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

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

Limited-transpiration response to high vapor pressure deficit in crop species

Water deficit under nearly all field conditions is the major constraint on plant yields. Other than empirical observations, very little progress has been made in developing crop plants in which specific physiological traits for drought are expressed. As a consequence, there was little known about under what conditions and to what extent drought impacts crop yield. However, there has been rapid progress in recent years in understanding and developing a limited-transpiration trait under elevated atmospheric vapor pressure deficit to increase plant growth and yield under water-deficit conditions. This review paper examines the physiological basis for the limited-transpiration trait as result of low plant hydraulic conductivity, which appears to be related to aquaporin activity. Methodology was developed based on aquaporin involvement to identify candidate genotypes for drought tolerance of several major crop species. Cultivars of maize and soybean are now being marketed specifically for arid conditions. Understanding the mechanism of the limited-transpiration trait has allowed a geospatial analyses to define the environments in which increased yield responses can be expected. This review highlights the challenges and approaches to finally develop physiological traits contributing directly to plant improvement for water-limited environments