316 L stainless steel, high density dislocations (~1.14 × 1015 m−2) obtained by selective laser melting process that play an important role in high yield strength. Dislocation slip and twinning ...during entire plastic deformation process, which maintained strain hardening rate at an ideal level and obtained outstanding ductility and resulting high yield ratio.
The potential yield of crops is not usually realised on farms creating yield gaps. Methods are needed to diagnose yield gaps and to select interventions. One method is the boundary line model in ...which the upper bound of a plot of yield against a potentially limiting factor is viewed as the most efficient response to that factor and anything below it has a yield gap caused by inefficiency of other factors. If many factors are studied, the cause of the yield gap can be identified (yield gap analysis, YGA). Though the boundary line is agronomically interpretable, its estimation and statistical inference are not straightforward and there is no standard method to fit it to data.
We review the different methods used to fit the boundary line, their strengths and weaknesses, interpretation, factors influencing the choice of method and its impact on YGA.
We searched for articles that used boundary lines for YGA, using the Boolean “Boundary*” AND “Yield gap*” in the Web of Science.
Methods used to fit boundary lines include heuristic methods (visual, Binning, BOLIDES and quantile regression) and statistical methods (Makowski quantile regression, censored bivariate model and stochastic frontier analysis). In contrast to heuristic methods, which in practice require ad hoc decisions such as the quantile value in the quantile regression method, statistical methods are typically objective, repeatable and offer a consistent basis to quantify parameter uncertainty. Nonetheless, most studies utilise heuristic methods (87% of the articles reviewed) which are easier to use. The boundary line is usually interpreted in terms of the Law of the Minimum or the Law of Optimum to explain yield gaps. Although these models are useful, their interpretation holds only if the modelled upper limit represents a boundary and not just a particular realization of the upper tail of the distribution of yield. Therefore, exploratory and inferential analysis tools that inform boundary characteristics in data are required if the boundary line is to be useful for YGA.
Statistical methods to fit boundary line models consistently and repeatably, with quantified uncertainty and evidence that there is a boundary limiting the observed yields, are required if boundary line methods are to be used for YGA. Practical and conceptual obstacles to the use of statistical methods are required. Bayesian methods should also be explored to extend further the capacity to interpret uncertainty of boundary line models.
•The boundary line model for the yield gap can be interpreted under different agronomic hypotheses.•Boundary lines are widely-used for yield gap analysis but there is no standard method as yet.•Heuristic methods are more widely-used than statistical models, with no formal account of uncertainty.•Consistent methods for outliers, and exploratory examination of the proposed boundary are needed.•Agronomists need user-friendly software which encodes repeatable and robust boundary line methods.
In-season prediction of crop yield is a topic of research studied by several scientists using different methods. Seasonal forecasts provide critical insights to different stakeholders who use the ...information for strategic and tactical decisions. In this study, we propose a novel scalable method to forecast in season subfield crop yield through a machine learning model based on remotely sensed imagery and data from a process-based crop model on a cumulative crop drought index (CDI) designed to capture the impact of in-season crop water deficit on crops. To evaluate the performance of our proposed model, we used 352 growers' fields of different sizes across the states of Michigan, Indiana, Iowa, and Illinois, with 2520 respective yield maps generated by combine harvesters equipped with precise high-resolution yield monitor sensor, over multiple years (from 2006 up to 2019). We obtained high resolution digital elevation model, climate, and soil data to execute the SALUS model, a process-based crop model, to calculate the CDI for each field used in the study. We used Landsat Analysis Ready Dataset (ARD) products generated by USGS as image source to calculate the green chlorophyll vegetation index (GCVI). We found that the inclusion of the CDI in remote sensing-based random forest models substantially improved in-season subfield corn yield prediction. The addition of the CDI in the yield prediction model showed that the greatest improvements in predictions were observed in the driest year (2012) in our case study. The proposed approach also showed that the subfield spatial variations of corn yield are better captured with the inclusion of CDI for most fields. The earliest prediction in the growing season with GCVI and CDI together outperformed the latest prediction with GCVI alone, highlighting the potential of CDI for predicting spatial variability of maize yield around grain filling period, which is on average close to two months before typical crop harvest in the US Midwest.
•A novel approach was developed to better predict in-season subfield corn yields.•Simulated plant water deficit was added in the model based on vegetation index.•Subfield predictions were conducted in 352 fields across US corn belt states with different environmental conditions.•In-season subfield maize yield prediction improved when crop water deficit was added remote sensing imagery-based model.
