Insufficient geographic characterization and analysis in the planning, execution and dissemination of agronomic research?

Field Crops Research 76 (2002) 45±54

Insuf®cient geographic characterization and analysis in the planning, execution and dissemination of agronomic research? Jeffrey W. Whitea,*, John D. Corbettb, Achim Dobermannc a

CIMMYT, Apdo. Postal 6-641, 06600 Mexico, D.F., Mexico Mud Springs Geographers Inc., 18 So. Main Street, Suite 718, Temple, TX 76501, USA c Department of Agronomy and Horticulture, University of Nebraska, P.O. Box 830915, Lincoln, NE 68583-0915, USA b

Received 9 September 2001; received in revised form 26 February 2002; accepted 1 March 2002

Abstract Understanding spatial variation in crop response to environment and management is an essential component of agronomic research. Given the increasing availability of geographic information systems (GISs) and spatial data, one might anticipate widespread use of maps, reference to geographic variation, and analyses that capitalize on the power of GIS. Such use would be re¯ected in improved selection of research sites or treatments, better understanding of the in¯uence of climatic or edaphic factors on crop responses, and more easily interpretable, quantitative results presented through maps. With the notable exception of research on individual ®elds or local landscapes, however, published research indicated insuf®cient use of spatial information and analyses or even explicit consideration of the geographical context of research. To assess use of geographic information in agronomic research, we examined papers in ®ve prominent agricultural journals for evidence of analyses at a spatial scale smaller than ®eld plots or local landscapes but larger than national or continental levels, which we term ``mesoscale''. Use of simulation models was also examined since models can quantify response to environmental factors and thus, their use might provide instructive comparisons with use of spatial analyses. Of 250 papers considered, less than half (119 papers) described the geographic context of the research, and only 90 gave geographic coordinates with suf®cient precision to locate sites within a 10 km radius. Only six papers included maps of the study area. Over 150 papers used single locations, and just 26 papers, more than four sites. In experiments creating variation in soil conditions, such as through irrigation, tillage, or nutrient treatments, the limited number of treatment combinations appeared to constrain quantitative interpretation of results. Most papers involving simulation models focused on model development or validation; models were seldom used to analyze effects of climatic or edaphic conditions as a complement to other lines of research. We conclude that there is need to increase the geographic relevance of agronomic research. Suggested steps include selecting sites using an explicit sampling strategy, using larger numbers of sites, and analyzing and presenting results using tools such as GIS and crop or ecosystem simulation models. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Adaptation; Field experimentation; Geographic information systems; Simulation models; Spatial analysis

1. Introduction *

Corresponding author. Tel.: ‡52-55-5804-2004; fax: ‡52-55-5804-7558. E-mail address: [email protected] (J.W. White).

Climate and soil are primary determinants of crop production (Loomis and Connor, 1992). These factors

0378-4290/02/$ ± see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 4 2 9 0 ( 0 2 ) 0 0 0 4 1 - 2

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J.W. White et al. / Field Crops Research 76 (2002) 45±54

can be viewed as de®ning a spectrum of agroecological zones for which potential production scenarios are subsequently narrowed by biotic, socioeconomic, and policy constraints. Most agricultural researchers acknowledge the importance of spatial and temporal variation in the environment. Terms such as ``sitespeci®c'', ``genotype environment interaction'', and ``scaling up'' are indicative of these concerns. The increasing availability of tools for spatial analysis, especially geographic information systems (Burrough and McDonnell, 1998; Johnston et al., 2001), offer researchers opportunities for improving analyses of spatial variation inherent to agronomic research. Bene®ts might include, improved selection of research sites or treatments, more quantitative assessments of the impact of climatic and edaphic factors, and better appreciation and presentation of how responses might vary over a target region. Following Burrough and McDonnell (1998), spatial analysis includes operations on spatial attributes, distance or location operations, and operations that consider topology. These operations frequently give rise to new spatial attributes or spatial entities. Simple logical operations such as map intersects and overlays may be used to de®ne zones based on speci®c climatic or edaphic criteria (e.g. Pollak and Corbett, 1993; Hartkamp et al., 2000; Young et al., 2000). More complex analyses may use spatial data as inputs to models, resulting in estimations of crop response as a function of regional variation in climatic and edaphic conditions (Hartkamp et al., 1999; Collis and Corbett, 2001). Availability of base data on climate, soils, topography, land cover and other factors is also improving, especially through Internet sources (e.g. USDA NRCS, 2001; USGS, 2001; Stanford University Libraries, 2001). Furthermore, georeferencing locations is increasingly simpler, making it much easier to link research sites to spatial data. Hand-held global positioning system (GPS) units now provide geographic positions with an accuracy of 15 m or less (Interagency GPS Executive Board, 2000), and wide area augmentation system (WAAS) should reduce this value to 7 m in 2003 (FAA GPS Product Team, 2001). Inspection of papers published in journals or presented in major meetings representative of mainstream research in agronomy, crop science and soil science, however, revealed few applications of spatial analysis

