Fernando Cornejo Valverde

Instituto de Geociências, Faculdade de Meteorologia, Universidade Federal do Para, Belém, Brazil
Antonio Lola da Costa

Department of Anthropology and Primate Molecular Ecology and Evolution Laboratory, University of Texas, Austin, TX, USA
Anthony Di Fiore

National Museum of Natural History, Smithsonian Institute, Washington, DC, USA
Terry Erwin

Universidad Nacional Jorge Basadre de Grohmann, Tacna, Peru
Gerardo Flores Llampazo

Museu Paraense Emílio Goeldi, Belém, Brazil
Ima Célia Guimarães Vieira & Rafael Salomão

Instituto Venezolano de Investigaciones Científicas (IVIC), Caracas, Venezuela
Rafael Herrera

IIAMA, Universitat Politécnica de València, València, Spain
Rafael Herrera

Universidad Nacional de San Antonio Abad del Cusco, Cusco, Peru
Isau Huamantupa-Chuquimaco

Instituto Amazónico de Investigaciones Imani, Universidad Nacional de Colombia Sede Amazonia, Leticia, Colombia
Eliana Jimenez-Rojas

Agteca, Santa Cruz, Bolivia
Timothy Killeen

College of Science and Engineering, James Cook University, Cairns, QLD, Australia
Susan Laurance & William Laurance

Department of Geography, University College London, London, UK
Simon L. Lewis

Environmental Science and Policy, George Mason University, Fairfax, VA, USA
Thomas Lovejoy

Research School of Biology, Australian National University, Canberra, ACT, Australia
Patrick Meir

School of Geosciences, University of Edinburgh, Edinburgh, UK
Patrick Meir

Escuela de Ciencias Forestales, Unidad Académica del Trópico, Universidad Mayor de San Simón, Cochabamba, Bolivia
Casimiro Mendoza

Facultad de Ingeniería Ambiental, Universidad Estatal Amazónica, Puyo, Ecuador
David Neill

Universidad Nacional de San Antonio Abad del Cusco, Cusco, Perú
Percy Nuñez Vargas, Nadir Pallqui Camacho & Javier Silva Espejo

Universidad Autónoma del Beni José Ballivián, Trinidad, Bolivia
Guido Pardo & Vincent Vos

Universidad Regional Amazónica Ikiam, Ikiam, Ecuador
Maria Cristina Peñuela-Mora

Broward County Parks Recreation, Oakland Park, FL, USA
John Pipoly

Keller Science Action Center, Field Museum, Chicago, IL, USA
Nigel Pitman

Instituto de Ciencias Naturales, Universidad Nacional de Colombia, Bogotá, Colombia
Adriana Prieto & Agustín Rudas

Institute of Research for Forestry Development (INDEFOR), Universidad de los Andes, Mérida, Venezuela
Hirma Ramirez-Angulo

Socioecosistemas y Cambio Climatico, Fundacion Con Vida, Medellín, Colombia
Zorayda Restrepo Correa

Centro de Conservacion, Investigacion y Manejo de Areas Naturales, CIMA Cordillera Azul, Lima, Peru
Lily Rodriguez Bayona

Universidade Federal Rural da Amazônia, Belém, Brazil
Rafael Salomão & Natalino Silva

Departamento de Biología, Universidad de La Serena, La Serena, Chile
Javier Silva Espejo

Guyana Forestry Commission, Georgetown, Guyana
James Singh

Federal University of Alagoas, Maceió, Brazil
Juliana Stropp

Institute for Conservation Research, Escondido, CA, USA
Varun Swamy

Institute for Transport Studies, University of Leeds, Leeds, UK
Joey Talbot

Biodiversity Dynamics, Naturalis Biodiversity Center, Leiden, The Netherlands
Hans ter Steege

Systems Ecology, Free University, De Boelelaan 1087, Amsterdam, Netherlands
Hans ter Steege

Department of Biology, University of Florida, Gainesville, FL, USA
John Terborgh

Iwokrama International Centre for Rainforest Conservation and Development, Georgetown, Guyana
Raquel Thomas