Herbaceous field crops include several hundred plant species worldly widespread for different end-uses, from food to no-food applications. Among them are included cereals, grain legumes, sugar beet, ...potato, cotton, tobacco, sunflower, safflower, rape, flax, soybean, alfalfa, clover spp. and other fodder crops, but only 15–20 species play a relevant role for the worldly global economy. Nowadays, to meet the food demand of the ever-increasing world population in a scenario of decreased arable lands, the development of holistic agricultural management approaches to boost contemporaneously yield and quality of herbaceous field crops is essential. Accordingly, this book represents an up-to-date collection of the current understanding of the impact of several agricultural management factors (i.e., genetic selection, planting density and arrangement, fertilization, irrigation, weed control and harvest time) on the yield and qualitative performances of 11 field crops (wheat, cardoon, potato, clary sage, basil, sugarcane, canola, cotton, tomato, lettuce and hemp). On the whole, the topics covered in this book will ensure students and academic readers, such as plant physiologists, environmental scientists, biotechnologists, botanists, soil chemists and agronomists, to get the information about the recent research advances on the eco-sustainable management cultivation of herbaceous field crops, with a particular focus on varietal development, soil nutrient and water management, weed control, etc.
Recently, with the advent of satellite missions and artificial intelligence techniques, supervised machine learning (ML) methods have been more and more used for analyzing remote sensing (RS) ...observation data for crop yield prediction. However, due to the domain shift between heterogeneous regions, supervised ML models tend to have poor spatial transferability. As a result, models trained with labeled data from one spatial region (i.e., source domain) often lose their validity when directly applied to another region (i.e., target domain). To address this issue, we proposed a multisource maximum predictor discrepancy (MMPD) neural network that is an unsupervised domain adaptation (UDA) approach for corn yield prediction at the county level. The novelties of this study include that: 1) we proposed to maximize the discrepancy between two source-specific yield predictors and align source and target domains by considering crop yield response in the target domain and 2) we adopted the strategy of multisource UDA to avoid negative interference between labeled samples from different sources. Case studies in the U.S. corn belt and Argentina demonstrated that the proposed MMPD model had effectively reduced domain shifts and outperformed several other state-of-the-art deep learning (DL) and UDA methods.
SUMMARY
Hybrid breeding is a promising strategy to quickly improve wheat yield and stability. Due to the usefulness of the Rht ‘Green Revolution’ dwarfing alleles, it is important to gain a better ...understanding of their impact on traits related to hybrid development. Traits associated with cross‐pollination efficiency were studied using Near Isogenic Lines carrying the different sets of alleles in Rht genes: Rht1 (semi‐dwarf), Rht2 (semi‐dwarf), Rht1 + 2 (dwarf), Rht3 (extreme dwarf), Rht2 + 3 (extreme dwarf), and rht (tall) during four growing seasons. Results showed that the extreme dwarfing alleles Rht2 + 3, Rht3, and Rht1 + 2 presented the greatest effects in all the traits analyzed. Plant height showed reductions up to 64% (Rht2 + 3) compared to rht. Decreases up to 20.2% in anther length and 33% in filament length (Rht2 + 3) were observed. Anthers extrusion decreased from 40% (rht) to 20% (Rht1 and Rht2), 11% (Rht3), 8.3% (Rht1 + 2), and 6.5% (Rht2 + 3). Positive correlations were detected between plant height and anther extrusion, anther, and anther filament lengths, suggesting the negative effect of dwarfing alleles. Moreover, the magnitude of these negative impacts depends on the combination of the alleles: Rht2 + 3 > Rht3/Rht1 + 2 > Rht2/Rht1 > rht (tall). Reductions were consistent across genotypes and environments with interactions due to magnitude effects. Our results indicate that Rht alleles are involved in multiple traits of interest for hybrid wheat production and the need to select alternative sources for reduced height/lodging resistance for hybrid breeding programs.
Significance Statement
Hybrid wheat is a promising strategy to increase grain yield. The negative effect on floral traits with relevance for hybrid breeding of different Rht genes using near‐isogenic lines was reported.
Mixed cropping, also known as inter-cropping or co-cultivation, is a plant production system that involves planting two or more species (or cultivars) in the same field in a variable order—row or ...rowless—simultaneously. Mixed cropping plays an important role in sustainable agriculture by adding value to crop rotations and agroecosystems. Scientific investigations on environmentally friendly mixed cropping should be supported by studies on the direct costs and long-term benefits that are the most relevant to farmers. Meeting the need to strengthen the scientific basis for mixed crops, the papers in this Special Issue enhance our understanding of the following: The selection of species and cultivars for a mixed crop system as well as the choice of agricultural treatments that will secure a stable yield of mixtures; Inter- and intra- species competition of plants in a canopy; Ecological intensification approaches and opportunities for maximizing crop performance and yield in mixtures; The effects of mixed crops on crop rotations; The short- and long-term ecosystem benefits of mixtures; The effects on pests and the biodiversity of agroecosystems provided by mixtures; The economic aspects of adopting the mixtures in farms; The nutritive value of mixtures for livestock; Other topics related to the mixed cropping.
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