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Showing papers in "Agronomy Journal in 2012"


Journal ArticleDOI
TL;DR: This review was conducted to provide a compilation of the most relevant historic research information and defi ne the tremendous future potential of castor.
Abstract: Castor (Ricinus communis L.) is one of the oldest cultivated crops, but currently it represents only 0.15% of the vegetable oil produced in the world. Castor oil is of continuing importance to the global specialty chemical industry because it is the only commercial source of a hydroxylated fatty acid. Castor also has tremendous future potential as an industrial oilseed crop because of its high seed oil con- tent (more than 480 g kg -1 ), unique fatty acid composition (900 g kg -1 of ricinoleic acid), potentially high oil yields (1250-2500 L ha -1 ), and ability to be grown under drought and saline conditions. Th e scientifi c literature on castor has been generated by a relatively small global community of researchers over the past century. Much of this work was published in dozens of languages in journals that are not easily accessible to the scientifi c community. Th is review was conducted to provide a compilation of the most relevant historic research information and defi ne the tremendous future potential of castor. Th e article was prepared by a group of 22 scientists from 16 institutions and eight countries. Topics discussed in this review include: (i) germplasm, genetics, breeding, biotic stresses, genome sequencing, and biotechnology; (ii) agronomic production practices, diseases, and abiotic stresses; (iii) management and reduction of toxins for the use of castor meal as both an animal feed and an organic fertilizer; (iv) future industrial uses of castor including renew- able fuels; (v) world production, consumption, and prices; and (vi) potential and challenges for increased castor production.

251 citations


Journal ArticleDOI
TL;DR: In this article, Huete et al. present a remote estimation of the gLAI, which is a biophysical characteristic of vegetation that can be subdivided into photosynthetically active and photosynthetic inactive components.
Abstract: Published in Agron. J. 104:1336–1347 (2012) Posted online 29 June 2012 doi:10.2134/agronj2012.0065 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. T leaf area index (LAI), the ratio of leaf area to ground area, typically reported as square meters per square meter, is a commonly used biophysical characteristic of vegetation (Watson, 1947). The LAI can be subdivided into photosynthetically active and photosynthetically inactive components. The former, the gLAI, is a metric commonly used in climate (e.g., Buermann et al., 2001), ecological (e.g., Bulcock and Jewitt, 2010), and crop yield (e.g., Fang et al., 2011) models. Because of its wide use and applicability to modeling, there is a need for a nondestructive remote estimation of gLAI across large geographic areas. Various techniques based on remotely sensed data have been utilized for assessing gLAI (see reviews by Pinter et al., 2003; Hatfield et al., 2004, 2008; Doraiswamy et al., 2003; le Maire et al., 2008, and references therein). Vegetation indices, particularly the NDVI (Rouse et al., 1974) and SR (Jordan, 1969), are the most widely used. The NDVI, however, is prone to saturation at moderate to high gLAI values (Kanemasu, 1974; Curran and Steven, 1983; Asrar et al., 1984; Huete et al., 2002; Gitelson, 2004; Wu et al., 2007; Gonzalez-Sanpedro et al., 2008) and requires reparameterization for different crops and species. The saturation of NDVI has been attributed to insensitivity of reflectance in the red region at moderate to high gLAI values due to the high absorption coefficient of chlorophyll. For gLAI below 3 m2/m2, total absorption by a canopy in the red range reaches 90 to 95%, and further increases in gLAI do not bring additional changes in absorption and reflectance (Hatfield et al., 2008; Gitelson, 2011). Another reason for the decrease in the sensitivity of NDVI to moderate to high gLAI values is the mathematical formulation of that index. At moderate to high gLAI, the NDVI is dominated by nearinfrared (NIR) reflectance. Because scattering by the cellular or leaf structure causes the NIR reflectance to be high and the absorption by chlorophyll causes the red reflectance to be low, NIR reflectance is considerably greater than red reflectance: e.g., for gLAI >3 m2/m2, NIR reflectance is >40% while red reflectance is <5%. Thus, NDVI becomes insensitive to changes in both red and NIR reflectance. Other commonly used VIs include the Enhanced Vegetation Index, EVI (Liu and Huete, 1995; Huete et al., 1997, 2002), its ABStrAct

223 citations


Journal ArticleDOI
TL;DR: In this article, the influence of soil and weather parameters on N responses of corn across 51 studies involving the same N rate treatments which were carried out in a diversity of North American locations between 2006 and 2009.
Abstract: 3 Soil properties and weather conditions are known to affect soil nitrogen (N) availability and 4 plant N uptake. However, studies examining N response as affected by soil and weather 5 sometimes give conflicting results. Meta-analysis is a statistical method for estimating treatment 6 effects in a series of experiments to explain the sources of heterogeneity. In this study, the 7 technique was used to examine the influence of soil and weather parameters on N responses of 8 corn (Zea mays L.) across 51 studies involving the same N rate treatments which were carried out 9 in a diversity of North American locations between 2006 and 2009. Results showed that corn 10 response to added N was significantly greater in fine-textured soils than in medium-textured 11 soils. Abundant and well-distributed rainfall and, to a lesser extent, accumulated corn heat units 12 enhanced N response. Corn yields increased by a factor of 1.6 (over the unfertilized control) in 13 medium-textured soils and 2.7 in fine-textured soils at high N rates. Subgroup analyses were 14 performed on the fine-textured soil class based on weather parameters. Rainfall patterns had an 15 important effect on N response in this soil texture class, with yields being increased 4.5-fold by 16 in-season N fertilization under conditions of “abundant and well-distributed rainfall.” These 17 findings could be useful for developing N fertilization algorithms that would allow for N 18 application at optimal rates taking into account rainfall pattern and soil texture, which would lead 19 to improved crop profitability and reduced environmental impacts. 20

189 citations


Journal ArticleDOI
TL;DR: In this article, a field study was conducted at Maricopa, AZ, where wheat was planted from September to May over a 2-yr period for a total of 12 planting dates.
Abstract: Possible future increases in atmospheric temperature may threaten wheat (Triticum aestivum L.) production and food security. The purpose of this research is to determine the response of wheat growth to supplemental heating and to seasonal air temperature from an unusually wide range of planting dates. A field study was conducted at Maricopa, AZ, where wheat was planted from September to May over a 2-yr period for a total of 12 planting dates. Supplemental heating was provided for 6 of the 12 planting dates using infrared heaters placed above the crop which increased canopy temperature by 1.3°C during the day and 2.7°C during the night. Grain yield declined 42 g m ―2 (6.9%) per 1°C increase in seasonal temperature above 16.3°C. Supplemental heating had no effect on grain yield for plantings in winter (Dec./Jan.) since temperatures were near optimum (14.9°C). However, in spring (Mar.) plantings where temperature (22.2°C) was above optimum, supplemental heating decreased grain yield from 510 to 368 g m ―2 . Supplemental heating had the greatest effect in the early fall plantings (Sept./Oct.) when temperature was slightly below optimum (13.8°C) and mid-season frost limited the yield of unheated plots to only 3 g m ―2 whereas yield of heated plots was 435 g m ―2 . Thus, possible future increases in temperature may decrease wheat yield for late plantings and shift optimum planting windows to earlier dates in areas of the world similar to the desert southwest of the United States.

