Cover crops (CCs) have shown great potential for suppressing annual weeds within agronomic cropping systems across the United States. However, the weed suppressive potential of CCs may be moderated by environmental and management factors that are specific to certain geographic areas and their associated characteristics. This may be particularly true within the U.S. Southeast, where higher mean annual temperature and precipitation generate favorable conditions for both CC and weed growth. To understand the effects of this regional context on CCs and weeds, a meta-analysis examining paired comparisons of weed biomass and/or weed density under CC and bare ground conditions from studies conducted within the Southeast was conducted. Data were identified and extracted from 28 journal articles in which weed biomass and/or weed density were measured along with cash crop yield data, if they were provided. Fourteen studies provided 142 comparisons for weed biomass; 23 studies provided 139 comparisons for weed density; and 22 studies, pooled over both weed response variables, provided 144 comparisons for cash crop yield. CCs had a negative effect on weed density (P = 0.0016) but no effect on either weed biomass (P = 0.16) or cash crop yield (P = 0.88). The mean relative reduction in weed density under CCs was 44%. Subsequent analyses indicated that CC biomass was the key factor associated with this reduction. Weed density suppression was linearly related to CC biomass; a 50% decrease in weed density was associated with 6,600 kg ha−1 of CC biomass. Edaphic, geographic, and other management factors had no bearing on this suppressive effect. This highlights the importance of generating adequate CC biomass if weed suppression is the primary objective of CC use and the potential for CCs to reduce weed density over diverse soil, climate, and farm management conditions.

The utilization of remote sensing in agriculture has great potential to change the methods of field scouting for weeds. Previous remote sensing research has been focused on the ability to detect and differentiate between species. However, these studies have not addressed weed density variability throughout a field. Furthermore, the impact of changing phenology of crops and weeds within and between growing seasons has not been investigated. To address these research gaps, field studies were conducted in 2016 and 2017 at the Horticultural Crops Research Station near Clinton, NC. Two problematic weed species, Palmer amaranth (Amaranthus palmeri S. Watson) and large crabgrass [Digitaria sanguinalis (L.) Scop.], were planted at four densities in soybean [Glycine max (L.) Merr.]. Additionally, these weed densities were grown in the presence and absence of the crop to determine the influence of crop presence on the detection and discrimination of weed species and density. Hyperspectral data were collected over various phenological time points in each year. Differentiation between plant species and weed density was not consistent across cropping systems, phenology, or season. Weed species were distinguishable across more spectra when no soybean was present. In 2016, weed species were not distinguishable, while in 2017, differentiation occurred at 4 wk after planting (WAP) and 15 WAP when weeds were present with soybean. When soybean was not present, differentiation occurred only at 5 WAP in 2016 and at 3 WAP through 15 WAP in 2017. Differentiation between weed densities did occur in both years with and without soybean present, but weed density could be differentiated across more spectra when soybean was not present. This study demonstrates that weed and crop reflectance is dynamic throughout the season and that spectral reflectance can be affected by weed species and density.

The effect of plant phenology and canopy structure of four crops and four weed species on reflectance spectra were evaluated in 2016 and 2017 using in situ spectroscopy. Leaf-level and canopy-level reflectance were collected at multiple phenologic time points in each growing season. Reflectance values at 2 wk after planting (WAP) in both years indicated strong spectral differences between species across the visible (VIS; 350–700 nm), near-infrared (NIR; 701–1,300 nm), shortwave-infrared I (SWIR1; 1,301–1,900 nm), and shortwave-infrared II (SWIR2; 1,901–2,500 nm) regions. Results from this study indicate that plant spectral reflectance changes with plant phenology and is influenced by plant biophysical characteristics. Canopy-level differences were detected in both years across all dates except for 1 WAP in 2017. Species with similar canopy types (e.g., broadleaf prostrate, broadleaf erect, or grass/sedge) were more readily discriminated from species with different canopy types. Asynchronous phenology between species also resulted in spectral differences between species. SWIR1 and SWIR2 wavelengths are often not included in multispectral sensors but should be considered for species differentiation. Results from this research indicate that wavelengths in SWIR1 and SWIR2 in conjunction with VIS and NIR reflectance can provide differentiation across plant phenologies and, therefore should be considered for use in future sensor technologies for species differentiation.