in the research process. Exceptions were noted in research on ®eld- or landscape-level variation (e.g. Cassel et al., 2000; van Kessel and Wendroth, 2000). Research at the ®eld scale usually involves ``precision farming'' or ``site-speci®c management'', which is often de®ned as managing soil and other spatial variability within production ®elds (Pierce and Nowak, 1999). With the current focus on high resolution (large scale) analyses (e.g. precision agriculture), agronomic research may under-exploit other opportunities for spatial analysis. These include improving ef®ciency of research activities ranging from problem de®nition, to site selection, to data analysis and interpretation in relation to a given crop or system's geographic distribution. Dobermann et al. (2002) and Wopereis et al. (1999) showed the bene®ts of such on-farm research across large regions for improving nutrient management of irrigated rice systems of Asia and West Africa, but similar approaches applied to different environments and crop management practices appear to remain rare. To assess current use of spatial analysis in agronomic research, we examined publications in ®ve journals in agronomy and soil science. Emphasis was given to what we term ``mesoscale'' variation, which represents spatial scales intermediate between those used for precision agriculture and landscape or watershed analysis and those for national, continental or global analyses. Typically, mesoscale variation would be of interest in ®eld research conducted at one or more locations over a region where relevant map scales are on the order of 1:10,000 to 1:500,000. This might range from the county or district level to state or province level or even geographic regions such as the Great Plains of North America. Process-based models are often advocated for analyzing variation in climatic and edaphic conditions (Hartkamp et al., 1999; Kropff et al., 2001), so they might be used either in lieu of, or as a complement to, spatial analyses. Thus, the review of papers also assessed use of crop simulation models. We do not attempt to provide a comprehensive literature review of the use of spatial methods or crop models in agronomic research. Instead, by reviewing a sample of journal papers we wish to raise attention to commonly observed problems. We also consider possible causes of the apparent under-use of spatial information

J.W. White et al. / Field Crops Research 76 (2002) 45±54

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issues of these journals suggested that the sample was representative of the current situation in agricultural research. Papers were assessed for various features to determine whether the research problem was placed in a geographic context, whether the research sites were georeferenced, whether more than one site was used, whether a map or maps were provided, and whether responses to the physical environment were assessed (Table 1). For each feature, papers were scored on a binary scale (0 or 1). Criteria were somewhat subjective and are summarized as follows:

and suggest steps for improving its use in agricultural research. 2. Materials and methods A sample of 32±70 articles per journal were reviewed from Agronomy Journal (1999, vol. 91, issue nos. 1±3), Crop Science (1999, vol. 39, issue nos. 1 and 2), Soil Science Society of America Journal (1999 and 2000, vols. 63 and 64, all issues), Field Crops Research (1999, vols. 60±64, all issues) and Soil and Tillage Research (1999±2000, vols. 53±55, all issues). We only reviewed papers that involved crop response to soil, environment, or management as studied under ®eld conditions, i.e. purely greenhouse or laboratory studies and review articles were excluded. A total of 250 papers were evaluated. Inspection of other recent

 Geographic context of research: Does the paper relate the research topic to specific and significant production regions, preferably indicating the approximate land area, identifying regions with similar or contrasting characteristics, and indicating from

Table 1 Evidence of spatial awareness from papers published in ®ve major agricultural journalsa Number of papersb