Universidad de los Andes, Mérida, Venezuela
Armando Torres-Lezama

School of Geography, University of Nottingham, Nottingham, UK
Geertje van der Heijden

Van Hall Larenstein University of Applied Sciences, Leeuwarden, Netherlands
Peter van der Meer

Van der Hoult Forestry Consulting, Leeuwarden, The Netherlands
Peter van der Hout

Núcleo de Estudos e Pesquisas Ambientais – Universidade Estadual de Campinas, Campinas, Brazil
Simone Aparecida Vieira

Herbario del Sur de Bolivia, Universidad de San Francisco Xavier de Chuquisaca, Sucre, Bolivia
Jeanneth Villalobos Cayo

Tropenbos International, Wageningen, Netherlands
Roderick Zagt

A.E.-M. and D.G. designed the study with contributions from O.L.P., R.J.W.B., S.F. and M.J.P.S. A.E.-M. carried out the analyses with inputs from D.G., O.L.P., R.J.W.B., S.F., M.J.P.S., J.H.-.D. and H.L. A.E.-M. wrote a first draft with contributions from D.G., M.J.P.S., T.A.M.P., S.F. and O.L.P. O.L.P., R.J.W.B., S.F., M.J.P.S., T.R.B., K.-J.C., T.R.F., N.H., Y.M., B.M., B.H.M.J., A.M.-M., L.P., M.S., E.V.T., E.A.D., J.d.A.P., E.A., P.A.L., A.A., L.E.O.CA., A.A.-M., E.Arets, L.A., G.A.A.C., M.B., C.B., P.B.C., J.B., L.B., D.B., F.B., R.J.W.B., F.Brown, B.B., J.L.C., W.C., V.C.M., J.C., J.Comiskey, F.C.V., A.L.d.C., N.D.C., A.D.F., A.D., T.E., G.F.L., I.C.G.V., R.H., E.H.C., I.H.-C., E.J.-R., T.K., S.L., W.L., S.L.L., T.L., P.M., C.M., P.Morandi, D.N., A.J.N.L., P.N.V., E.A.d.O., N.P.C., G.Prado, J.Pipoly, M.P.-C., M.C.P.-M., N.P., A.P., C.Q., H.R.-A., S.M.d.A.R., M.R.-M., Z.R.C., L.R.B., A.R., R.S., J.S., J.S.E., N.S., J.Singh, C.S., J.Stroop, V.S., J.T., H.t.S., J.T., R.T., M.T., A.T.-L., L.V.G., G.v.d.H., P.v.d.M., P.v.d.H., R.V.M., S.A.V., J.V.C., V.V., R.Z. and P.Z. led field expeditions for data collection. O.L.P., J.L. and Y.M. conceived the RAINFOR forest plot network; D.G., E.G. and T.R.B. contributed to its development. O.L.P., R.J.W.B., T.R.F., T.R.B., A.M.‐M., L.E.O.C.A., E.A.D., B.M., B.H.M.J., N.H., E.V.T., J.C., E.G. and Y.M. coordinated data collection with the help of many co‐authors. O.L.P., T.R.B., S.L.L. and G.L.-G. conceived ForestPlots.net, and M.J.P.S., A.L., J.Peacock, G.P., K.L.L.M.L., D.G. and E.G. helped to develop it. All authors read and approved the manuscript (with important insights provided by O.L.P., L.P., H.t.S., T.E., W.C., S.M.d.A.R., E.G., E.A.d.O., P.M., M.J.P.S., D.B., G.v.d.H. and P.Z.). More