Field studies were conducted in 2016 and 2017 at Clinton, NC, to quantify the effects of season-long interference of large crabgrass [Digitaria sanguinalis (L.) Scop.] and Palmer amaranth (Amaranthus palmeri S. Watson) on ‘AG6536’ soybean [Glycine max (L.) Merr.]. Weed density treatments consisted of 0, 1, 2, 4, and 8 plants m−2 for A. palmeri and 0, 1, 2, 4, and 16 plants m−2 for D. sanguinalis with (interspecific interference) and without (intraspecific interference) soybean to determine the impacts on weed biomass, soybean biomass, and seed yield. Biomass per square meter increased with increasing weed density for both weed species with and without soybean present. Biomass per square meter of D. sanguinalis was 617% and 37% greater when grown without soybean than with soybean, for 1 and 16 plants m−2 respectively. Biomass per square meter of A. palmeri was 272% and 115% greater when grown without soybean than with soybean for 1 and 8 plants m−2, respectively. Biomass per plant for D. sanguinalis and A. palmeri grown without soybean was greatest at the 1 plant m−2 density. Biomass per plant of D. sanguinalis plants across measured densities was 33% to 83% greater when grown without soybean compared with biomass per plant when soybean was present for 1 and 16 plants m−2, respectively. Similarly, biomass per plant for A. palmeri was 56% to 74% greater when grown without soybean for 1 and 8 plants m−2, respectively. Biomass per plant of either weed species was not affected by weed density when grown with soybean due to interspecific competition with soybean. Yield loss for soybean grown with A. palmeri ranged from 14% to 37% for densities of 1 to 8 plants m−2, respectively, with a maximum yield loss estimate of 49%. Similarly, predicted loss for soybean grown with D. sanguinalis was 0 % to 37% for densities of 1 to 16 m−2 with a maximum yield loss estimate of 50%. Soybean biomass was not affected by weed species or density. Results from these studies indicate that A. palmeri is more competitive than D. sanguinalis at lower densities, but that similar yield loss can occur when densities greater than 4 plants m−2 of either weed are present.

Field studies were conducted in 2016 and 2017 in Clinton, NC, to determine the interspecific and intraspecific interference of Palmer amaranth (Amaranthus palmeri S. Watson) or large crabgrass [Digitaria sanguinalis (L.) Scop.] in ‘Covington’ sweetpotato [Ipomoea batatas (L.) Lam.]. Amaranthus palmeri and D. sanguinalis were established 1 d after sweetpotato transplanting and maintained season-long at 0, 1, 2, 4, 8 and 0, 1, 2, 4, 16 plants m−1 of row in the presence and absence of sweetpotato, respectively. Predicted yield loss for sweetpotato was 35% to 76% for D. sanguinalis at 1 to 16 plants m−1 of row and 50% to 79% for A. palmeri at 1 to 8 plants m−1 of row. Weed dry biomass per meter of row increased linearly with increasing weed density. Individual dry biomass of A. palmeri and D. sanguinalis was not affected by weed density when grown in the presence of sweetpotato. When grown without sweetpotato, individual weed dry biomass decreased 71% and 62% from 1 to 4 plants m−1 row for A. palmeri and D. sanguinalis, respectively. Individual weed dry biomass was not affected above 4 plants m−1 row to the highest densities of 8 and 16 plants m−1 row for A. palmeri and D. sanguinalis, respectively.

Studies were conducted at six locations across North Carolina to determine tolerance of ‘Sunbelt’ grape (bunch grape) and muscadine grape (‘Carlos’, ‘Triumph’, ‘Summit’) to indaziflam herbicide. Treatments included indaziflam (0, 50, 73 g ai ha–1) or flumioxazin (213 g ai ha–1) applied alone in April, and sequential applications of indaziflam (36, 50, 73 g ai ha–1) or flumioxazin (213 g ai ha–1) applied in April followed by the same rate applied in June. No crop injury was observed across locations. Muscadine yield was not affected by herbicide treatments. Yield of ‘Sunbelt’ grape increased with sequential applications of indaziflam at 73 g ha–1 when compared to a single application of indaziflam at 50 g ha–1 or flumioxazin at 213 g ha–1 in 2015. Sequential applications of flumioxazin at 213 g ha–1 reduced ‘Sunbelt’ yield compared to a single application of indaziflam at 73 g ha–1 in 2016. Trunk cross-sectional area was unaffected by herbicide treatments. Fruit quality (soluble solids concentration, titratable acidity, and pH) for muscadine and bunch grape was not affected by herbicide treatments. Indaziflam was safe to use at registered rates and could be integrated into weed management programs for southern US vineyards.

