Determining the most important physiological and agronomic traits contributing to maize grain yield through machine learning algorithms: a new avenue in intelligent agriculture

Prediction is an attempt to accurately forecast the outcome of a specific situation while using input information obtained from a set of variables that potentially describe the situation. They can be used to project physiological and agronomic processes; regarding this fact, agronomic traits such as yield can be affected by a large number of variables. In this study, we analyzed a large number of physiological and agronomic traits by screening, clustering, and decision tree models to select the most relevant factors for the prospect of accurately increasing maize grain yield. Decision tree models (with nearly the same performance evaluation) were the most useful tools in understanding the underlying relationships in physiological and agronomic features for selecting the most important and relevant traits (sowing date-location, kernel number per ear, maximum water content, kernel weight, and season duration) corresponding to the maize grain yield. In particular, decision tree generated by C&RT algorithm was the best model for yield prediction based on physiological and agronomical traits which can be extensively employed in future breeding programs. No significant differences in the decision tree models were found when feature selection filtering on data were used, but positive feature selection effect observed in clustering models. Finally, the results showed that the proposed model techniques are useful tools for crop physiologists to search through large datasets seeking patterns for the physiological and agronomic factors, and may assist the selection of the most important traits for the individual site and field. In particular, decision tree models are method of choice with the capability of illustrating different pathways of yield increase in breeding programs, governed by their hierarchy structure of feature ranking as well as pattern discovery via various combinations of features.Avat Shekoofa, Yahya Emam, Navid Shekoufa, Mansour Ebrahimi, Esmaeil Ebrahimi

Aquaporin Activity to Improve Crop Drought Tolerance

In plants, aquaporins (AQP) occur in multiple isoforms in both plasmalemma and tonoplast membranes resulting in regulation of water flow in and out of cells, and ultimately, water transfer through a series of cells in leaves and roots. Consequently, it is not surprising that physiological and molecular studies have identified AQPs as playing key roles in regulating hydraulic conductance in roots and leaves. As a result, the activity of AQPs influences a range of physiological processes including phloem loading, xylem water exit, stomatal aperture and gas exchange. The influence of AQPs on hydraulic conductance in plants is particularly important in regulating plant transpiration rate, particularly under conditions of developing soil water-deficit stress and elevated atmospheric vapor pressure deficit (VPD). In this review, we examine the impact of AQP activity and hydraulic conductance on crop water use and the identification of genotypes that express soil water conservation as a result of these traits. An important outcome of this research has been the identification and commercialization of cultivars of peanut (Arachis hypogaea L.), maize (Zea mays L.), and soybean (Glycine max (Merr) L.) for dry land production systems

Application of supervised feature selection methods to define the most important traits affecting maximum kernel water content in maize

This study presents the results of applying supervised feature selection algorithms in the selection of the most important traits contributing to the maximum kernel water content (MKWC) as a major yield component. Data were obtained from a field experiment conducted during 2008 growing season, at the Experimental Farm of the College of Agriculture, Shiraz University, and from the literature. Experiments on the subject of sink/source relationships in maize were collected from twelve fields (as records) of different parts of the world, differing in 23 characteristics (features). The feature selection algorithm demonstrated that 15 features including: planting date (days), countries (Iran, Argentina, India, USA, Canada), hybrid types, Phosphorous fertilizer applied (kg ha-1), final kernel weight (mg), soil type, season duration (days), days to silking, leaf dry weight (g plant-1), mean kernel weight (mg), cob dry weight (g plant-1), kernel number per ear, grain yield (g m-2), nitrogen applied (kg ha-1), and duration of the grain filling period (0C day) were the most effective traits in determining maximum kernel water content. Among the effective traits (features), planting date (days) revealed to be the critical one. Hybrids and countries were the second most important affecting factors on the maize kernel water content. For the first time, our results showed that features classification by supervised feature selection algorithms can provide a comprehensive view on distinguishing the important traits which contribute to maize kernel water content and yield. This study opened a new vista in maize physiology using feature selection and data mining methods and would be beneficial to newcomers of this fieldA. Shekoofa, Y. Emam, M. Ebrahimi, E. Ebrahimiehttp://www.cropj.com/february2011.htm

Allelopathic Impacts of Cover Crop Species and Termination Timing on Cotton Germination and Seedling Growth