133 citations


Journal ArticleDOI
TL;DR: Wortman et al. as discussed by the authors found that cover crop residue conserved soil moisture relative to a no-till system without cover crops and showed that the amount of soil water used by cover crops, potentially reducing available soil moisture for the cash crop.
Abstract: Published in Agron. J. 104:1425–1435 (2012) Posted online 1 Aug. 2012 doi:10.2134/agronj2012.0185 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. C crops have been shown to provide many environmental and agronomic services within agroecosystems. These include reduced soil erosion, increased biological diversity (e.g., microbes, insects, and birds), increased nutrient cycling and biological N2 fixation, increased soil organic matter, improved weed control, and increased crop yield (Pimentel et al., 1992; Pimentel et al., 1995; Sainju and Singh, 1997; Williams et al., 1998; Altieri, 1999; Reddy et al., 2003; Teasdale et al., 2007). While cover crops have traditionally been used as a soil conservation tool (Pimentel et al., 1995), there is increasing interest in using cover crops to enhance agronomic crop performance. However, maximizing agronomic benefits associated with cover crops will depend on appropriate species choice and residue management (Ashford and Reeves, 2003; Wortman et al., 2012). Selecting a single species is often popular among farmers due to the ease of planting, uniform development, and predictable termination efficacy of the cover crop (Creamer et al., 1995; Mirsky et al., 2009). However, multi-species mixtures may increase productivity, stability, resilience, and resource-use efficiency of the cover crop community (Tilman, 1996; Tilman et al., 1997, 2001; Trenbath, 1999; Wortman et al., 2012). Despite the demonstrated benefits, on-farm adoption remains limited due to farmer concerns about the potential cost and management implications of cover crop use. One of the top concerns among farmers is the amount of soil water used by cover crops, potentially reducing available soil moisture for the cash crop. During seasons with average and above-average rainfall conditions, differences in available soil moisture among cover crop species and mixtures are often undetectable. However, when cover crop productivity is high and precipitation becomes limiting, species can differ greatly in their effects on soil moisture (Unger and Vigil, 1998; Daniel et al., 1999). While transpiration demands will undoubtedly vary among species, the method of cover crop termination and residue management may have a greater impact on available soil moisture during main crop growth. Daniel et al. (1999) found that volumetric soil moisture (%) was increased by as much as 2.4% to a depth of 61 cm when cover crops were terminated with herbicides in a no-till system compared to conventional termination with a field disk. Soil water savings associated with no-till practices have been well documented (Blevins et al., 1983; De Vita et al., 2007), but the additional benefits of cover crop residue in a conservation tillage system are not as clear. Liebl et al. (1992) found that transpiration reduced available soil moisture during dry periods, but following no-till termination cover crop residue conserved soil moisture relative to a no-till system without cover crops. Given that the driest portion of the growing season in the western Corn Belt typically occurs after cover crop growth (i.e., June–August), potential soil moisture savings offered by the residue (post-termination) throughout the growing season may negate moisture deficits observed during cover crop growth. AbstrAct

132 citations


Journal ArticleDOI
TL;DR: Aneja et al. as mentioned in this paper measured NH3 loss from surface-applied N fertilizer by using a combination of micrometeorological, enclosure, and indirect methods and found that the micrometerological and 15N balance (indirect) methods do not interfere with atmospheric conditions near the soil surface or affect NH3 volatilization.
Abstract: Published in Agron. J. 104:1595–1603 (2012) Posted online 5 Sep 2012. doi:10.2134/agronj2012.0210 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. N fertilizer consumption has increased more rapidly than that of P or K to support world food supplies (FAO, 2006). Improperly managed, N fertilizers can form gaseous or soluble compounds with potential to pollute air (NH3, NOx, N2O) or contaminate surface and ground waters with nitrate N (Aneja et al., 2003). Ammonia is the most reduced form of reactive N in the atmosphere and is increasing from human activities (Aneja et al., 2008) with an unpredictable fate of when and where it will be redeposited into the global N cycle (Galloway et al., 2004). Agriculture represents 20 to 80% of NH3–N emissions in many countries with livestock manures and N fertilizers as major sources (Misselbrook et al., 2000; Aneja et al., 2008; Zhang et al., 2010). Ammonia volatilization is one of the main pathways that N is lost from organic and inorganic N fertilizer application (Ma et al., 2010). This can result in low nitrogen use efficiencies (NUE) by crops (Bouwman et al., 2002; Rochette et al., 2008). The amount of NH3 volatilized from surface-applied N fertilizer is controlled by many interacting soil characteristics and climatic factors and is difficult to predict (McGinn and Janzen, 1998). Over the past 35 to 40 yr, development of methodology to measure NH3 losses under field condition has led to an improved understanding of major factors that drive this loss under dynamic field conditions (McGinn and Janzen, 1998, Misselbrook and Hansen, 2001). Conditions that favor gaseous loss of NH3 from surface-applied N includes high crop residue, warm temperature (>13°C), a drying soil surface (water vapor loss from surface), neutral or alkaline soil pH, and low cation exchange capacity (as found in sandy soil) (Clay et al., 1990; Ferguson and Kissel, 1986; Bouwmeester et al., 1985; Sommer and Christensen, 1992). Advances in N management and fertilizer technology have made it possible to reduce NH3–N losses (Snyder, 2008). Enhanced-efficiency N fertilizers that control N release have been available in the U.S. fertilizer market for several years, but their use has been limited due to their higher cost (Stewart, 2008). However, increasing N fertilizer prices, heightened environmental awareness, increasing area under conservation tillage agriculture and improved manufacturing technology has led to increased interest in enhanced-efficiency N fertilizers (Rochette et al., 2009; Halvorson et al., 2010). Different techniques have been employed to measure NH3 loss from N sources applied to soils (Misselbrook et al., 2005; McGinn and Janzen, 1998; Cabrera et al., 2001; Pacholski et al., 2006). Most of these measurement techniques represent major categories: micrometeorological, enclosure, and indirect methods. The micrometeorological (direct) and 15N balance (indirect) methods (McGinn and Janzen, 1998) allow absolute estimates of NH3–N loss in the field because they do not interfere with atmospheric conditions near the soil surface or affect NH3 volatilization. Evaluating the effects of N source, N application rate, or soil management on NH3 emissions in field environments generally requires several treatments and replications, and small plots (<50 m2) where the fetch criterion is not adequate to ABSTRACT

