Wheat (Triticum aestivum L.) grain yield response to plant density is inconsistent, and the mechanisms driving this response are unclear. A better understanding of the factors governing this relationship could improve plant density recommendations according to specific environmental and genetics characteristics. Therefore, the aims of this paper were to: i) execute a synthesis-analysis of existing literature related to yield-plant density relationship to provide an indication of the need for different agronomic optimum plant density (AOPD) in different yield environments (YEs), and ii) explore a data set of field research studies conducted in Kansas (USA) on yield response to plant density to determine the AOPD at different YEs, evaluate the effect of tillering potential (TP) on the AOPD, and explain changes in AOPD via variations in wheat yield components. Major findings of this study are: i) the synthesis-analysis portrayed new insights of differences in AOPD at varying YEs, reducing the AOPD as the attainable yield increases (with AOPD moving from 397 pl m^-2 for the low YE to 191 pl m^-2 for the high YE); ii) the field dataset confirmed the trend observed in the synthesis-analysis but expanded on the physiological mechanisms underpinning the yield response to plant density for wheat, mainly highlighting the following points: a) high TP reduces the AOPD mainly in high and low YEs, b) at canopy-scale, both final number of heads and kernels per square meter were the main factors improving yield response to plant density under high TP, c) under varying YEs, at per-plant-scale, a compensation between heads per plant and kernels per head was the main factor contributing to yield with different TP.

The metrica R package (Correndo et al., 2022) is an open-source software designed to facilitate the quantitative and visual assessment of prediction performance of point-forecast simulation models for continuous (regression) and categorical variables (classification). The package ensembles a series of 80+ functions that account for multiple aspects of the agreement between predicted and observed values. Without the need of advanced skills on programming, metrica enables users to automate the estimation of multiple prediction performance metrics including goodness of fit, error metrics, error decomposition, model efficiency, indices of agreement, and to produce stylish data visualization outputs. This article introduces metrica, an R package developed with the main objective of contributing to transparent and reproducible evaluation of point-forecast models performance.

Identifying nematode damage in large soybean areas is not always achievable in a practical way. Multispectral reflectance sensors have not been thoroughly evaluated to detect nematode damage in soybeans (Glycine max L.). The main research aims of this study were to: (i) determine the bivariate relationship between individual spectral bands and vegetation indices (VIs) relative to soybean conditions (symptomatic versus asymptomatic), and (ii) to select the best model for identifying plant conditions using three algorithms (logistic regression—LR, random forest—RF, conditional inference tree—CIT) and three options for data input using bands, vegetation indices (VIs), and bands plus VIs. The trial was conducted in Brazil on three on-farm soybean fields presenting different species of nematode infestation. Multispectral imagery was obtained using a drone-mounted MicaSense RedEdge^® sensor. At each sampling, georeferenced point nematode infestation and spectral measurements of soybean plants were retrieved for the classification of symptomatic and asymptomatic areas, according to the threshold level adopted. Bivariate analysis of variance (ANOVA), LR, RF, and CIT were used to select the multispectral bands/VIs that discriminated among symptomatic and asymptomatic plants, assessing the best model via their respective parameters for accuracy, sensitivity, and specificity. The greatest classification accuracy (>0.70) was achieved when using the CIT algorithm with the spectral bands only, with green (560 ± 20 nm) and near-infrared (840 ± 40 nm) included as the main spectral input variables in the model. These results demonstrate the potential of combining remotely sensed data and machine learning to distinguish nematode-symptomatic and asymptomatic soybean plants.

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.

Fertilizer N management can mitigate N ₂ O emissions but complex soil‐weather conditions modulate the mitigation potential. Conditional inference tree (CIT) is a machine learning method able to untangle complex interactions while providing an interpretable model. The goals of this study were (a) to assess the effect of N fertilizer on N ₂ O emissions, and to use CIT to identify (b) the main soil‐weather drivers of daily N ₂ O hot moments and (c) fertilizer management options to mitigate them. The study was conducted in 2 yr in no‐till corn ( Zea mays L.) with seven combinations of N source and placement tested. Daily N ₂ O emissions were measured with vented chambers, and soil temperature and water‐filled pore space (WFPS) were measured near the chambers on the same days of gas sampling. Overall, 2013 was drier with lower N ₂ O emissions than 2014. Cumulative N ₂ O losses differed across treatments and years, with broadcast emitting more in 2014 than in 2013, and only subsurface‐banded fertilizer with a nitrification inhibitor (NI) consistently abated N ₂ O losses. The main hot moment conditions were within ∼80 d of fertilizer application when soil temperature >15 °C and WFPS >57%. Under these conditions, NI abated losses by 50% compared with fertilizer alone. The machine learning approach used here could be used in larger datasets to elucidate environment‐specific drivers of N ₂ O hot moments and potential fertilizer mitigation practices under different soil, weather, and management conditions.