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Weather
All the weather parameters measured during the study period were similar to the long-term averages (Fig. S1). During the study period (2014–2018), crops received an average annual rainfall of 763 mm, although its distribution was quite different amongst the rainy season (June–September) (Fig. S1). Rice/maize season in 2014, 2015, and 2016, 2017 received 485 (256 mm in September), 420 (255 mm in July), 533 (284 mm in August), and 695 mm (247 mm in June and 226 mm in September) of rainfall, respectively. In 1st year, the wheat crop receivedrainfall of 247 mm whereas in the 2nd, 3rd, and 4th years it was only 56, 96 and 78 mm, respectively.
Crops and system productivity
The management practices under different rice/maize-based scenarios influenced the crop grain yields over the 4-years (2014–2017) (Table 1). Scenarios with rice crops (Sc1-Sc3) did not differ in rice yields during the year 2014 and 2017, but CT direct seeded rice (Sc2) in the 2nd year (2015) and ZT direct seeded rice (Sc3) in the 3rd year (2016) produced 0.9 Mg ha−1 higher and 1.1 Mg ha−1 lower yield than farmers’ practice (Sc1), respectively (Table 1). Rice equivalent maize yields in CA-based scenarios (Sc6-Sc7) did not differ from scenarios with rice crops (Sc1-Sc3) in any of the study years. Rice equivalent maize yield of CA-based Sc5 with maize on PB, although was similar to Sc1 in all the years but was 1.41 Mg ha−1 lower than ZT-DSR (Sc3) in 1st year and 0.98 Mg ha−1 lower than CT-DSR (Sc2) in 2nd year. In contrast, rice equivalent yield (REY) of Sc4 with maize on fresh beds (FB) produced lower yields than one of the rice-based scenarios in three out of four years. These results suggest that maize performs better under CA-based management system than under conventional tillage system. Almost 5% higher yield of maize was recorded in the 1st year and 12–16% higher in the last three years under CA-based scenario (Sc7) compared to CT-based scenario (Sc4) and at par with Sc5. Based on the 4-years average, rice equivalent yield (REY) of Sc4 (maize on FB ) was 0.8 Mg ha−1 (12%) lower than Sc1 (business-as-usual) whereas other scenarios did not differ from each other in REY (Table 1).
Table 1 Effect of different scenarios on grain yields (Mg ha−1) of rice, maize, wheat and systems during 4-years (2014–2018).
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The management practices influenced wheat grain yield over the years of experimentation (Table 1). Across study years, the grain yield of ZT wheat in CA-based scenario was either similar or higher than CT wheat. Results showed significantly (P  Sc2 = Sc3 (1753–1759 mm ha−1)  > S7 = Sc6 = Sc4 = S5 (289–365 mm ha−1) (Table 2). The same trend followed in all the study years except in the 4th year, where irrigation water input in Sc5 (maize on PB) was 109–154 mm ha−1 (22–28%) lower than Sc6 and Sc7 (ZT maize on flat beds). The amount of water applied in CT-based rice crop (Sc1; farmers’ practice) was significantly (P  Sc2 = Sc3  > Sc7  > Sc4-Sc6.
Higher grain yield and low water use led to significantly (P  Sc4 (2.25 kg grain m−3)  > Sc7 (2.15 kg grain m−3)  > Sc6 (2.06 kg grain m−3), respectively compared to Sc1 (0.30 kg grain m−3) (Table 2). In wheat, CA-based management practices increased WPI by 9% (4-years’ mean) compared to Sc1 (1.21 kg grain m−3). CA-based management practices improved mean WPI by 23 and 438% in RW and MW system, respectively compared to Sc1 (0.42 kg grain m−3).
Figure 3

Effect of different scenarios on irrigation water productivity (kg grain m−3) of rice, maize, wheat and systems during 4-years (2014–2018).