116 citations



Journal ArticleDOI
TL;DR: In this paper, the authors evaluated differences in yields and associated downside risk from using no-till and tillage practices across the United States and evaluated with respect to six crops and environmental factors including geographic location, annual precipitation, soil texture, and time since conversion from tillage to notill.
Abstract: Th is research evaluated diff erences in yields and associated downside risk from using no-till and tillage practices. Yields from 442 paired tillage experiments across the United States were evaluated with respect to six crops and environmental factors including geographic location, annual precipitation, soil texture, and time since conversion from tillage to no-till. Results indicated that mean yields for sorghum [Sorghum bicolor (L.) Moench] and wheat (Triticum aestivum L.) with no-till were greater than with tillage. In addition, no-till tended to produce similar or greater mean yields than tillage for crops grown on loamy soils in the Southern Seaboard and Mississippi Portal regions. A warmer and more humid climate and warmer soils in these regions relative to the Heartland, Basin and Range, and Fruitful Rim regions appear to favor no-till on loamy soils. With the exception of corn (Zea mays L.) and cotton (Gossypium hirsutum L.) in the Southern Seaboard region, no-till performed poorly on sandy soils. Crops grown in the Southern Seaboard were less likely to have lower no-till yields than tillage yields on loamy soils and thus had lower downside yield risk than other farm resource regions. Consistent with mean yield results, soybean [Glycine max (L.) Merr.] andmore » wheat grown on sandy soils in the Southern Seaboard region using no-till had larger downside yield risks than when produced with no-till on loamy soils. Th e key fi ndings of this study support the hypothesis that soil and climate factors impact no-till yields relative to tillage yields and may be an important factor infl uencing risk and expected return and the adoption of the practice by farmers.« less

104 citations


Journal ArticleDOI
TL;DR: In this paper, the impact of cover crops (CCs) on wheat and grain sorghum yields is not well understood, and the authors assessed crop yield and its relationship with CC-induced changes in soil properties for a 15-yr CC experiment in wheat-sorghum rotation at 0, 33, 66, and 100 kg ha of N application in south central Kansas.
Abstract: Impact of cover crops (CCs) on winter wheat [Triticum aestivum (L.)] and grain sorghum [Sorghum bicolor (L.) yields is not well understood. We assessed crop yield and its relationships with CC-induced changes in soil properties for a 15-yr CC experiment in wheat-sorghum rotation at 0, 33, 66, and 100 kg ha of N application in south central Kansas. Hairy vetch (Vicia villosa Roth) was used as a winter CC from 1995 to 2000, while sunn hemp (SH; Crotalaria juncea L.) and late-maturing soybean (LMS; Glycine max L.) were used as summer CCs in notill from 2002 to 2008. Summer CCs increased crop yields particularly at low rates of N application. At 0 kg N ha, SH increased sorghum yield by 1.18 to 1.54 times, while wheat yield increased by 1.60 times in the first year (2004) after CC establishment relative to non-CC plots. At 66 kg N ha, SH had no effects on sorghum yield, but it increased wheat yield in three of four years. Cover crops increased near-surface soil total N pool by 270 kg ha. Crop yield increased with the CC-induced decrease in soil maximum compactibility (soil’s susceptibility to compaction) and soil temperature, and increase in soil aggregate stability, soil organic C (SOC) and total N concentration, and soil water content, particularly at 0 kg N ha. Principal component analysis (PCA) selected soil compactibility and total N as the best yield predictors. Inclusion of summer legume CCs in no-till fixes N, increases crop yield and improves soil-crop relationships. Abbreviations: CCs, cover crops; LMS, late-maturing soybean; SOC, soil organic carbon; SH, sunn hemp; PCA, principal component analysis; PCs, principal components Understanding CC impacts on soil-crop relationships is essential to the development of sustainable cover cropping systems. Benefits of CCs for providing additional biomass input,

102 citations


Journal ArticleDOI
TL;DR: In this paper, the Agricultural Reference Index for Drought (ARID) was developed as a reference index to approximate the water stress factor that is used to affect growth and other physiological processes in crop simulation models.
Abstract: Several drought indices are available to compute the degree of drought to which crops are exposed. They vary in complexity, generality, and the adequacy with which they represent processes in the soil, plant, and atmosphere. Agricultural Reference Index for Drought (ARID) was developed as a reference index to approximate the water stress factor that is used to affect growth and other physiological processes in crop simulation models. Using RMSE, Willmott d index, and modeling efficiency (ME) as performance measures, ARID was evaluated using soil water contents in the root zone measured daily in two grass fields in Florida. The ability of ARID was assessed through comparison with the water deficit index (WSPD) of the Decision Support System for Agrotechnology Transfer (DSSAT) CERES-Maize model. Seven other drought indices were compared with WSPD to identify the most appropriate agricultural drought index. Values of each index were computed for full canopy cover periods of maize (Zea mays L.) crops for 16 locations in the U.S. Southeast. Using periodic values, the performance of each index was assessed in terms of its correlation (r) with and departure from WSPD. The ARID reasonably predicted soil water contents (RMSE = 0.01–0.019, d index = 0.92–0.94, ME = 0.66–0.73) and adequately approximated WSPD (r = 0.90, RMSE = 0.15). Among the indices compared, ARID mimicked WSPD the most closely (RMSE smaller by 1–83%, r larger by 1–630%) and captured weather fluctuation effects the most accurately. Results indicated that ARID may be used as a simple index for quantifying drought and its effects on crop yields.