Geographic context of researchd Geographic coordinates of sites Number of field sites usede One site Two to four sites Five or more sites Maps provided Evaluated Response to season Response to climate Response to soil moisture Response to nutrients or soil fertility Response to soil physical or chemical constraintsf Developed or evaluated simulation model Used simulation modeling to examine responses Total number of articles reviewed a

Agronomy Journalc

Crop Sciencec

Soil Science Society of America Journalc

Field Crops Research

Soil and Tillage Research

23 (37) 20 (32)

19 (37) 9 (18)

9 (28) 11 (34)

43 (61) 34 (49)

25 (74) 16 (47)

34 23 6 1

(53) (37) (10) (2)

26 21 4 1

(51) (41) (8) (2)

25 6 1 2

(78) (19) (3) (6)

47 14 9 0

(67) (20) (13) (0)

22 6 6 2

(65) (18) (18) (6)

40 19 18 24 10 10 1 63

(63) (30) (29) (38) (16) (16) (2)

12 11 4 3 0 0 1 51

(24) (22) (8) (6) (0) (0) (2)

24 4 7 20 12 2 2 32

(75) (13) (22) (63) (38) (6) (6)

46 27 33 25 9 10 8 70

(66) (39) (47) (36) (13) (15) (11)

18 11 13 12 27 1 1 34

(53) (32) (38) (35) (79) (3) (3)

Section 2 explains details of categories and sources of journals. Numbers in parentheses are percentages. Due to overlap among categories, not all values total 100%. b Excludes journal articles that did not involve ®eld experiments. c Agronomy Journal, Crop Science, and Soil Science Society of America Journal are published, respectively, by the Agronomy Science of America, the Crop Science Society of America, and the Soil Science Society of America, all based in Madison, WI. Field Crops Research is published by Elsevier, Amsterdam, The Netherlands. Soil and Tillage Research is published by Elsevier, Amsterdam, The Netherlands, in collaboration with the International Soil and Tillage Research Organization. d Papers justifying selection of research themes or sites in terms of climate, soils or factors varying in space. e Sites at a single location (e.g. geo-referenced location) are counted as one except if reference is given to naturally occurring differences in soil conditions that do not re¯ect imposed treatments or consequences of recent management (e.g. residue retention or reduced tillage). f Includes salinity, soil depth, bulk density, aluminum toxicity, and other edaphic factors exclusive of nutrients.

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J.W. White et al. / Field Crops Research 76 (2002) 45±54

the review of previous research whether variation in the environment is a major concern? Geographic coordinates of sites: Does the paper provide geographic coordinates sufficiently precise to allow locating the site within a 10 km radius? Providing the name of a research station was considered insufficient. Number of sites used: How many field sites were used? Sites were considered different if they appeared to be more than 1 km apart or specific reference was made to differences in the physical environment (e.g. contrasting soil types within a research station). Maps provided: Were maps showing regional variation presented? Diagrams of field-level variation in yield or soil characteristics were not included since our concern was mesoscale analyses. Response to season: Does the study consider variation in experimental response over seasons or years, usually evidenced by trials being conducted in multiple years? Response to climate: Does the study explicitly consider variation in climatic conditions (potentially including UV radiation and atmospheric CO2 concentration)? Simply describing conditions during a trial was insufficient. Climatic conditions had to be referred to in the analyses or discussion of results. Response to soil moisture: Does the study explicitly consider variation in soil moisture or use treatments that were intended to affect the soil moisture regime (e.g. irrigation or mulching)? Response to nutrients or soil fertility: Does the study explicitly consider variation in soil or plant nutrient status or use treatments that were intended to affect nutrient availability (e.g. fertilizer, crop residue, or manure treatments)? Response to soil physical or chemical constraints: Does the study explicitly consider variation in soil physical or chemical conditions that would limit plant growth (e.g. salinity or soil compaction)? Develops or evaluates simulation model: Does the study describe or validate process-based quantitative models? Used simulation modeling to examine responses: Does the study use simulation models to analyze responses to factors that show spatial or temporal variation (excluding calibration and validation exercises)?