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Energy use efficiency
Energy equivalents for different agricultural operations used in the study are given in Table S2. The energy input and output (Tables S3 and S4), and energy use efficiency (EUE) of rice, maize, wheat and mungbean were influenced by the management practices and varied from year to year (Fig. 4). During rice/maize, higher EUE was observed in maize based scenarios (Sc4-Sc7) than in rice-based scenarios (Sc1-Sc3) (10.81–13.83 MJ MJ−1 versus 3.95–4.85 MJ MJ−1) (Table 2). Rice-based scenarios (Sc1-Sc3) did not differ in EUE. However, in maize-based scenarios (Sc4-Sc7), EUE of CA-based maize scenarios (Sc5-Sc7) was 17–28% higher than CT-based maize Sc4. Across years also, the same trend was observed with no difference in EUE of rice-based scenarios (Sc1-Sc3), whereas CA-based maize scenarios (Sc5-Sc7) had higher EUE than CT-based Sc4 (Table 2). In wheat crop, highest EUE was observed under CA-based scenarios (Sc2-Sc3 and Sc5-Sc7) compared to CT-based scenarios (Sc1 and Sc4) across all study years and based on four years’ average (9.26–10.05 MJ MJ−1 versus 7.44–7.84 MJ MJ−1), it is indicated that CA-based scenarios are more energy-efficient than those of CT-based scenarios (Fig. 4). In all the years, EUE of maize-based scenarios (Sc4-Sc7) were higher than rice-based scenarios (Sc1-Sc3) but within rice-based scenarios (Sc1-Sc3), results were more variable with higher EUE of CA-based Sc2 and Sc3 in 1st and 2nd year than CT-based scenarios (Sc1) but did not differ in 3rd and 4th year (Fig. 4). On system basis, the EUE of different scenarios decreased in the following order: Sc5 (11.92 MJ MJ−1)  > Sc6 = Sc7 (10.26–10.95 MJ MJ−1)  > Sc4 (9.25 MJ MJ−1)  > Sc3 = Sc2 (6.23–6.25 MJ MJ−1)  > Sc1 (5.05 MJ MJ−1) (Table 2). Maize-based scenarios (Sc5-Sc7) had 48 to 136% higher EUE than rice-based scenarios (Sc1-Sc3) suggesting maize-wheat based cropping systems were more efficient in energy use than rice–wheat based systems (Table 2). Scenario 3 (+ 24%) in RW and Sc5 (+ 136%) in MW system were the most energy-efficient among the different combinations of management practices in 4-years of study.
Figure 4

Effect of different scenarios on energy use efficiency of rice, maize, wheat and systems during 4-years (2014–2018).