98 citations


Journal ArticleDOI
TL;DR: Evaluating nutrient concentration and grain yield of soybean cultivars with diff erent life cycles as aff ected by palisadegrass found them to be viable options to crop-livestock integration, although they did not have both soybean or palisADEgrass yield.
Abstract: Agriculture and livestock integration is a sustainable practice that improves both crop yield and pasture recuperation/formation. However, to achieve success it is important to identify crop cultivars more adapted to intercropping with grasses. Th erefore, the objective was to evaluate nutrient concentration and grain yield of soybean (Glycine max (L.) Merr.) cultivars with diff erent life cycles as aff ected by palisadegrass (Brachiaria brizantha (Hochst. ex A. Rich) Stapf) intercropped in the same furrow at diff erent depths, in a no-till system, as well as dry matter production and protein concentration of palisadegrass pasture. Experiments were performed during two growing seasons, on a Typic Haplorthox, at Botucatu, Sao Paulo State, Brazil. Th e experimental design was a randomized block, arranged in a 2 × 4 factorial scheme, with six replications. Treatments consisted of two cropping systems (sole cropped soybean; soybean and palisadegrass intercropped) and four soybean cultivars (super-early cycle (Monsoy 6101), early cycle (Embrapa 48), normal cycle (BRS 133), and late cycle (Emgopa 313)). Life cycle duration of the soybean had a marked eff ect, and only early cycle soybean were successful intercrops. Intercropping palisadegrass with super-early or early soybean cultivars were viable options to crop-livestock integration, because they did not aff ect both soybean or palisadegrass yield. In addition, with these cultivars, it was possible to cultivate grain and then aft erward more time for cattle (Bos taurus and Bos indicus) grazing in the same area, providing greater revenue compared to sole soybean cropped or in the intercropping with longer cycle cultivars.





Journal ArticleDOI
TL;DR: In a recent study, this article showed no consistent grain yield benefit from a twin-row over single-row confi gurations at the same plant densities in the states of Alabama, Iowa, Missouri, or Nebraska (Elmore and Abendroth, 2007; Nelson and Smoot, 2009; Balkcom et al., 2011; Novacek, 2011).
Abstract: Published in Agron. J. 104:1747–1756 (2012) doi:10.2134/agronj2012.0231 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. T continuous increase in maize grain yield in the world’s primary growing areas during the last decades was mainly driven by the development of crowding stress tolerant hybrids that allowed for dramatic increases in plant population and, therefore, in production per unit area (Russell, 1984; Tollenaar and Wu, 1999; Duvick, 2005). Maize grain yields in the United States have also increased due to earlier planting dates (Kucharik, 2008) and more extensive use of irrigation (Cassman, 1999). Sustaining maize grain yield increases into the future requires continued reconsideration of current agronomic practices. Decreasing row spacing at equal plant density promotes more equidistant plant spacing, theoretically reducing plant-toplant competition, while improving plant resource capture and utilization (Duncan, 1984; Andrade et al., 2002; Barbieri et al., 2008) and decreasing weed competition through earlier canopy closure (Bullock et al., 1988). Nonetheless, sharply contrasting conclusions have been reported regarding grain yield response to narrow rows (Nielsen, 1988; Porter et al., 1997; Barbieri et al., 2000; Farnham, 2001; Ma et al., 2003; Andrade et al., 2002; Shapiro and Wortmann, 2006; Yilmaz et al., 2008), and the grain yield benefi t from the implementation of this practice may not warrant the additional machinery investment required. Th e spatial confi guration known as twin rows (Karlen and Camp, 1985) is not a new concept. Twin-row planting systems have proven to be advantageous to soybean [Glycine max (L.) Merr.] yields vs. the single-wide-row alternative of 76-cm spacing (Janovicek et al., 2006) and have gained renewed interest for U.S. maize production in the past decade. Th eoretically, twinrow maize planting systems appears to be an opportunity to derive the benefi ts of narrow rows without need of major changes in harvest, nutrient, or pest application equipment. While the distance between consecutive maize plants within a row at around 85,000 pl ha–1 is around 15 cm for 76-cm planting row widths, in a precisely distributed twin-row arrangement with a 20-cm distance between paired rows, plants ought to be approximately 25 cm from their closest neighbors. Twin-row research has been performed across the United States with varying success, but recent studies showed no consistent grain yield benefi t from twin-row over single-row confi gurations at the same plant densities in the states of Alabama, Iowa, Missouri, or Nebraska (Elmore and Abendroth, 2007; Nelson and Smoot, 2009; Balkcom et al., 2011; Novacek, 2011). In conditions without major nutrient or water limitations, maize grain yield depends most on radiation interception and radiation-driven photosynthetic conversion effi ciencies around ABSTRACT Twin-row planting systems in maize (Zea mays L.) have been proposed as an alternative spatial arrangement that should theoretically decrease plant-to-plant competition, alleviate crop crowding stress and improve yields. Uncertainty remains, however, as to whether twin rows are a feasible option to increase plant densities and improve grain yields. Th ree hybrids (DKC62-54, DKC61-19, and DKC57-66) were grown from 2009 to 2011 to evaluate the individual and interacting eff ects of plant density (PD1 = 69,000; PD2 = 81,000; PD3 = 93,000; and PD4 = 105,000 plants [pl] ha–1) and spatial confi guration (conventional single 76-cm row width vs. 20-cm twin rows spaced 76-cm between paired-rows) on dark prairie soil in WestCentral Indiana. Th e primary research objectives were to determine (i) whether the twin-row spatial arrangement permits higher optimum plant densities, (ii) whether hybrids vary in their response to a twin-row arrangement, and (iii) diverse morphophysiological trait responses to density and spatial treatments. Twin rows never yielded signifi cantly more than single rows at any plant density or hybrid combination in any year of this study. Furthermore, there was no evidence that grain yield-optimizing plant densities were any higher with twin vs. single rows in any hybrid. Twin rows slightly increased leaf area index (LAI) at silk emergence stage in 2010 (mean LAI = 4.8) and 2011 (mean LAI = 4.0), but not in 2009 (mean LAI = 4.4). Despite higher plant spacing variation, radiation interception was initially favored by earlier canopy closure with twin-row planting, but the relative radiation-interception advantage declined as plant density increased and at a later vegetative stage.