The integration of cover crops into cotton (Gossypium hirsutum, L.) production remains challenging. One potential negative impact of cover crops on cotton is allelopathy. Proper selection of cover crop species and termination timing could potentially reduce the impacts of allelopathy on cotton seedlings. Two studies were conducted to determine cotton germination and growth sensitivity to cover crop leachate, which were measured using (I) five cover crops species, including: oats (Avena sativa L.), hairy vetch (Vicia Villosa), winter pea (Lathyrus hirsutus), winter wheat (Triticum aestivum), and annual rye (Lolium multiflorum), and (II) a blend of cover crops at four termination timings, including: at planting, three weeks prior to planting, six weeks prior to planting, and a split termination, where a 25 cm band in the top of the bed was terminated six weeks prior to planting, and the remaining cover crop was terminated at planting (referred to as strip 6-wk). Samples for Experiment I were collected on May 24th and for Experiment II on March 22nd (Strip/6-wk and 6-wk), April 30th (3-wk), and May 11th (at planting) in 2018. The effect of 0 (deionized water), 25, and 50 (v/v) cover crop leachate extract on cotton seed germination was evaluated in a series of controlled environmental studies. All cover crop species’ leachates negatively impacted cotton germination and seedling growth (p < 0.05). Germination inhibition rates declined numerically by species, with winter pea ≥ hairy vetch ≥ oats ≥ annual rye ≥ winter wheat at the 50 v/v concentrations. Winter pea germination inhibition on cotton equaled 47.0% and cotton radicle length was decreased by 62.8%. Termination at planting suppressed cotton germination more than the other termination timings, with the 50 v/v treatment resulting in a germination inhibition of 60.0%. Proper selection of cover crop species and termination timing prior to planting cotton will be critical in maximizing the benefits and minimizing the risks of a cover crop

Variation among maize hybrids in response to high vapor pressure deficit at high temperatures

Temperature and vapor pressure deficit (VPD) are two important environmental factors influencing stomatal conductance and transpiration. A limited transpiration rate (TRlim) trait expressed under high VPD has been shown to offer an approach to increase crop yield in water-limited areas. The benefit of the TRlim trait is that it lowers the effective VPD under which plants lose water and so conserves soil water to support crop growth for use during drought periods later in the growing season. Previous studies at moderate temperatures (32 degrees C and lower) identified 12 maize (Zea mays L.) hybrids that express the TRlim trait. A critical question is whether the TRlim trait is also expressed by these hybrids under temperatures up to 38 degrees C, which are relevant in environments where maize may be grown. Five hybrids failed to express the TRlim trait at 38 degrees C but seven hybrids had sustained expression of the trait at 38 degrees C. The loss of expression of the TRlim response in the five hybrids was found to occur in the very narrow range of temperature increase from 36 to 38 degrees C. The genetic differences in water use among these maize hybrids could be useful in selecting hybrids that are especially well adapted for temperature conditions in a targeted production area

Allelopathic Impact of Cover Crop Species on Soybean and Goosegrass Seedling Germination and Early Growth

Cover crops can provide a variety of benefits to an agricultural system: weed suppression, soil quality improvement, and soil water infiltration. Although there is ample research documenting weed suppression from cover crops, the mechanics of the suppression are not implicitly understood. Along with the aforementioned positive attributes, negative allelopathic effects on row crops planted into cover crop systems have been documented. The objective of this study was to evaluate the allelopathic potential of certain cover crop species on soybean (Glycine max L.) and goosegrass (Eleusine indica L.) germination and early seedling growth under controlled environments in petri dish and pot experiments. Leachates from above-ground biomass of five cover crop species, wheat (Triticum aestivum L.), cereal rye (Secale cereale), hairy vetch (Vicia villosa), crimson clover (Trifolium incarnatum L.), and canola (Brassica napus L.), from two locations (East and Middle Tennessee) were extracted and applied at 0 (water) and 50 v/v. In experiment I, both soybean and goosegrass seeds were examined, and, in experiment II, only soybean seeds were examined under the application of cover crop leachates. Most cover crop leachates from both locations significantly reduced the soybean seedling root length (p < 0.01). Overall, the application of canola extract (East Tennessee) suppressed soybean seed germination the most (28%) compared to deionized water. For goosegrass, the wheat cover crop leachate significantly reduced seedling root length (p < 0.01). In experiment II, the soybean root nodulation was significantly increased with the wheat extract treatment compared to deionized water. While the results indicate that the location and environment may change cover crop species allelopathic potential, the wheat cover crop leachate had the most potent allelopathic impact on goosegrass germination and growth; however, had the lowest observed adverse effect on our tested row crop, soybean