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Methane (CH4) and nitrous oxide (N2O) emission from soil
Methane (CH4) was emitted only from the rice plots (Table 3). The estimated mean value of CH4 emission (kg CO2 eq. ha−1) was 39% lower in CA-based rice scenarios without continuous flooding (Sc2 and Sc3) compared to CT-based Sc1 with continuous flooding for  > 1 month (Table 3).
Table 3 Effect of different scenarios on GHGs emissions, C-sequestration and GWP of rice, maize, wheat and systems (based on 4-year average, 2014–18).
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N2O emission varied from 7 to 583 kg CO2 eq. ha−1 during the rice season (Table 3). The maximum amount of N2O emission (580–583 kg CO2 eq. ha−1) was observed in CA-based rice scenarios (Sc2-Sc3) followed by the maize-based scenarios (50–61 kg CO2 eq. ha−1) and was the lowest in CT-based rice Sc1 (7 kg CO2 eq. ha−1). The CA-based rice and maize scenarios produced 88 and 9 times higher N2O emission compared to Sc1, respectively. The N2O emission in the wheat season ranged between 50 to 102 kg CO2 eq. ha−1 (Table 3). The highest N2O emission was estimated with CA-based scenarios (Sc2-Sc3) (101–102 kg CO2 eq. ha−1) and followed by scenarios Sc5-Sc7 (72–73 kg CO2 eq. ha−1) and was lowest in CT-based scenarios Sc1 and Sc4 (50 kg CO2 eq. ha−1). The N2O emission in the wheat crop was increased by 57% under CA-based management scenarios compared to CT-based management scenario (Table 3). On system basis, CA-based rice and maize systems emitted 12 and 2.4 times more N2O compared to Sc1, respectively but methane emission was reduced to zero (Table 3). Overall CA-based cereal management systems emitted almost six-time higher N2O emission compared to farmers’ practice, irrespective of cropping systems (Table 3).
GHG emission associated with residue burning (kg CO2 eq. ha−1)
Crop residue burning is a common farmers’ practice in the western IGP. Therefore, GHG emission due to residue burning (kg CO2 eq. ha−1) was estimated with CT-based system of rice (Sc1; 278 kg CO2 eq. ha−1) and maize (Sc4; 69 kg CO2 eq. ha−1) cultivation (Table 3). In the case of wheat, the GHG emission due to residue burning (kg CO2 eq. ha−1) was estimated with CT-based cultivation of wheat in Sc1 (59 kg CO2 eq. ha−1) and Sc4 (40 kg CO2 eq. ha−1). No GHG emission (kg CO2 eq. ha−1) was considered due to burning where crop residues were retained/incorporated in CA-based management practices under different scenarios.
GHG emission due to energy consumption (kg CO2 eq. ha−1)
GHG emission due to energy consumption varied from 2414 to 2941, 1005 to 1126 and 1122 to 1299 kg CO2 eq. ha−1 in rice, maize, and wheat, respectively (Table 3). Compared to CA-based management scenarios, CT-based scenarios emitted more GHGs due to the higher consumption of electricity and diesel energy in all the crops and cropping systems. Compared to Sc1, GHG emission due to energy consumption from rice/maize season was 16–18% lower in CA-based rice scenarios (Sc2-Sc3) and 63–66% lower in maize-based scenarios (Sc4-Sc7) (Table 3). Overall, compared to Sc1, CA-based scenarios reduced ~ 17 and 63% of GHG emissions due to energy consumption in rice and maize across the years, respectively. Similarly, in wheat, CA-based scenarios (Sc2-Sc3 and Sc5-Sc7) reduced 10% GHG emission due to energy consumptions as compared to CT-based scenarios (Sc1 and Sc4). On the system basis, Sc2, Sc3, Sc4, Sc5, Sc6, and Sc7 recorded lower energy-related emission of GHG by 14, 15, 43, 50, 46, and 43% (4-years’ mean), respectively, relative to Sc1 (4240 kg CO2 eq. ha−1) (Table 3). Rice and maize-based systems recorded ~ 15 and 46% lower GHG related emissions, respectively compared to farmers’ practice (Sc1-4240 kg CO2 eq. ha−1).
Carbon (C) sequestration
The estimated C-sequestration was carried out in those scenarios where crop residues were retained/ incorporated during the study period. The C-sequestration varied with the amount of crop residue was recycled under different crops and cropping systems. Estimated C-sequestration in soil varied from 0 to − 625 kg CO2 eq. ha−1 in rice, 0 to − 908 CO2 eq. ha−1 in maize and 0 to − 1821 kg CO2 eq. ha−1 in wheat (Table 3). On system basis, the highest C-sequestration was estimated under CA-based management scenarios which varied in the following order of Sc7 (3039 kg CO2 eq. ha−1)  > Sc3 (2446 kg CO2 eq. ha−1)  > Sc2 (2086 kg CO2 ha−1)  > Sc6 (2070 kg CO2 eq. ha−1).
Total global warming potential (GWP)
Global warming potential (GWP) varied with crop management practices under different scenarios over the years. In 4-year, the total estimated GWP from rice was lower under the CA-based systems than CT-based system. On 4-year mean basis, the GWP under the CA-based rice (Sc2-Sc3) and maize (Sc5-Sc7) systems were lowered by ~ 28 and 90% compared to farmers’ practice (Sc1), respectively (Table 3). Within maize-based scenarios, the CA-based scenarios (Sc5-Sc7) reduced the GWP of maize by 77–83% compared to CT-based Sc4. The GWP in wheat varied from − 384 to 1409 kg CO2 eq. ha−1 based on 4 year average (Table 3). The 4 years mean GWP was significantly lower by 127–138% in CA-based RW system (Sc2-Sc3) and 96–99% in CA-based MW system (Sc5-Sc7) compared to Sc1, respectively (Table 3). The mean GWP of wheat under CT-based RW system (Sc1) was similar to CT-based MW (Sc1and Sc4) systems.
The crop management practices under different scenarios influenced the total GWP (CO2 eq. ha−1) in both the cropping systems (RW and MW system) during the study years (Table 3). On 4-years system mean basis, GWP under Sc2, Sc3, Sc4, Sc5, Sc6, and Sc7 were 48, 54, 59, 96, 95, and 107% lower compared to Sc1 (farmers’ practice), respectively. In CA-based RW and MW systems, GWP was estimated lower by 50 and 89% compared to CT-based Sc1(6451 kg CO2 eq. ha−1), respectively. More