Journal ArticleDOI
TL;DR: The results of the four experiments point to a common conclusion that fall weed competition is the dominant mechanism for early spring weed suppression following forage radish winter cover crops.
Abstract: Little is known about the mechanism of winter annual weed suppression by forage radish (Raphanus sativus L. variety longipinnatus) winter cover crops. Previous studies suggest that allelopathy from decomposing residue and competition due to rapid canopy development contribute to weed suppression by other Brassica cover crops. Four contrasting experimental approaches were used to identify the mechanism of weed suppression by forage radish cover crops. Results of a field based cover crop residue-transfer experiment supported the hypothesis that fall cover crop weed competition is the dominant mechanism of weed suppression following forage radish cover crops. A high level of early spring weed suppression was observed where forage radish grew in the fall regardless of whether residues were left in place or removed. In contrast, there was limited weed suppression in bare soil treatments that received additions of forage radish tissues. Bioassays using cover crop amended soil or aqueous extracts of cover crop tissues and amended soil did not reveal any allelopathic activity limiting seed germination or seedling establishment. In a field-based weed seed bioassay, forage radish cover crops did not inhibit emergence of winter-planted weed seeds relative to a no cover crop control. Forage radish amended soils stimulated seedling growth of lettuce (Lactuca sativa L.) in all types of bioassays. The results of the four experiments in this study point to a common conclusion that fall weed competition is the dominant mechanism for early spring weed suppression following forage radish winter cover crops.

Journal ArticleDOI
TL;DR: In a recent study, this paper found that cover crop use on irrigated crop land in California will likely increase due to the Irrigated Lands Regulatory Program that regulates discharges such as winter runoff from agricultural lands (CEPA, 2011).
Abstract: Published in Agron. J. 104:684–698 (2012) Posted online 7 Mar. 2012 doi:10.2134/agronj2011.0330 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. C organic production systems for high-value, cool-season vegetables such as lettuce (Lactuca sativa L.) and broccoli (B. oleraceae L. Italica Group) can be classified as high-input organic systems because they have high production costs (>$18,000 ha–1crop–1) (Tourte et al., 2004a, 2004b), and typically use high-N supplemental organic fertilizers. Winter cover cropping is a best management practice for these shallow-rooted vegetables systems because the more extensive root systems of cover crops scavenge nutrients that might otherwise be lost by leaching or soil erosion, and because cover crops add organic matter that is critical to maintain and improve soil quality (Wyland et al., 1996; Fageria et al., 2005; Hartz, 2006). Despite their benefits in both organic and conventional systems, cover crops are much more common on organic than conventional vegetable farms in the central coast of California. Annual agricultural land rent here can exceed $6000 ha–1 yr–1 and replacing a bare fallow with a winter cover crop can reduce the typical number of crops produced per ha per year from 2.5 to 2 or 1.5 due to delayed spring plantings (Klonsky and Tourte, 2011). The opportunity costs of forgone cash crop income are one of the largest costs of cover cropping and a major obstacle to increased adoption (Snapp et al., 2005). However, cover crop use on irrigated crop land in California will likely increase due to the Irrigated Lands Regulatory Program that regulates discharges such as winter runoff from agricultural lands (CEPA, 2011). The USDA National Organic Program standards (§205.203a) require that organic producers ‘select and implement tillage and cultivation practices that maintain or improve the physical, chemical, and biological condition of soil and minimize soil erosion’ (AMS, 2011). Maintaining and improving soil organic matter (SOM) in tillage-intensive vegetable production is challenging because postharvest crop residues that are incorporated into the soil are often low (i.e., 2.2 Mg ha–1 for lettuce) (Mitchell, 1999). Furthermore, vegetables with greater residue such as broccoli, are unlikely to improve SOM because the low C/N ratio of the vegetable residue hastens their decomposition. Therefore, vegetable farmers typically apply compost and grow cover crops to add more recalcitrant forms of C to increase SOM. Compost from off-farm sources is a more convenient way than cover cropping to add SOM because fields are always available for cash cropping. However, cover cropping is a more sustainable approach because it reduces a farm’s reliance on off-farm inputs and also provides essential ecosystem services such as nutrient scavenging. Typical winter cover crops in the central coast of California include mustards, cereals, and legume–cereal mixtures (Brennan and Smith, 2005). Mustard cover crops became popular here in the past 10 yr and were aggressively marketed for their potential biofumigation properties to suppress soilborne diseases of lettuce; however, this tactic is not effective (Bensen et al., 2009). Several 2-yr studies (van Bruggen et al., 1990; Jackson et al., 1993, 2004; Brennan and Smith, 2005; ABSTRACT Long-term research on cover crops (CC) is needed to design optimal rotations. Winter CC shoot dry matter (DM) of rye (Secale cereale L.), legume–rye, and mustard was determined in December to February or March during the first 8 yr of the Salinas Organic Cropping Systems trial focused on high-value crops in Salinas, CA. By seed weight, legume–rye included 10% rye, 35% faba (Vicia faba L.), 25% pea (Pisum sativum L.), and 15% each of common vetch (V. sativa L.) and purple vetch (V. benghalensis L.); mustard included 61% Sinapis alba L. and 39% Brassica juncea Czern. Cover crops were fall-planted at 1x and 3x seeding rates (SR); 1x SR were 90 (rye), 11 (mustard), and 140 (legume–rye) kg ha–1. Vegetables followed CC annually. Cover crop densities ranged from 131 to 854 plants m–2 and varied by CC, SR, and year. Year, CC, and SR affected DM production, however, the effects varied across the season and interactions occurred. Averaged across years, final DM was greater in rye and legume–rye (7 Mg ha–1) than mustard (5.6 Mg ha–1), and increased with SR through January. Dry matter production through the season was correlated significantly with growing degree days (GDD). Legumes contributed 27% of final legume–rye DM. Season-end legume DM was negatively correlated with GDD at 30 d, and legume DM in the 3x SR increased during years with frequent late-season rainfall. Seed costs per Mg of final CC DM at 1x SR were approximately three times higher for legume–rye than rye and mustard.