A field study was conducted in 2014 and 2015 in an established 5-yr old commercial blackberry planting to determine the effect of vegetation-free strip width (VFSW) on ‘Navaho’ blackberry vegetative growth, yield and fruit quality parameters, identify the optimum VFSW for blackberry plantings in the southeastern USA, and provide practical groundcover management recommendations that can increase the productivity of blackberry plantings. In Fall 2013, tall fescue was seeded in-row and allowed to establish. In Spring 2014, VFSW treatments (0, 0.6, 0.9, 1.2, and 1.8 m) were established in a randomized complete block statistical design with four replications. Blackberry growth measurements included primocane and floricane number, cane diam, individual fruit weight and yield. Fruit quality measurements included, soluble solids concentration (SSC), titratable acidity (TA) and pH. Primocane number increased with increasing VFSW in both years. Floricane number increased with increasing VFSW in 2014. Primocane diam decreased with increasing VFSW in 2014 but had a quadratic response in 2015. Berry weight and cumulative yield increased with increasing VFSW in both years. The only berry quality component affected by VFSW was pH, which decreased as VFSW increased. Results indicate that widening the VFSW in blackberry from the current recommendation of 1.2 m to 1.8 m could provide growers a means to increase plant growth, berry weight, and cumulative yield blackberry of a planting.

Tomato rootstocks have been successfully used for eggplant production. However, the safety of herbicides registered in tomato has not been tested on grafted eggplant, which is a combination of tomato rootstock and eggplant scion. Greenhouse and field experiments were conducted to determine response of grafted eggplant on tomato rootstock to napropamide, metribuzin, halosulfuron, trifluralin, S-metolachlor, and fomesafen herbicides. In greenhouse experiments, herbicide treatments included pretransplant S-metolachlor (400 and 800 g ai ha−1), pre- or posttransplant metribuzin (140 and 280 g ai ha−1), and posttransplant halosulfuron (18 and 36 g ai ha−1). In field experiments, herbicide treatments included pretransplant fomesafen (280 and 420 g ai ha−1), halosulfuron (39 and 52 g ha−1), metribuzin (280 and 550 g ha−1), napropamide (1,120 and 2,240 g ai ha−1), S-metolachlor (800 and 1,060 g ha−1), and trifluralin (560 and 840 g ai ha−1). The eggplant cultivar ‘Santana' was used as the scion and nongrafted control, and two hybrid tomatoes ‘RST-04−106-T' and ‘Maxifort' were used as rootstocks for grafted plants. In both greenhouse and field experiments, there was no difference between grafted and nongrafted eggplant in terms of injury caused by herbicides. Metribuzin posttransplant at 140 and 280 g ha−1 caused 94 and 100% injury to grafted and nongrafted eggplant 4 wk after treatment. In field experiments, pretransplant fomesafen, napropamide, S-metolachlor, and trifluralin caused less than 10% injury and no yield reduction in grafted and nongrafted eggplant. However, metribuzin caused injury and yield reduction in both grafted and nongrafted eggplant. Metribuzin at 550 g ha−1 caused 60 and 81% plant stand loss in 2013 and 2014, respectively. Halosulfuron reduced yield 24% in both grafted and nongrafted eggplant compared to nontreated control in 2013 but did not reduce yield in 2014. The pretransplant S-metolachlor, napropamide, fomesafen, and trifluralin are safe to use on eggplant grafted onto tomato rootstock, and will be a valuable addition to the toolkit of eggplant growers.

The pattern center of an electron backscatter diffraction (EBSD) image indicates the relative position of the image with reference to the interaction volume of the sample. As interest grows in high-resolution EBSD techniques, accurate knowledge of this position is essential for precise interpretation of the EBSD features. In a typical EBSD framework, Kikuchi bands are recorded on a phosphor screen. If the flat phosphor were instead shaped as a sphere, with its center at the specimen's electron interaction volume, then the incident backscattered electrons would form Kikuchi bands on that sphere with parallel band edges centered on great circles. In this article, the authors present a method of pattern center (PC) refinement that maps bands from the planar phosphor onto a virtual spherical screen and measures the deviation of bands from a great circle and from possessing parallel edges. Potential sources of noise and error, as well as methods for reducing these, are discussed. Finally, results are presented on the application of the PC algorithm to two types of simulated EBSD patterns and two experimental setups, and the resolution of the method is discussed.