Summary statistics
To date, The Connecticut Agricultural Experiment Station (CAES) has collected and tested 4,602,240 female mosquitoes comprised of 47 species in 8 genera. Approximately 98% of these collections were obtained from 92 trapping sites in 73 towns throughout the state, while the remainder of collections were from an additional 365 supplemental sites sampled between 1996 and 2007. Eighty-eight percent of collections come from CDC Light Traps, CDC Gravid Traps and Biogents BG Sentinel Traps (beginning in 2012). There have been several other collection methods used throughout the years that account for 11.6% of the mosquitoes collected (S. Table 1). Overall, there was considerable variation in mosquito abundance, surveillance effort, species richness/evenness, and the proportion of single species detections across CT (Fig. 1). One clear trend was that surveillance effort was greatest in CT’s human population centers (predominately CT’s southwestern and central counties) where WNV is commonly detected and along the CT-Rhode Island border where EEEV is most commonly detected (Fig. 1A). Another noticeable visual trend was that species evenness tends to be higher in the eastern portion of CT (Fig. 1B).
Figure 1

Maps of total mosquito abundance (log10 transformed) (A), total number of trap nights (A), average annual mosquito species richness (B), average annual mosquito species evenness (B), and average annual prevalence of single species detections (C) across 87 mosquito surveillance sites throughout Connecticut, U.S. sampled with ground level CDC CO2-baited light traps from 2001 to 2019. (A) Point sizes represent abundance while colors represent trap-nights; (B) point sizes represent species richness while colors represent species evenness; (C) point sizes represent prevalence of single species detections. (A–C) Solid black lines represent county political boundaries. The figure was created in R V 3.6.3 using the following packages: ggplot2 and maps.

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Objective 1: annual collections of mosquito populations among sites
Our first objective was to identify spatial and temporal linear and nonlinear trends in mosquito abundance among sites. We also examined coarse-scale correlations between statewide (i.e., annual) and site-wide abundance and weather and land classification variables. All regression results and tables are provided as supporting information in Supporting Information: Regression Tables.
Mosquito abundance
Temporal regressions
After accounting for trapping effort, regression parameters estimating the relationship between site-level mosquito abundance and year of collection were positive using generalized linear mixed effects models (GLMMs) (“Year”—Estimate 0.03, t-value 9.11) and generalized additive mixed effects models (GAMMs) (“Year”—Est. 0.77, t-value 2.7, p = 0.007), suggesting that site-level mosquito abundance has increased in CT since 2001 (Fig. 2A,B): this trend resulted in a predicted 60% increase in annual abundance from 2001 to 2019. While these regressions identified possible increasing trends in site-level abundance, they provided an overall poor-fit to the data: AIC scores from fixed effect GLMMs were higher than random effects-only models (ΔAIC 415.1). This poor model fit may be in part driven by directly modeling Year as a fixed continuous effect; Year as a random categorical effect may better capture variation in mosquito collections30. Despite large differences in AIC scores between fixed and random effects-only models, we detected a pattern of increasing intercept values when examining “Year” as a random effect (S. Fig. 1), providing further evidence of an increasing temporal trend in site-level mosquito abundance.
Figure 2

Average annual mosquito abundance (A), number of trap nights (B), mosquito species richness (C), mosquito species evenness (D), the annual correlation between mosquito species richness and evenness (E), and the prevalence of single mosquito species detections (F) across 87 mosquito surveillance sites throughout Connecticut, U.S. sampled with ground level CDC CO2-baited light traps from 2001 – 2019. For (A)–(D) and (F), points represent the average across all sites, solid lines represent the standard error of the average, and dashed lines are added to aid interpreting each plot as a time series. For (E), points represent the average across all sites while solid lines represent the 95% CI of the correlation point estimate. The figure was created in R V 3.6.3 using base functions.