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TL;DR: Nielsen et al. as discussed by the authors used a plant canopy analyzer and point analysis of above-canopy digital photographs to determine relationships between LAI and canopy cover for corn (Zea mays L), winter wheat ( Triticum aestivum L.), and spring triticale (Triticosecale spp.) grown under dryland or very limited irrigation conditions.
Abstract: Previously collected data sets that would be useful for calibrating and validating AquaCrop contain only leaf area index (LAI) data but could be used if relationships were available relating LAI to canopy cover (CC). The objective of this experiment was to determine relationships between LAI and CC for corn ( Zea mays L.), winter wheat ( Triticum aestivum L.), and spring triticale (´ Triticosecale spp.) grown under dryland or very limited irrigation conditions. The LAI and CC data were collected during 2010 and 2011 at Akron, CO, and Sidney, NE, using a plant canopy analyzer and point analysis of above-canopy digital photographs. Strong relationships were found between LAI and CC that followed the exponential rise to a maximum form. The relationship for corn was similar to a previously published relationship for LAI <2 m 2 m –2 but predicted lower CC for greater LAI. Relationships for wheat and triticale were similar to each other. D.C. Nielsen, USDA-ARS Central Great Plains Research Station, 40335 County Road GG, Akron, CO 80720; J.J. Miceli-Garcia, LI-COR Biosciences, 4647 Superior Street, Lincoln, NE 68504; D.J. Lyon, Dep. of Agronomy and Horticulture, Univ. of Nebraska, Panhandle Research and Extension Center, 4502 Ave. I, Scottsbluff, NE 69361. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. The USDA is an equal opportunity employer. Received 28 Mar. 2012. *Corresponding author (david.nielsen@ars.usda.gov).

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TL;DR: The government of Ireland as discussed by the authors provided assistance through the Irish Aid and Irish Embassy in Malawi to the World Agroforestry Centre (WAC) to support the development of Malawi's agriculture.
Abstract: The government of Ireland through the Irish Aid and Irish Embassy in Malawi to the World Agroforestry Centre.


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TL;DR: The fate of ad hoc approaches to crop simulation modeling during the past 15 yr is reviewed to review the fate of the myth of the universal crop model.
Abstract: The "Use and Abuse of Crop Simulation Models" special issue of Agronomy Journal published in 1996 ended with the myth of the universal crop model. Sinclair and Seligman consequently recommended tailoring models to specific problems. This paper reviews the fate of the idea of such ad hoc approaches to crop simulation modeling during the past 15 yr. Most crop modelers have since adhered to the principles formulated by Sinclair and Seligman, but yet their practice faces two major issues: (i) how to define the structure of the model as depending on the question to be addressed (model conceptualization) and (ii) how to minimize efforts in software development (model computerization). Progress in model conceptualization as reported in the literature concerns (i) inferring a conceptual model from what is known of the problem to address, (ii) deriving summary models from comprehensive ones, and (iii) using multivariate methods to analyze the hierarchy of drivers of variability in the variable to be predicted. Considerable effort has been invested in the development of frameworks to facilitate model computerization, and the commercial modeling software is constantly improving. But there are limits in the flexibility permitted by these tools. Acquiring basic skills in coding a model using a scientific programming language is preferred by scientists wishing to keep the fullest understanding and control on their crop models. Connecting the model to commercial database software may facilitate this strategy. However, the computerization issue may still lead to tensions between modeling teams concerning the legitimacy to develop their own model. (Resume d'auteur)

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TL;DR: This article developed a new maize simulation model, CSM-IXIM, by adapting code from CERES-Maize to describe individual leaf area growth, leaf-level C assimilation and partitioning scaled to the canopy level, and growth of reproductive organs.
Abstract: Published in Agron J 104:1523–1537 (2012) Posted online 9 Aug 2012 doi:102134/agronj20110321 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711 All rights reserved No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher E crop and soil simulation models are problem-solving tools that are well suited to addressing today’s multiple agricultural challenges Simulation models provide quantitative descriptions of plant and soil behavior, and calculate their responses to environmental changes, climate variability, and agricultural management Th ey are useful both as decision support tools for on-farm management and for assessment of agricultural policies and practices Many existing models simulate growth and yield of a generic plant Th e growth rate is a function of time (RZWQM, Ahuja et al, 2000) or ambient temperature expressed in thermal units (APSIM, Keating et al, 2003; EPIC, Williams, 1995; CERES, Jones and Kiniry, 1986; CROPGRO, Boote et al, 1998) modifi ed by C availability, and biomass accrues according to intercepted radiation In most cases, diff erent plant species are simulated utilizing appropriate parameter fi les, but the basic processes are the same Globally, corn or maize is one of the most important food crops and is the most important crop with a C4 photosynthetic pathway; only wheat (Triticum aestivum L) and rice (Oryza sativa L) are ahead of maize in terms of total global production Despite the importance of maize, relatively few simulation models have been developed for this crop Among the most widely used models for maize are CERES-Maize (Jones and Kiniry, 1986) and EPIC (Williams, 1995) More recently, Lizaso et al (2011) developed a new maize simulation model, CSM-IXIM, by adapting code from CERES-Maize to describe individual leaf area growth, leaf-level C assimilation and partitioning scaled to the canopy level, and growth of reproductive organs Yang et al (2004) also developed a maize simulation model, Hybrid-Maize, by combining components of CERESMaize with components of INTERCOM and Wofost (van Ittersum et al, 2003) Attempts to assess the impacts of global climate changes on maize production have been made using both CERES-Maize (Iqbal et al, 2011) and EPIC (Gaiser et al, 2011; Brown and Rosenberg, 1999) Stockle et al (1992) modifi ed the EPIC model to assess the impacts of elevated CO2 concentration and associated climate changes Both CERES-Maize and EPIC calculate biomass through radiation use effi ciency parameters Stockle et al (1992) modifi ed EPIC to describe an empirical link between the response of crop biomass production and crop transpiration to changes in the atmospheric CO2 concentration and vapor pressure defi cit In order for researchers to be able to predict the climate impacts realistically, process-level models that incorporate physiological processes (eg, canopy development, phenology, CO2 assimilation, stomatal relations, and transpiration) on a mechanistic level are essential ABSTRACT


