**1. Introduction**

Feeding a global population of 9.5 billion by 2050 is anticipated to become one of the greatest challenges of our time [1–3]. Rapid population growth [1,3–7], decreasing agricultural productivity [8–10], climate change [3,10,11], natural resource scarcity [3,12], and biofuel production [3,13–18] collectively undermine the current and future capacity of global food production systems. The risk of food insecurity is no longer a challenge exclusive to lesser-developed countries. In Australia, one in six Australians reported having experienced food insecurity in 2016 [10], with an estimated 2 million people having sought food relief [19,20].

While there have been considerable effort to identify strategies to enhance and diversify current food production systems [4,5,9], of equal importance is an increasing realisation of significant inefficiencies in the global food system due to food loss and waste (FLW) [6,21–25]. Global FLW has been estimated to represent 27% to 50% of total agricultural production [26–31]. Annually, there is around 4 Mt or AUD8 billion worth of FLW in Australia, 33% of which is horticultural product [19,32,33]. Due to their relative perishability, horticultural products are considered particularly vulnerable to elevated losses. Until recently, reliable and systematic estimations of global FLW have been difficult to

determine, due to an absence of a universal and consistent quantification methodology for reporting and managing food removed from the food supply chain [31,34–36]. In response, the Food Loss and Waste Protocol was established in 2013, with the first international *FLW Accounting and Reporting Standard* ratified in June 2016 during the Global Green Growth Forum (3GF) in Copenhagen.

FLW within commercial food supply chains is shaped by multifarious contributors, including various types of production system inefficiencies and consumer behaviour [21,23,24,27,28]. Of increasing concern and importance is the discourse between the food marketing and consumer purchasing behaviour that is perpetuating FLW throughout the food supply chain [3,6,22,25,31,37,38]. Supermarkets showcase only premium and unblemished product, fabricating unrealistic expectations of how fruits and vegetables should appear. Accordingly, consumers often equate food safety and freshness with elevated cosmetic standards. In combination, these factors have created intrinsically wasteful food systems [1,3,19,22,24,25,27,29,31,35,39]. Private food policy and standards aligned with marketing campaigns often reinforce high levels of FLW via cosmetic product specifications and use-by-dates, driving losses up-stream within the food supply chain [3,19,37].

In seeking to address FLW, potential remediation strategies are predominantly directed at the consumer-end of the food supply chain, in part due to difficulties in quantifying loss at the primary production stages [6]. Highlighting this fact, a newly established protocol for quantification of FLW [40] specifically quantifies postharvest losses, deliberately excluding pre-harvest losses and consumer waste. There is a premise that commercial farms, operating highly mechanised and technology-centric agricultural production systems have achieved an optimum level of FLW minimisation [31,41]. While it is intuitive to presume low levels of FLW within technology-dense horticultural supply chains, there is increasing evidence to the contrary [21,22,25,27,28,42] proposing that such production systems may in fact be more wasteful given the stringent adherence to private food policy and standards.

This study sought to quantify horticultural postharvest losses associated with a highly mechanised commercial tomato enterprise with access to appropriate and effective postharvest handling equipment and infrastructure. The aim of this study was to document accumulative and overall postharvest losses, and to better understand the impacts of technology (e.g., packing shed mechanisation and grading/sorting automation), supply chain length (distance, time, and biophysical conditions), and private food policy and standards (i.e., supermarket standards and product specification) on FLW. To do so, a multi-disciplinary approach was undertaken, based on quantitative documentation of postharvest losses and handling conditions, and qualitative techniques to identify the drivers of the loss and contextualise the findings within the food supply chain.


**1. Introduction**

As a climate-sustainable solution, the world needs urgent and rapid incorporation of renewable energy (RE) into the global energy scenario. RE sources are commonly known as clean energy solutions, gaining the deep attention of energy users in commercial and domestic applications. The PV energy conversion method has received immense attention from researchers in recent days [1]. Concerns about the worldwide energy problems and the threat of climate change posed by existing energy sources have spurred the research for alternative energy sources. The most popular renewable energy source is solar PV, which is nonpolluting and requires no maintenance [2].