Full size image

Spatial regressions
After accounting for trapping effort, regression parameters estimating the relationship between site-level mosquito abundance and latitude/longitude were positive using a GLMM (“Latitude (centered)”—Est. 0.49, t-value 5.48; Longitude (centered)”—Est. 0.20, t-value 4.78), indicating that mosquito abundance tends to increase on a south to north and west to east gradient (which reflects the overall transition in land cover from developed to forested in CT). The best fitting fixed effect GAMM included Longitude by Latitude smoothing terms, which also predicted positive relationships between abundance and site coordinates (Smoothing term 1: Est. 0.24, p = 0.06; Smoothing term 2: Est. 0.05, p = 0.67). GAMM predictions of site-level mosquito abundance were considerably more complex than GLMM predictions, yet still supported the overall trend of increasing abundance from south to north and west to east (S. Fig. 2). Overall, the fixed effect GLMMs provided an extremely poor fit to the data compared to random effects-only GLMMs (Latitude—ΔAIC 1092.7; Longitude—ΔAIC 1099.8). These poor model fits may be in part driven by directly modeling coordinate (i.e., site) as a fixed continuous effect: GAMM predictions that account for nonlinear relationships between abundance and spatial location may provide a more appropriate fit to the data while site as a categorical random effect in the GLMMs may better capture variation in mosquito collections30.
Weather correlations
When comparing statewide annual mosquito abundance to weather variables, we found no correlations between summer temperatures, spring temperatures or precipitation. This was despite detecting a slight annual increase in temperatures across all three seasons examined (average daily temperature GLMM Est., Season/Summer: 0.05 °C, Prior Spring: 0.02 °C, Prior Winter: 0.07 °C) and a slight annual decline in within season and prior spring precipitation (total precipitation GLMM Est., Season/Summer: − 4.23 mm, Prior Spring: − 3.38 mm; Prior Winter: 2.22 mm) in CT since 2001. However, we did find a positive correlation between total summer precipitation and annual statewide mosquito abundance (r = 0.50, CI 0.07–0.78).
Land cover correlations
When comparing total site-wide abundance to land cover classifications, we found positive correlations between percent land cover categorized as barren (r = 0.22, CI 0.01–0.41), forested wetland (r = 0.34, 0.14–0.52), and non-forested wetland (r = 0.21, 0.004–0.41). We also found a negative association in total site-level abundance and percent land cover categorized as grass (r = − 0.35, − 0.52 to − 0.15).
Species richness
Temporal regressions
After accounting for trapping effort, regression parameters estimating the relationship between site-level species richness and year of collection were positive using both GLMMs (“Year (centered)”—Est. 0.10, t-value 9.46) and GAMMs (“Year”—Est. 1.78, t-value 1.93, p = 0.05) (Fig. 2C): this trend resulted in a predicted 10% increase in site-level species richness from 2001 to 2019. Overall, fixed effects GLMMs of species richness provided an overall poor fit to the data when compared to a random effects-only model (ΔAIC 319.37). However, we did observe a pattern of increasing intercept values when examining “Year” as a random effect (S. Fig. 3), further indicating that mosquito species richness has annually increased across sites in CT since 2001.
Spatial regressions
Similar to models of site-level mosquito abundance, GLMMs of species richness by coordinate predicted positive relationships (Latitude (centered): Est. 0.63, t-value = 2.11; Longitude (centered): Est. 1.26, t-value = 9.34), indicating the species richness tends to increase along a south to north, west to east gradient. The best fitting GAMM included Longitude by Latitude smoothing terms, which also predicted positive relationships between species richness and site coordinate (Smoothing term 1: Est. 1.45, p = 0.0001; Smoothing term 2: Est. 0.70, p = 0.05). The GAMM further predicted a complex relationship of species richness among sites, yet overall predicted richness was lowest in the southwest/central portions of CT (areas of greatest development) and highest along coastal/eastern portions of CT (areas of non-forested and forested wetlands) (S. Fig. 4). The fixed effect GLMMs provided very poor fits to the data compared with random effects-only models (Latitude: ΔAIC 953.01; Longitude: ΔAIC 871.93; see the above results for Site-level collections: spatial regressions for possible reasons for these poor fits).
Weather correlations