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TL;DR: In this paper, the authors use linear or exponential models to describe the relationship between vegetative indices and plant yield, and show that NDVI can be used to predict plant growth and yield.
Abstract: Published in Agron. J. 104:378–387 (2012) Posted online 12 Jan 2012 doi:10.2134/agronj2011.0249 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. T invention of analog-based, pulse-modulated, two-band, active lighting sensors (Beck and Vyse, 1994, 1995) and the equivalent digitally based sensor (Stone et al., 2003, 2005) have contributed to the potential use of these technologies for variable-rate application of N fertilizers. One of the more common reflectance indices used in agriculture is the normalized difference vegetation index (NDVI). The index is computed as (NIR – Red)/(NIR + Red), where NIR is the fraction of emitted near-infrared radiation returned from the sensed area (reflectance) and Red is the fraction of emitted red radiation returned from the sensed area (reflectance). Work by Filella and Penuelas (1994) and Liu et al. (2004a) noted that red edge reflectance can be indicative of plant chlorophyll content and biomass. Kanke et al. (2011) reported that NDVI better detected differences in plant growth, especially at early growth stages, than red edge reflectance. Spectral measurements of plants correlated with numerous physiological and morphological factors affecting growth and yield. Because of the difficulty in accounting for all confounding factors, models for computing N fertilizer rates are generally empirical and plant species specific and do not account for environmental factors, particularly rainfall, and their interactions with plant growth factors. Biggs et al. (2002) proposed a reference strip, where fertilizer is applied at a sufficient rate such that crop yield reaches a response plateau, that would subsequently be used to manage N fertilization. He patented a concept to measure reflectance with an optical sensor of the strip and the adjacent field rate and calculated the N application rate based on the ratio of the two readings (Biggs et al., 2002). The sensors were mounted on a center pivot irrigation system and paired measurements were made on-the-go. Researchers use linear or exponential models to describe the relationship between vegetative indices and plant yield. Linear relationships have been identified between yield and NDVI for corn (Diker et al., 2004), wheat (Nidumolu et al., 2008; Liu et al., 2004b), tomato (Solanum lycopersicum L.) (Bala et al., 2007), cotton lint (Gossypium hirsutum L.) (Plant et al., 2000), and barley (Hordeum vulgare L.) (Kancheva et al., 2007). Multiple linear regression was used for winter wheat (Salazar et al., 2006; Kumar et al., 1999). Exponential relationships were used for NDVI and yield in cotton lint (Plant et al., 2000), winter wheat (Enclona et al., 2004; Raun et al., 2005), spinach (Spinacia oleracea L.) (Jones et al., 2007), canola (Brassica napus L. var. napus) (Osborne, 2007), and corn (Raun et al., 2005). One model incorporated additional variables to account for other confounding factors such as the date of planting (Kumar et al., 1999). A comprehensive theory is needed to account for effects of the growth stage, rate of growth, date of sensing, and environment on crop growth and yield. Raun et al. (2005) recognized that N algorithms should account for the independence of the crop response to additional N and potential maximum yield. As such, they must be measured individually. Because N is highly mobile (Khosla and Alley, 1999), the maximum potential crop yield is temporally and spatially (Girma et al., 2007) variable, and the amount N ABSTRACT


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TL;DR: In this article, the authors compared wood ash and agricultural lime on a clay loam soil with an initial pH of 4.9 and found that wood ash applied at rates equivalent to agricultural lime improved some soil chemical and physical properties and increased crop production.
Abstract: Wood ash has the properties to be an effective liming material, and research is needed to compare its effectiveness relative to agricultural lime on acidic agricultural soils. Wood ash at a calcium carbonate rate of 6.72 t ha−1 was compared with an equivalent rate of agricultural lime on a clay loam soil with an initial pH of 4.9. Replicated plots were managed under a barley (Hordeum vulgare L.)–canola (Brassica rapa L.)–pea (Pisum sativum L.) rotation for 4 yr (2002–2005). Soil pH increased in the order of: wood ash = lime > control (without lime or wood ash). Available soil P increased in the order of: wood ash > lime ≥ control. The effect of wood ash and lime application on pH and available P was greatest in the 0- to 5-cm depth, less but still significant in the 5- to 10-cm depth, and not significant below 10 cm. The effect on soil aggregation was: wood ash > lime > control. Averaged over 4 yr, application of wood ash increased grain yields of barley, canola, and pea by 49, 59, and 55%, respectively, compared to a corresponding increase of 38, 31, and 49% by agricultural lime. The increase in crop yield with wood ash compared with lime is attributed partly to increased P availability in wood ash-amended plots. It is concluded that wood ash applied at rates equivalent to agricultural lime improved some soil chemical and physical properties and increased crop production relative to agricultural lime.

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TL;DR: In this paper, the American Society of Agronomy (ASG) published a survey of the state of the art in sustainable and sustainable food production in the West Texas High Plains, where agriculture accounts for about 28% of the region's economy.
Abstract: Published in Agron. J. 104:1625–1642 (2012) Posted online 12 Sept. 2012 doi:10.2134/agronj2012.0121 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. A long-term goal for any nation must be food security— the basis of a stable society and the foundation of national security. Sustainable and secure food and fi ber production and an economically and ecologically viable agriculture cannot deplete the resources nor destroy the environment on which they depend. Agriculture today faces global challenges including continued population growth with an increasing food demand, changes in composition of food demand from a grain-based diet toward one higher in meat and milk products, depletion of natural resources, dependence on irrigation, and climate change. Th ese issues, combined with unstable economics, a fossil fuel-based energy system, changing government policies and regulations, and competition for land by nonagricultural uses threaten our capacity for food security and sustainable production. Monoculture systems in the United States, with economies of scale and advantages of specialization, have been highly successful at providing an abundance of safe and healthful foods. Such monocultures can put unacceptable stress on ecosystems and natural resources and result in unstable economics (Altieri, 2000). Input costs are now such that a single unfavorable growing season can result in fi nancial collapse for the producer and the loss of future opportunities. Water scarcity and municipal and industrial competition with agriculture for water are becoming a national imperative. Population growth and economic development are projected to have greater impact on water supply by 2025 than projected global climate change (Vorosmarty et al., 2000). Nowhere is this more evident than in the semiarid West Texas High Plains where agriculture accounts for about 28% of the region’s economy (IMPLAN, 2009), but depends heavily on water for irrigation from the Ogallala aquifer at nonsustainable rates of extraction (TWDB, 2007). Once a vast grassland, today this region has about 25% of U.S. cattle on feed and produces about 30% of U.S. cotton, primarily in monoculture systems (USDA-NASS and TDA, 2009). About 70% of this is irrigated cotton. With dependence on irrigation at nonsustainable rates of use, the future of agriculture in this region will not be a continuation of traditional practices. Although crop rotations have long been known for complementary benefi ts, eff ects of irrigation and the deliberate integration of crops and livestock to achieve complementary benefi ts on system sustainability is less well known. Krall and Schuman (1996) suggested that integrated crop and livestock systems can represent an ecologically and economically ABSTRACT