3. Results Of 250 papers examined, only 119 described the geographical context of the central research problem (Table 1). Although not tabulated separately, explicit reference to variation in environments reported in previous studies was particularly rare. Most authors brie¯y described preceding works but did not indicate whether differences in climate or soils explain similarities or contrasts reported in previous research papers. Less than half of the papers georeferenced sites precisely enough to permit another researcher to locate the site within a 10 km radius (Table 1). Papers describing sites outside the USA were more apt to provide coordinates. Of the 32 papers from the Soil Science Society of America Journal, only 34% provided geographic coordinates, but this proportion was 11% for USA papers versus 69% for papers from outside the USA. In Agronomy Journal, 34% of papers provided coordinates, representing 24% of USA papers versus 50% for non-USA locations. The majority of papers presented research conducted at a single site. Less than 10% of papers involved ®ve or more sites, and very few studies analyzed large number of locations through on-farm trials or similar sources, such as those done by DõÂaz-Zorita et al. (1999), CalvinÄo and Sadras (1999), Wade et al. (1999) and Dobermann et al. (2002). Six papers presented maps but in these, emphasis was on geographic description of research areas rather than on environmental variation or presentation of results. For example, DõÂaz-Zorita et al. (1999) located 134 farms sampled in a study relating wheat (Triticum aestivum L.) yield to soil organic matter in the Argentine Pampas, and Gustine and Huff (1999) showed approximate locations of white clover (Trifolium repens L.) populations. Many papers did include two or more seasons of data (Table 1). However, an alarming number of papers consisted of what is often considered the minimal con®guration for a paper describing ®eld research, two seasons of data at a single location. Few papers analyzed potential sources of year-to-year variation using quantitative approaches (counts not taken). Weather, water, and nutrient regimes were common themes that were re¯ected in selection of experimental treatments and measurement variables, with soil

J.W. White et al. / Field Crops Research 76 (2002) 45±54

constraints other than fertility receiving less attention except in Soil and Tillage Research (Table 1). Analogous to the situation with response to season, many papers used a limited a range of treatments, which would constrain quantitative analyses of responses. Use of simulation models varied among the journals, with Field Crops Research containing the highest proportion (18 of 70 papers). Of 36 papers involving models, however, only 13 applied the models to analyze possible impacts of climate, soils or other factors that might show spatial variation. 4. Discussion Our survey indicated that while research in agronomy, crop science and soil science often focuses on factors having strong spatial variation, few papers surveyed in leading journals of these disciplines used approaches that directly address variation at scales above the ®eld level. These ®ndings raise fundamental concerns about the ef®ciency and effectiveness of current research. Strategies for selecting research sites or treatments are seldom based upon an explicit geographic analysis, including reference to environmental effects noted in previous research. Thus, it is dif®cult to judge whether results are applicable to a signi®cant production area, let alone to assess their potential impact on production or the environment. Webster and Oliver (1990) discussed sampling strategies for soil science, and their approaches are applicable to many agronomic studies. A recent assessment of sampling strategies in research on non-timber forest products (Wong, 2000) also provides useful examples. Hodson et al. (1998) and White et al. (2001) illustrated use of simple spatial analyses to identify regions with environmental conditions similar to a given site. Spatial analyses were seldom used to examine responses to environmental factors. The validity of the results is implicitly con®ned to the study site or sites, and opportunities are missed to determine how responses might vary with climate, edaphic conditions or other factors showing mesoscale variation. We do not exclude the value of more intensive measurements and analytical approaches applied to a limited number of sites. Agronomic research should include both detailed research and studies that examine variation