We found no correlations of note between mosquito species richness and seasonal temperatures and precipitation.
Land cover correlations
Positive correlations of note for site-level species richness included: coniferous forest (r = 0.25, 0.04–0.43), deciduous forest (r = 0.56, 0.40–0.69), and forested wetland (r = 0.43, 0.23–0.58). Negative correlations included: barren (r = − 0.30, − 0.48 to − 0.10), developed (r = − 0.66, − 0.77 to − 0.53), grass (r = − 0.24, − 0.43 to − 0.03), and open water (r = − 0.31, − 0.49 to − 0.11).
Species evenness
Temporal regressions
Trends in species evenness were negative using both GLMMs (“Year”—Est. − 0.01, t-value − 7.86) and GAMMs (“Year (centered)”—Est. − 0.04, t-value − 5.58, p = 0.000) (Fig. 2D): this trend resulted in a predicted 12% decrease in site-level species evenness from 2001 to 2019. Similar to fixed effects GLMMs of species richness, fixed effects GLMMs of species evenness were less informative than a random effects-only model (ΔAIC 66.5). Declining intercept values were evident when evaluating “Year” as a random effect (S. Fig. 5), further supporting an overall annual decline in species evenness estimates among sites.
Spatial regressions
Similar to spatial models of species richness, GLMMs predicted positive relationships between species evenness and coordinate (Latitude (centered): Est. 0.36, t-value = 7.63; Longitude (centered): Est. 0.18, t-value = 8.54); the best fitting GAMM, which included Longitude by Latitude smoothing terms, also predicted positive relationships (Smoothing term 1: Est. 0.12, p = 0.01; Smoothing term 2: Est. 0.16, p = 0.004). GAMM predictions of site-level species evenness were equally complex to predictions of abundance and richness, and predicted evenness to be highest in southcentral and eastern CT (S. Fig. 6). Fixed effect GLMMs provided very poor fits to the data compared with random effects-only models (Latitude: ΔAIC 502.6; Longitude: ΔAIC 488.4; see the above results for Site-level collections: spatial regressions for possible reasons for these poor fits).
Weather correlations
We did find a negative correlation between statewide prior spring minimum temperatures and mosquito species evenness (r = − 0.49, − 0.77 to − 0.04).
Land cover correlations
Positive correlations of note for species evenness included: deciduous forest (r = 0.46, 0.28–0.61) and forested wetland (r = 0.22, 0.01–0.41). Negative correlations included: barren (r = − 0.37, − 0.54 to − 0.18), developed (r = − 0.45, − 0.60 to − 0.26), and open water (r = − 0.32, − 0.50 to − 0.12).
Correlations between abundance, richness, and evenness
The relationships between abundance, richness, and evenness varied depending on the scale examined. Across all years of data at the site-level, the correlation between abundance and richness was positive (r = 0.53, 0.36–0.67), the correlation between abundance and evenness as negative (r = − 0.35, − 0.52 to − 0.15), and there was no correlation of note between richness and evenness. Across all sites at the year-level, there were no correlations of note between abundance, richness, and evenness. Annual statewide correlations between richness and evenness (RRE) were positive for all years yet there was no noticeable annual trend in these correlations (Fig. 2E). Spatially, the average site-level RRE was 0.15 (± 0.03 SE). Furthermore, the magnitude and direction of RRE tended to increase on a south to north gradient (r = 0.31, 0.11–0.49), yet there was no apparent relationship in RRE along a west to east gradient (S. Fig. 7). We did detect a positive correlation between RRE and average maximum spring temperatures (r = 0.46, 0.01–0.76) as well as a positive correlation between RRE and percent land cover classified as coniferous forest (r = 0.23, 0.02–0.42).
Single detection events
Single detection events were defined as the prevalence of single species detections at a site (i.e., number of species with a single pool divided by species richness). Changes in single species detections could indirectly indicate range expansion among species (i.e., the prevalence of single detections decreases with time) and/or areas of unique mosquito diversity (i.e., the prevalence of single detections changes across space).
Temporal regressions
We detected no overall pattern of increasing/decreasing annual prevalence of single-species detections among sites (GLMM, “Year”—Est. − 0.13, t-value = − 1.12, p = 0.22; GAMM, “Year”—Est. 0.02, t value = − 0.31, p = 0.75) (Fig. 2F). These models were considered equivalent to a random effects-only GLMM (ΔAIC  More

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