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TL;DR: In this paper, the authors evaluated the performance of N, P, and K application on the EONR, EOPR, and EOKR at 11 site-seasons.
Abstract: Sorghum [Sorghum bicolor (L.) Moench] is important for smallholder production in semiarid parts of Uganda. Grain yields are low because of low soil fertility. Little fertilizer is used. Yield response to N, P, and K application, economically optimal rates for N, P, and K (EONR, EOPR, and EOKR, respectively), and N use efficiency (NUE) were evaluated at 11 site-seasons. Mean sorghum yield with no N applied (N0) was 0.69 Mg ha−1 and was consistently increased by a mean of 230% with N application. Mean EONRs were 34 to 18 kg ha−1 N with fertilizer use cost to grain price ratios (CPs) of 10 to 30, respectively. Mean EOPRs were 11 to 2 kg ha−1 P with CPs of 10 to 50, respectively. Sorghum did not respond to K application. Net economic returns were greater for N than P application. Mean aboveground biomass N with 0 and 90 kg ha−1 N applied was 31.3 and 75.9 kg ha−1, respectively. Grain N concentration, N harvest index, and internal NUE at the EONR were 1.67%, 53.2%, and 31.8 kg kg−1, respectively, and higher than for N0. Mean recovery efficiency, partial factor productivity, and agronomic efficiency declined with increased N rate and were 135%, 79 kg kg−1, and 52 kg kg−1, respectively, at the EONR. The profit potential of fertilizer N use is high for smallholder sorghum production in Uganda. Policy interventions to reduce fertilizer cost and improve grain marketing efficiency will enable smallholders to increase fertilizer use for substantial increases in sorghum production

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TL;DR: In this article, the authors used the analysis of variance approach by imposing a response function to find the yield-maximizing N rate for switchgrass, and the results have varied depending on spatial and temporal factors.
Abstract: Published in Agron. J. 104:1579–1588 (2012) Posted online 24 Aug. 2012 doi:10.2134/agronj2012.0179 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. S is a warm-season, perennial grass species largely recognized for its potential as a herbaceous lignocellulosic biomass crop in North America (Vogel, 1996). Switchgrass can achieve high yields with minimal input management on land that is considered marginal for crop production (McLaughlin and Kszos, 2005). For instance, nutrients such as N move from the aboveground biomass into the root system following crop senescence, so harvesting after senescence minimizes the need for nutrient replacement (Parrish and Fike, 2005); however, annual applications of N fertilizer are needed to produce yields large enough to make growing switchgrass for lignocellulosic biomass economically viable (Heaton et al., 2004). Little attention, however, has been given to determining the N rate that maximizes producers’ profits. Several agronomic studies have estimated yield-maximizing N rates for switchgrass by performing an analysis of variance on the mean yield response to various N rates, and the results have varied depending on spatial and temporal factors. For example, Lemus et al. (2008) tested the switchgrass yield response to N for a 5-yr, large-field experiment in Iowa and found that yield was maximized when 112 kg N ha–1 was applied. With 3 yr of data from Oklahoma, Thomason et al. (2005) found that 448 kg N ha–1 achieved the highest annual switchgrass yield when the crop was harvested multiple times per year. Mulkey et al. (2006) conducted a 5-yr experiment on the switchgrass response to N on land enrolled in the Conservation Reserve Program (CRP) in South Dakota and concluded that the yieldmaximizing N rate was 56 kg N ha–1. Mooney et al. (2009) found that the yield-maximizing N rate for switchgrass grown for 3 yr on poorly drained soils in West Tennessee was 200 kg N ha–1 and the yield-maximizing N rate for switchgrass grown on moderately to well-drained soils was 67 kg N ha–1. Because previous studies had discounted the influence of soil quality and landscape on switchgrass production (Fike et al., 2006), the variation in yield-maximizing N rates on different soil types and landscapes found by Mooney et al. (2009) was an important contribution to the literature. Wullschleger et al. (2010) determined that more research is needed on this issue. The analysis of variance approach is useful to provide some insight into optimal N rates but excludes the possibility of the optimal N rate being between the discrete N rates in an experiment. A few studies have gone beyond using the analysis of variance approach by imposing a response function to find the yield-maximizing N rate for switchgrass. Muir et al. (2001) estimated the yield-maximizing N rate at 168 kg ha–1 for ‘Alamo’ switchgrass produced in Texas using linear and quadratic yield response functions. Vogel et al. (2002) estimated a quadratic response function for 2 yr of switchgrass data from Iowa and Nebraska and found a yield-maximizing N rate of 120 kg N ha–1. Recently, Haque et al. (2009) found the yieldand profitmaximizing N rate to be 65 kg N ha–1 using 3 yr of switchgrass yield data from Oklahoma. The three yield response functions considered by Haque et al. (2009) were the linear, quadratic, and linear response plateau. The linear response plateau function was ABSTRACT Little is known about how yieldand profit-maximizing N rates of switchgrass (Panicum virgatum L.) respond to environmental influences. The objective of this research was to determine the most suitable yield response functions and profit-maximizing N rates of switchgrass grown on four landscapes in Tennessee. Research was conducted in West Tennessee during a 7-yr period on four landscapes including: (i) a well-drained level upland (WDLU), (iii) a wellto moderately well-drained floodplain (WDFP), (iii) a moderate to somewhat poorly drained eroded sloping upland (MDSU), and (iv) a poorly drained floodplain (PDFP). The yield response functions considered were the quadratic, the quadratic-plus-plateau, the linear response plateau, and the linear response stochastic plateau. The most suitable response function for each landscape was used to calculate the profit-maximizing N rate. The linear response stochastic plateau function was the most suitable for the WDFP, WDLU, and MDSU landscapes, and the quadratic function was the most suitable for the PDFP landscape. The most suitable yield response function and the yieldand profit-maximizing N rates for switchgrass were sensitive across landscapes. Overapplication of N and a decrease in farmers’ net returns were predicted when a less suitable response function was selected to determine optimal N rates.