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over larger regions. The latter studies can identify research issues as well as validate ®ndings from a limited set of sites or treatments. Our argument is that geographically oriented multi-site research is underrepresented. In presenting background information or research results, very few maps were used. Nonetheless, maps are often more effective than tables for expressing spatial variation (e.g. Smelcer and Carmel, 1997). Exceptions to the overall trend of lack of geographic perspectives are found in research on variation at the ®eld or local landscape level, often conducted under the labels of ``precision agriculture'' or ``landscape research'' (for a de®nition of ``landscape research'', see van Kessel and Wendroth (2000)). Such research has relevance for attempts to increase resource-use ef®ciency within individual farms or regions of a few square kilometers, but it only partially addresses how genotypes or management should be varied across larger regions in relation to climate, soils or other spatially varying factors. Furthermore, results from plots or regions measuring 0.01±20 km2 (the range represented in a special issue of Soil and Tillage Research that focused on landscape research; van Kessel and Wendroth, 2000) should still consider criteria used to select the research site (or landscape) and how directly applicable the ®ndings might be to other regions. We realize that a review of journal publications can be misleading for various reasons. The sample of journals might be biased. In 1999, Kluwer Academic Publishers launched the journal Precision Agriculture, but as argued above, while research on ®eld-level spatial variation involves issues and methods of relevance to mesoscale analyses, by de®nition its focus is at larger geographic scales (e.g. 1:1000). Brief inspection of several other agricultural journals (e.g. American Journal of Alternative Agriculture, Australian Journal of Experimental Agriculture, Canadian Journal of Plant Science) indicated that overall trends of Table 1 were valid with a few quali®cations. In Economic Botany, authors frequently included maps delimiting study areas or distribution of species. In the American Journal of Alternative Agriculture, there was considerable use of multiple locations, including studies using farmers' ®elds. Journals such as Agricultural Ecosystems and Environments and Agricultural Systems publish more

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J.W. White et al. / Field Crops Research 76 (2002) 45±54

papers in which simulation models or spatial analyses for larger regions are used, but as emphasized previously, such research should appear more frequently in journals targeting a broader audience of researchers. The potentially high costs and technical dif®culty of presenting detailed geographic data in printed formats also may induce researchers to publish through media such as the Internet and CD ROM. For example, Hodson et al. (1999) published a study on water de®cits and potential maize production areas as a CD ROM atlas that included map-viewing software. Perhaps electronic journals will provide a more attractive medium for publishing detailed maps. At a more technical level, researchers may encounter dif®culties in publishing studies that rely heavily on spatial analyses. A map representing spatial variation in an agronomic trait (e.g. economic yield or a water de®cit index) is a model and thus should be validated. Nonetheless, obtaining reliable validation data over a region may be dif®cult. Similarly, statistical methods for analyzing error in spatial data are evolving rapidly (Burrough and McDonnell, 1998; Heuvelink, 1998), and most sets of spatial data are provided without quantitative indicators both of error in values of attributes or in geographic position. Studies on diffusion and use of spatial analysis in other ®elds have identi®ed multiple barriers to adoption. These include doubts over cost-effectiveness, limited access to data and lack of awareness among potential users (e.g. Masser et al., 1996; Hernandez and Verrips, 2001). While cost-effectiveness was an issue several years ago, costs of many GIS packages are now similar to desktop statistical packages (e.g. Limp, 2000), and there are options for low or no-cost simpli®ed systems (e.g. Corbett et al., 2001; GRASS Development Team, 2001; Levine, 2000) that also have reduced training requirements. Large amounts of spatial data are available at low cost through the Internet, although we recognize that the quality and spatial scale may be inadequate for many research applications. Increased access to software tools is also evidenced in widespread use of GIS in precision agriculture and landscape research. Inadequate awareness or training is likely a substantial constraint. Training in use of GIS is now widely available from universities but may mainly bene®t recent graduates. Furthermore, training that focuses on how to use software per se may not create

suf®cient understanding of analytic approaches and their potential applications to agronomic research. While there are numerous examples of use of GIS in agriculture (e.g. Petersen et al., 1995, 1997; UNEP/ GRID-Arendal, 1997; Hartkamp et al., 1999; Nelson et al., 1999), many of these involve applications at larger spatial scales. Declining funds for agronomic ®eld research also may constrain the use of multiple sites for ®eld research with a longer-term or strategic character. In many developing countries, government agencies or international donors funded such research because it was deemed essential for securing food supply and pro®tability of farming. Recent shifts in funding priorities have reduced support for ®eld research. In most developed countries, competitive grant funding is dif®cult to obtain for research on topics such as crop response to soil and nutrients over a larger geographical area, making it dif®cult to regularly update recommendations and incorporate aspects of more detailed agroecological zoning. If our perception of funding constraints is correct, however, the proper use of geospatial techniques seems even more important simply as a means to increase research ef®ciency. Although not the central focus of this paper, we examined use of simulation models since their use might provide instructive comparisons with GIS, and their ability to integrate effects of climatic and edaphic factors as well as management makes modeling complementary to use of GIS (Hartkamp et al., 1999). Of the relatively low portion of papers that dealt with modeling, less than 30% used models to strengthen the analysis or interpretation of results. This pattern was also noted by Kovacs and Fodor (2000) for a larger sample and indicates that there currently is much greater emphasis on model development and validation as compared to application of models to solve research problems. Validated crop or ecosystem models should become standard tools of agronomic research, with particular emphasis given to using them for interpreting results of multi-site or multi-season ®eld experiments. Dobermann et al. (2000) used a crop model in combination with other measured data to assess the in¯uence of factors causing a yield increase in a long-term continuous rice experiment. By doing so, they were able to separate climatic effects from those caused by changes in management.