During the first half of 2020, thirteen countries pledged the largest amount of new RE ever, nearly 50 GW, to be installed in the year span of 2021–2024. Global demand in 2021 is expected to be 25% higher than in 2020 [3,4]. The researchers performed different studies to establish an effective and stable conversion of solar PV energy. Module mismatching and PSCs are two key contributors to PV system losses. Under these conditions, the PV system

**Citation:** Aljafari, B.; Pachauri, R.K.; Thanikanti, S.B.; Ayodele, B.V. Innovative Methodologies for Higher Global MPP of Photovoltaic Arrays under PSCs: Experimental Validation. *Sustainability* **2023**, *15*, 11852. https:// doi.org/10.3390/su151511852

Academic Editors: Domenico Mazzeo and Cristina Ventura

Received: 9 May 2023 Revised: 20 June 2023 Accepted: 27 July 2023 Published: 1 August 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

(P-V and I-V characteristics) becomes more complex and has many power peaks. The amount of energy made by a PV array is greatly reduced when it is in the shade [5–7]. To boost the efficiency of PV systems, a number of modules are coupled in specified combinations. An evaluation of the most recent research papers is conducted in this work in order to determine the research gap. The many varieties of PV array configurations' performance, reliability, precision, resilience, efficiency and operation are all investigated. There are several things that can cause PSCs, such as passing clouds, high-rise buildings, telecommunications towers, adjacent trees, dead leaves, and so on.

The PV power output is reduced, allowing shaded modules to waste a significant percentage of the power produced by unshaded modules, resulting in hotspots that might eventually destroy the PV cell/module [8,9]. When designing an array, several PV modules are arranged in series and parallel to satisfy the load power demand. Parallel and series configurations of PV modules are used to meet a PV array's power requirements. Traditional and game puzzle-based reconfiguration approaches such as series, series-parallel (SP), bridge-link (BL), total cross-tied (TCT), honey-comb (HC), Latin square (LS), magic square (MS) and Su-Do-Ku (SDK) puzzles have been adopted to design PV array systems.

Because of the way these algorithms are designed, the operative point is the earliest peak in the PV characteristic, which may or may not be a global power peak. Several advancements in the literature have been recorded to promote these algorithms in order to find the global power peak under PSCs. This is accomplished by adding more stages to the algorithm, making it more complex and restricting monitoring speed. Recently, an improved SDK-puzzle-based algorithm for MPP monitoring was discovered to yield good results.


4. Conclusions

3.2. Real data sets

(Trait-Env).

In Table 3 we can observe that in the wheat data set the best predictions were observed under the proposed improved BMTME model (M2), since in all trait-environment combinations was better model M2 in terms of Pearson correlation and in 10 out of 12 was the better in terms of MSEP than model M3 (that ignore the correlation between traits and between environments). However, in the maize data set the best predictions were observed under

CorP SE MSEP SE CorP SE MSEP SE

DTHD\_Bed2IR 0.876 0.008 8.117 0.692 0.875 0.009 10.636 0.882 GNDVI\_Bed2IR 0.848 0.008 0.000 0.000 0.009 0.022 0.103 0.006 GRYLD\_Bed2IR 0.639 0.014 0.055 0.002 0.463 0.015 0.161 0.007 PTHT\_Bed2IR 0.658 0.014 22.527 0.841 0.566 0.020 25.798 0.895 DTHD\_Bed5IR 0.873 0.007 13.074 0.733 0.845 0.010 15.312 0.508

