A
Aber, John, 76–77
academics on collaborative teams, 27–28
accountability: project leaders, 25–26; steps to ensure, 84; of team members, 83–84
accounting, natural resource. See natural resource accounting
agricultural systems: agroecology, 9–11, 13; concepts of, 9–11; definition of, 9, 12 (Box 1.1); emergent properties of, 11; existing, 43–45, 51–55; model of, 5 (Fig. I1); multifunctional, 3; nomenclature of, 12; processes, varieties of, 10; simulated, 43–51; structure and function relationship, 10; subsystems within, 10
agricultural systems research, 5–86; beginnings of, 11; carbon footprints use in, 73; collaborative culture, 6, 13, 27, 29, 31; design, 6; ecological footprints use in, 73; focus shift, 6, 13–17; goals of, 11; implementation, 7, 81–85; life cycle assessment, use in, 69; planning and revising, 38–57, 82; publishing results, 84–85; rewards of, 86; size of study plots, 16; sustainability and, 6; system design, 46–55; systems research approach, 7, 13; time frames, 16. See also experiments; project planning; systems research; team members, collaborative
agricultural systems research experiments: baseline data collection in, 55; control groups, 55; current projects, 44; financial planning, 55–57; spatial variability in, 55. See also experiments
agricultural systems research teams: collaborative teams, 16–19, 24–25; concept map, 30 (Fig. 2.3B); decision-making, 27, 31–32 (Fig. 2.4); meetings, 28, 33–34, 83–84; members of, 24–31; project leaders, 24–26, 42, 83, 84; selection of members, 26. See also team members, collaborative
agroecology: concepts of, 9–11; definition of, 13. See also agricultural systems
analysis: mathematical methods, 67–68; natural resource accounting methods, 68–73; statistical methods, 61–67; sustainability indicators, 74–75
analysis, statistical methods: multivariate approach, 62, 63–67; univariate approach, 62–63
analysis of variance (ANOVA), 62
aphids, 13–14
Asian ladybugs in aphid control, 14
authorship of research publications, 84–85
B
baseline data collection in agricultural systems research experiments, 55
biodegradable mulches, case study, 18
bradyrhizobia in aphid control, 14
Brock, Caroline, 58
budgets. See financial planning
C
canonical discriminant analysis (CDA), 66
canonical functions, 66
carbon cycle: concept map, 30 (Fig. 2.3A)
carbon footprints, 68, 73
case studies: biodegradable mulches, 18; cropping systems, 13, 35–36; dairy farms, 76–77; farmers in decision-making process, 35–36, 58; life cycle assessment model, 76–77; multivariate analysis, 63–67; organic dairy farm, 76–77; participatory decision-making, 35–36; potato production systems, 48; reductionist vs. systems approach, 13–14; as research methodology, 54–55; site selection, 53; soybean aphids, 13–14; spiderwort weed, 20, 21; stakeholders involvement, 20–21; systems vs. reductionist approach, 13–14; tomato plants and cover crops, 15–16, 35–36. See also examples
CDA (canonical discriminant analysis), 66
Center for Environmental Farming Systems (CEFS), 20–21
Cherry Research Farm, 20
chronosequence, definition of, 54
collaboration: culture, development of, 29, 31; of research teams, 26; stages of development, 28–29, 31
communication, 29, 31
compensation for team members, 28, 57, 83
compost usage over time: study of, 68
concept maps, 5, 13–14, 31, 42; examples of, 14 (Fig. 1.1), 30 (Fig. 2.3A and B), 40–41 (Fig. 3.2A, B, and C)
Constance, Doug, 18
control groups: in agricultural systems research experiments, 55
corn yield experiments, 62–63
correlation, 64–67
covariance statistical analysis, 63–67
cover crops and tomato plants: case study, 35–36
Creamer, Nancy, 20–21
crop rotation studies, 11, 13, 49
crop system research, 11, 13
crop yield experiments, 15–16, 35–36, 62–63, 63–67, 73
D
dairy farms, 13, 58, 76–77
data collection software, 71
Davis, Matt, 77
decision-making, 27, 31–32, 35–36
dendrogram, 67
disease control: experiments of, 63–67
E
ecological footprints, 68, 72–73
economists on collaborative teams, 17, 19. See also team members, collaborative
ecosystem services, 3
emergent properties of agricultural systems, 11
energy conservation: in experiments, 76–77
environmental impact: study methods, 71–75
EPA, 7
examples: collaboration of farmers and scientists, 15–17; concept maps, 14 (Fig. 1.1), 30 (Fig. 2.3A and B), 40–41 (Fig. 3.2A, B, and C); covariance statistical analysis, 63; existing agricultural systems use of, 43, 54–55; factorial design, 15; farm management, impact on crop systems, 47–48; hypotheses of agricultural systems research, 42; interdisciplinary research teams, 17–18, 40; life cycle assessment, 69–71; nitrate field management, 16–17; path analysis, 68; principal components analysis, 64–66; research study sites, 16; simulated agricultural systems use of, 43; site selection process, 53; structural equation modeling, 67; subjective boundaries, 10; system research approach, 15–16; time-series analysis, 16, 67; univariate statistical analysis, 62–63. See also case studies