J.W. White et al. / Field Crops Research 76 (2002) 45±54

Several leverage points exist for promoting a more ``spatially aware'' approach to agronomic research, including increased use of spatial analyses of mesoscale variation. At the level of individual researchers, these include the following: 1. Explicitly consider environmental differences in the review of previous research, such as by using reported differences in climate and soil to reconcile contrasting results from different studies. 2. Select research sites or treatments based on demonstrated relevance to target environments. This allows for use of sites selected for speci®c stresses (e.g. water de®cit or salinity) if the sites are explicitly linked to target production environments. 3. Use larger numbers of sites (or treatments) in order to strengthen analyses of variation across target environments. In an era of concerns relating to climate change and risk, such work could constructively be combined with an attempt to analyze spatial and temporal variation. We particularly encourage greater use of on-farm sites in strategic research that goes beyond yield response trials conducted at multiple sites. Such research should include minimum sets of soil and plant measurements as well as auxiliary data collection (geographical environment, climate, soil) that will allow using tools such as crop models or GIS to expand the geographical range of experimental work beyond the conditions found at research stations. On-farm research should, however, be complemented by detailed experiments done at one or few sites, focusing on a clearer understanding of the processes involved in crop response to environment and management. Alternately, researchers might seek to synthesize results from multiple reports of research conducted over different locations within a region. Increasingly, the private sector may be an important source of such data. 4. Locate all research sites with hand-held GPS units, thus providing a positional accuracy of 15 m or less. If weather stations or soil pro®le pits are at locations separate from ®eld sites, this should be clearly explained. At a minimum, such positions should be described in text (e.g. ``approximately 400 m northwest of the ®eld'').

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5. Employ analysis throughout the research process, starting with site selection and culminating in ®nal analysis and presentation of results. Where appropriate, spatial variation should be presented in maps. 6. Conduct further research on methods for appropriate analyses of error in spatial data used in agricultural research. 7. Use simulation modeling or related quantitative approaches to supplement analyses of variation across locations and seasons. This can include use of models integrated with GIS (Hartkamp et al., 1999; Collis and Corbett, 2001) or use of models for analyzing the temporal variation in crop response to environment and management (Dobermann et al., 2000). Table 2 provides an annotated list of examples of papers that incorporate aspects of these points, but a striking feature of the list is that no study appeared to fully exploit the potential of spatial analyses. The study by Haefele et al. (2000) on rice production in the Senegal River Valley met our criteria exclusive of explicit spatial analyses and research on error analysis. Five sites were selected over a distance of about 120 km along an agroecological gradient (the river valley) to capture major differences in soils and climate and therefore, differences in cropping practices. Geographical coordinates are provided for each site, and a map shows their locations in relation to other important features. Five to 10 on-farm experiments were conducted at each of the sites to capture local spatial variability (at a larger scale), and an interdisciplinary approach considered soil fertility, agronomy, weed science, and socioeconomic factors. The crop model ORYZAS was used to simulate the yield potential based on historical weather data, which then was taken as the basis for a target yield (80% of the maximum simulated yield) to work out fertilizer recommendations for one of the treatments. The statistical analyses assessed major differences among the ®ve sites and helped explain why they occurred. The integrated use of this information identi®ed knowledge gaps and therefore, focused future research. We suggest that spatial analyses might have been used to justify better the selection of sites and to allow estimating of optimal planting dates and fertilizer rates over the entire study region.