In a recent article in this journal Andrew Chignell assesses attempts by Marilyn McCord Adams and Eleonore Stump to resolve the problem that infant suffering poses for theistic belief, concluding that while the theodicy of each is inadequate in its current form, both can be satisfactorily amended. I argue that (1) Chignell fails to show that the theodicy of either Adams or Stump is inadequate and that (2) since Chignell's revisions are based on assumptions about God and evil held by few, such revisions are of little value as responses to the actual challenge infant suffering poses for theistic belief.

In a recent article in this journal, Michael Murray and Kurt Meyers offer us (among other things) two innovative and thought-provoking responses to the important question of why God would, even occasionally, refrain from giving us that which he can and would like to give us until we request that he do so: to help the believer learn more about God and thus become more like him and to help the believer realize she is dependent on God. I argue that neither explanation is adequate and thus that more work on this significant topic remains to be done.

Current discussions of the ‘problem of evil’ vary greatly in atleast two ways. First, those involved in such discussions often differ on the exact nature of the problem. Some see it as primarily logical (deductive), some as primarily evidential (inductive), and still others as primarily psychological (personal, pastoral).1 Second, those involved in such discussions differ radically on what is required of the theist in response. Some claim that unless the theist can offer an explanation for evil (a theodicy) that is satisfying to rational individuals in general, theistic belief is rendered unjustified.2 Others agree that the theist must offer a theodicy, but deny that such an explanation must be found convincing by most if theistic belief is to remain justified.3 And still others deny that the theist is required to offer any sort of explanation (theodicy), arguing instead that the theist need only defend the logical consistency of simultaneous belief in the existence of evil and God.4

The problem of evil normally discussed in philosophical theology is concerned with the pain and suffering experienced in this life. Why do so many innocent children die slow, torturous deaths as the result of disease, famine or earthquakes? Why do so many seemingly innocent adults suffer as the result of the greed, indifference or perversity of others? If God is all-good, then he certainly does not want such suffering. If God is all-powerful, he should be able to do away with such evils. Thus, must we not conclude that the existence of such evil counts against belief in the existence of an all-loving, all-powerful God?

To say that God is omniscient is normally to say that God knows all true propositions and none that are false. But what exactly is knowable? Some believe that God possesses only ‘present knowledge’ (PK). All that is know-able is that which is (or has been) actual and that which follows deterministically from it. Others believe that God possesses ‘simple foreknowledge’ (SFK). God can also know what will actually happen, including what humans will freely do. And still others believe that God possesses ‘middle knowledge’ (MK). God is able to know not only what will happen in the actual world or what could happen in all worlds but also what would in fact happen in every possible situation, including what every possible free creature would in fact do in every possible situation in which that creature could find itself.

Traditionally, Calvinists (those in the Reformed tradition) have placed a great deal of emphasis on their belief that

(1)God has total control over all earthly affairs.

To say that God is omniscient, most philosophers and theologians agree, is to say that he knows all true propositions and none that are false. But there is a great deal of disagreement about what is knowable. Some believe that God's knowledge is limited to everything that is (or has been) actual and that which will follow deterministically from it. He knows, for example, exactly what Caesar was thinking when he crossed the Rubicon and how many horses he had in his army that day. And he knows exactly how Gorbachev feels about the use of nuclear weapons. And since he knows how the ‘laws of nature’ (which he has purportedly created) function, he knows, for example, how certain weather systems will develop and what their effects will be on certain natural environments. But with respect to any future state of affairs which includes free human decision-making as a causal component, God is said not to know what will occur. God, as the ultimate psychoanalyst or behaviourist, can with great accuracy predict what we will freely decide to do in the future in many cases. He might well, for example, be able to predict quite accurately who will win the 1988 Presidential election. But a God who possesses only ‘present knowledge’ (PK) cannot know who will win. Given that the election in question is dependent on free choices which have yet to be made, there is presently nothing for God to know.