existing agricultural systems: advantages and limitations of, 45; design considerations, 54–55; examples of use of, 43, 54–55; information gathering, 52 (Fig. 3.5); methodology related to research goals, 54–55; site selection, 51–53
experiments: compost use over time, 68; conventional farms, 63–67; corn yield, 62–63; crop yield, 15–16, 35–36, 62–63, 63–67, 73; disease control, 63–67; energy conservation, 76–77; existing agricultural systems use of, 43, 45, 51–52, 54–55; farm management methods use of, 47, 49–50; farm scale equipment use of, 49; insect control, 63–67; nitrate field management study, 17; organic farms, 63–67; plot size considered, 49; simulated agricultural systems use of, 43–51; soil health, 63–67, 75; soil nitrogen mineralization, 67; spatial variability, 55; split plots use of, 49, 50 (Fig. 3.4); subplots use of, 21, 50–51, 63; surface runoff, 76–77; time frames of, 16; tomato yield, 15–16, 35–36, 63–67, 73. See also agricultural systems research experiments; project planning
F
facilitators and facilitation, 32–33
factorial design, 14–15 (Box 1.2)
faculty on collaborative teams, 27–28
farm management methods: in experiments, 47, 49–50
farm scale equipment: in experiments, 49
farmer-led decision making model, 27
farmers: on collaborative teams, 26–28; decision making factors, 18, 35–36, 58; engagement of, 27–28; issues facing, 5; as research corroborators, 6, 13, 15–16, 35–36
farming system movement, 11, 13
farming systems, defined, 12
Farming Systems Project (FSP): example of univariate analysis, 62–63; split plot design used in, 50 (Fig. 3.4)
farming systems research, 11, 27
Farming Systems Trial, 13
farms, conventional vs. organic research, 64–67; site selection criteria, 54. See also agricultural systems
financial planning: in agricultural research systems experiments, 55–57; budget adjustments, 82–83; compensation for team members, 28, 57, 83; expense items to consider, 56; steps in, 56; timeline for, 55–56
food system map, 5 (Fig. I1)
FSP. See Farming Systems Project (FSP)
funding. See financial planning
funding sources, 7. See also SARE
G
goals of planning project: defining, 42, 70; methodologies and, 54–55
greenhouse gas emissions: carbon footprints as study method, 73
greenhouse tomato production: environmental impact, 73
H
hierarchical clustering, 67
hypotheses of agricultural systems research, 42
I
impact assessment of resources: during research projects, 71
implementation: of research projects, 7, 81–85
indicators, sustainability, 74–75
information transfer methods, 6 (Fig. I2)
insect control: experiments of, 63–67
integrated project design model, 29 (Fig. 2.2), 31
interactive decision making model, 27
interdisciplinary research, 7, 24
interdisciplinary research teams: examples of, 17–19, 40; vs. multidisciplinary research teams, 24–25
interpretation: of research results, 71
Inwood, Shoshanah, 18
Iowa State University (ISU), 27
J
Jordan, Jeff, 19
Judith Basin, Montana: farmers and scientists corroborative experiment, 17
K
knowledge transfer, 6 (Fig. I.2)
L
land use statistics, 73
life cycle assessment (LCA): agricultural systems research uses, 69; beginnings of, 68; in case study, 76–77; characteristics of, 68; examples of, 69, 70–71; methodology, 70–71; software for data collection, 71; strengths and limitations of, 71–72
loadings, 65
long-term experiments, 16, 51
M
maize. See corn yield experiments
management. See project leaders
mathematical methods, nonstatistical, 67–68
McDowell, Bill, 77
measurement of experiment results. See analysis
meetings, 28, 33–34, 83–84
methodologies in agricultural research experiments, 16, 54–55
milk production, 58, 76–77
Morrow Plots, 11
Mueller, Paul, 21
mulch research, 18
multidisciplinary research teams: vs. interdisciplinary research teams, 24–25; study of vegetable growers and mulches, 18
multivariate statistical analysis: challenges of, 63; example of, 63–67; time-series analysis, 67; types of, 63, 67; uses for, 62–63
N
National Research Council, 7
natural resource accounting: carbon footprints, 68, 73; ecological footprints, 68, 72–73; life-cycle assessment, 68–72
nitrate field management study, 16–17
nitrogen use efficiency: concept maps, 40 (Fig. 3.2A), 41(Fig. 3.2B and C)
North Carolina Department of Agriculture (NCDA), 20–21
O
objectives of research project, 42
organic agricultural systems: dairy, case study of, 76–77; tomato plant experiments, 63–67; vegetable cropping systems, 47–48
O’Sullivan, John, 21
P
parallel project design model, 29 (Fig. 2.2), 31
parasitoids, 16
Parker, Jason, 18
participatory decision making model, 27, 31–32; case study, 35–36
path analysis, 67–68
PCA, 63–66
PCs (statistical method), 64–65