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J.W. White et al. / Field Crops Research 76 (2002) 45±54

Table 2 Examples of papers that illustrate different aspects of how spatial analyses and related methods can strengthen agronomic research at mesoscales Paper

Comments

Use of spatial methods to characterize environments Pollak and Corbett (1993) Classified maize growing regions of Mexico and Central America using interpolated climate data Corbett (1998) Classified maize growing regions of Kenya using interpolated climate data Hartkamp et al. (2000) Classified tropical and subtropical maize of the world using interpolated climate data Describes spatial sampling framework Hassan et al. (1998) DõÂaz-Zorita et al. (1999)

Used GIS to design a spatial sampling frame for a national survey of Kenyan maize farmers Described pattern of systematic sampling of 134 wheat fields in Argentina

Use of spatial analysis in processing results Young et al. (2000) Overlaid layers derived from soil and climate data to assess the potential of alternative crops in Wyoming Dobermann and OberthuÈr (1997) Used fuzzy sets to map multivariate soil qualities for irrigated rice in the Philippines Stoorvogel et al. (1993) Estimated nutrient balances for Africa using an agroecological approach Use of maps to present results Andresen et al. (2001)

Mapped results of climate analyses for 13 locations in the Great Lakes region, USA

Use of spatial methods linked to models van Lanen et al. (1992) Linked spatial data and crop models for assessing land-use scenarios across large regions Wilson et al. (1996) Linked solute transport model to climate and soil maps to estimate picloram leaching Matthews et al. (1997) Linked crop models to climate maps and global circulation models to estimate the impact of climate change on rice in Asia Collis and Corbett (2001) Linked climate and soils data to a crop model to estimate potential maize yield in East Africa Stoorvogel (1995) Integrated models and tools with GIS to analyses land-use scenarios for tropical low-lands of Costa Rica

The actions suggested for individuals should be reinforced by institutional policies, including promoting training in spatial analyses as applied to agricultural research, requiring research proposals to characterize the geographic extent of problems in a more quantitative fashion, promoting use of more numerous and diverse research sites, and improving access to spatial data at a range of spatial scales. Implicit in these suggestions is that if funding agencies are truly concerned with increasing the impact of agronomic research, they will have to fund larger (more sites) and longer-term (more than 2-year duration) projects than currently appears to be favored. References Andresen, J.A., Alagarswamy, G., Ritchie, J.T., Rotz, C.A., LeBaron, A.W., 2001. Weather impacts on maize, soybean, and alfalfa production in the Great Lakes Region, 1895±1996. Agron. J. 93, 1059±1070. Burrough, P.A., McDonnell, R.A., 1998. Principles of Geographic Information Systems. Oxford University Press, Oxford.

CalvinÄo, P.O., Sadras, V.O., 1999. Interannual variation in soybean yield: interaction among rainfall, soil depth and crop management. Field Crops Res. 63, 237±246. Cassel, D.K., Wendroth, O., Nielsen, D.R., 2000. Assessing spatial variability in an agricultural experiment station ®eld: opportunities arising from spatial dependence. Agron. J. 92, 706±714. Collis, S.N., Corbett, J.D., 2001. A methodology for linking spatially interpolated climate surfaces with crop growth simulation models. In: White, J.W., Grace, P.R. (Eds.), Proceedings of the Workshop on Directions in Modeling Wheat and Maize for Developing Countries, CIMMYT International, El BataÂn, Mexico, 4±6 May 1998. CIMMYT, Mexico, D.F., pp. 28±35. Corbett, J.D., 1998. Classifying maize production zones. In: Hassan, R.M. (Ed.), Maize Technology Development and Transfer: A GIS Application for Research Planning in Kenya. CIMMYT±KARI±CAB International, New York, pp. 15±25. Corbett, J.D., Collis, S.N., Bush, B.R., Muchugu, E.I., Jeske, R.Q., Martinez, R.E., Zermoglio, M.F., Martinez-Romero, E., White, J.W., Hodson, D., 2001. Almanac characterization tool: a resource base for characterizing agricultural, natural and human environments. Blackland Research Center Report No. 99-06. Texas Agricultural Experiment Station, Texas A&M University System, Temple, TX. DõÂaz-Zorita, M., Buschiazzo, D.E., Peinemann, N., 1999. Soil organic matter and wheat productivity in the semiarid Argentine Pampas. Agron. J. 91, 276±279.

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