GRYLD\_Bed5IR 0.178 0.023 0.251 0.008 0.175 0.020 0.336 0.014 PTHT\_Bed5IR 0.076 0.016 24.064 0.620 0.245 0.023 20.831 0.721 DTHD\_Drip 0.915 0.005 4.514 0.201 0.895 0.006 3.321 0.224 GNDVI\_Drip 0.681 0.012 0.000 0.000 0.262 0.022 0.123 0.008 GRYLD\_Drip 0.653 0.011 0.126 0.005 0.638 0.011 0.144 0.005 PTHT\_Drip 0.658 0.019 21.565 0.531 0.602 0.012 21.306 0.728 Average 0.651 0.013 7.858 0.303 0.462 0.016 8.191 0.334 Yield\_EBU 0.320 0.019 0.789 0.018 0.365 0.018 0.731 0.017 ASI\_EBU 0.501 0.016 0.396 0.012 0.510 0.015 0.391 0.012 PH\_EBU 0.308 0.025 0.015 0.003 0.305 0.011 0.010 0.000 Yield\_KAK 0.402 0.022 0.446 0.020 0.416 0.020 0.438 0.019

Wheat GNDVI\_Bed5IR 0.758 0.019 0.000 0.000 0.496 0.023 0.219 0.011

Maize ASI\_KAK 0.389 0.015 0.936 0.043 0.423 0.018 0.822 0.029

CorP: average of Pearson correlation obtained across all trait-environment combination; SE: standard error; MSEP: mean square error of prediction. Trait\_Env means trait-environment combination. In bold are the best predictions of each row

Table 3. Comparison in terms of prediction accuracy of models M2 and M3 using the two real data sets.

PH\_KAK 0.462 0.025 0.011 0.001 0.369 0.022 0.013 0.001 Yield\_KTI 0.276 0.015 0.848 0.022 0.318 0.018 0.825 0.024 ASI\_KTI 0.290 0.018 0.607 0.018 0.280 0.020 0.614 0.019 PH\_KTI 0.460 0.017 0.019 0.001 0.443 0.017 0.020 0.001 Average 0.379 0.019 0.452 0.015 0.381 0.018 0.429 0.014

Data set Trait\_Env M2 M3

28 Physical Methods for Stimulation of Plant and Mushroom Development

In this paper we proposed an improved version of the Bayesian multiple-trait multipleenvironment (BMTME) model of Montesinos-López et al. [3] that was derived using the matrix normal distribution. The advantage of the proposed model (M2) is that it is more efficient in terms of time of implementation since this improved version works using as rows the genotypes by environment combinations in place of using as rows the combination of traits, genotypes and environments which allows a more practical implementation of the Gibbs sampler in terms of time of implementation. Another, improvement of the BMTME model is that now allows unstructured covariance matrix for modeling environments in place of only a diagonal matrix as the original BMTME model. We compared the extended model (M2) with an uncorrelated multiple-trait and multiple-environment model (M3) that ignores the general correlation between traits (genetic and residual) and between environments and we found that the proposed improved BMTME model (M2) outperforms model (M3) in all the scenarios under study with simulation, however the larger the correlation between traits and between environments the better the performance in terms of prediction accuracy of the improved BMTME model. Additionally, we provided all full conditionals required for the implementation of the improved BMTME model (see Gibbs sampler section and Appendix A). However, we are aware that more empirical evidence with real and simulated data is needed to support our findings, and for this reason, we encourage researcher to implement our proposed improved model and compare with models that ignore the correlation between traits and between environments like the model M3 given in Eq. (8).


**10. Conclusions**

Many research studies suffer from design and analysis flaws, limiting their utility for this purpose. The biological significance of many of the more sensitive end-points is still debated, as is whether studies that only examined conventional end-points are adequate for detecting all potentially relevant effects. Few clear trends have emerged from studies of BPA's effects on wildlife. Terrestrial wildlife is likely to be exposed to low levels of BPA, and few studies have looked at environmentally relevant doses. Although the majority of BPA regulation focuses on human exposure through food packaging, these applications account for only a small portion of BPA use. In the absence of new regulations, if current trends continue, BPA production and environmental release will increase. BPA would not necessarily result in a safer or more thoroughly researched chemical substitute. A more cautious approach to chemical regulation and use may reduce potential environmental impacts. Issues like these will continue to arise as humans become more reliant on chemical advances to meet global needs.

*Bisphenols*