planning. See project planning
plastic mulch and vegetable growers, 18
plot size in experiments, 49
Postharvest Research Team, University of Georgia, 17, 19
potato production systems case study, 48
poultry farms, 21
Practical Farmers of Iowa (PFI), 27
principal components (PCs), 64–65
principal components analysis (PCA), 63–66
project decision making models, 27
project design models, 29 (Fig. 2.2), 31
project leaders: accountability, 25–26; skills of, 25–26, 42, 83, 84
project planning, 38–57; analysis of research results, 61–75; budgets, adjusting of, 83; control groups, 55; defining problem, 39–40; experimental design, 42–43; farm management methods, impact on system design, 46–50; funding, 28, 55–57, 82–83; goals and objectives defined, 42; implementation of plan, 81–85; information gathering, 39; meetings, 28, 33–34, 83–84; methodologies of research, 54–55; publishing research results, 84–85; review and revise, 81–82; site selection, 43–46, 51–53; stages of, 38; system design, 46–55; time allotted, 82. See also experiments
properties, emergent, 11
publishing research, 84–85
R
reductionist research, 7, 11, 13–17
regrouping, 16–17, 24
research. See agricultural systems research; systems research
research sites. See existing agricultural systems; names of universities; simulated agricultural systems
research teams. See team members, collaborative
Rodale Institute, 13
Rominger, Bruce, 36
S
SAFS, 15–16, 35–36
Sanborn Field, 11
SARE: about, iv; farmers involvement, 13; funded projects, 13, 15, 20, 35–36, 44, 53, 58, 76–77; producer grants, 27; sociologists involvement, 18
satellite trials, 21, 50–51, 63
science-led decision making model, 27
scientists on collaborative teams, 27–28. See also team members, collaborative
shared variance, 64–67
simulated agricultural systems, 43, 45–51; advantages and limitations of, 45; design considerations of, 48–51; examples of use of, 43–44; long-term experiments, 51
site selection process, 53
Snapp, Sieglinde, 48
sociologists on collaborative teams, 17–18. See also team members, collaborative
software for data collection, 71
soil management, 16
soil nitrogen mineralization study, 67
soil organic matter (SOM), 16
soil surveys: experiments of, 64–66, 75
soybean aphids, 13–14
spatial variability, 55
spiderwort weed, 20, 21
split plot, 49, 50 (Fig. 3.4)
stakeholders, 6, 20–21, 26–27. See also team members, collaborative
statistical analysis of research results, 61–67
structural equation modeling (SEM), 67–68
subject specialists. See stakeholders; team members, collaborative
subplots, 21, 50–51, 63
surface runoff, 76–77
sustainability: dimensions of, 6; as emergent property, 11; funding requirement for, 7; mutifunctional agricultural systems and, 3; research movement, 13
sustainability indicators in analysis, 74–75
Sustainable Agriculture Farming Systems (SAFS), 15–16, 35–36
Sustainable Agriculture Research and Education (SARE). See SARE
syrphid flies in aphid control, 14
system boundaries identification, 10, 70
systems research: applying to agricultural systems research, 7, 13–17; background of, 11, 13; benefits of, 6–7; collaboration, 13; goal of, 11; methodologies of, 16; problems with, 7; vs. reductionist research, 7, 11, 13–17; regrouping, 16–17, 24; stakeholders, 6, 16–21, 24–28. See also agricultural systems research; analysis; experiments; project planning; team members, collaborative
T
team members, collaborative: accountability of, 83–84; benefits of, 24; challenges of, 24; collaboration development, 29, 31–32; commitment confirmed, 82; decision-making models for, 27; examples of collaboration, 16–17; faculty on, 27–28; farmers on, 26–28; integration of diverse viewpoints, 81; multidisciplinary vs. interdisciplinary approaches, 24–25; project leaders, roles and skills, 25–26, 42, 83, 84; reduced budgets, adjusting to, 83; selection of, 26, 84; stakeholders, 16–21, 24–28; team building, 28 (Fig. 2.1), 29–31
Temple, Steve, 35, 36
timeline of research project, 16, 82
time-series analysis, 16, 67
tomato production, greenhouse, 73
tomato yield experiments, 15–16, 35–36, 63–67, 73
Toward Sustainable Agricultural Systems in the 21st Century: (National Research Council), 7
transition effects, 45
turkey farms, 21
Type I errors: definition of, 63
U
United States Department of Agriculture, 3, 7
univariate statistical analysis, 62–63
University of California, Davis: case study, 35–36
University of Georgia Postharvest Research Team, 17, 19
University of Illinois, Urbana-Champaign: experiment sites, 11
University of Missouri: experiment sites, 11
University of New Hampshire: case study, 76–77
USDA, 3, 7
USDA-SCRI grant, 18
V
variables, statistical. See multivariate statistical analysis; univariate statistical analysis
vegetable growers, 18
W
whole farm system viewpoint, 6
writing research results, 84–85
Y
yield per acre statistics, 73