The Future of Farming in a Changing World: From Physiology to Technology

Topic Information

Dear Colleagues,

This Topic explores the multifaceted effects of climate change, precision agriculture technologies, and agrivoltaics on crops’ physiology, growth, yield, quality, and subsequent food processing. These factors significantly influence the agricultural economies of many nations, leading to challenges (such as water scarcity) and opportunities (like the advent of innovative technologies). Climate change manifests in diverse forms: elevated temperatures, droughts, heat waves, intense rainfall, and the emergence of new diseases, which each present unique challenges in crop production. Conversely, precision agriculture technologies offer hope, enabling resource conservation, enhanced crop management, and the efficient utilization of traditional and underutilized crop varieties. This Topic will explore these issues to provide insights and solutions that will shape future crop production strategies.

Dr. Giuseppe Ferrara
Prof. Dr. Olaniyi Amos Fawole
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Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.3	4.9	2011	20.2 Days	CHF 2600	Submit
Agronomy agronomy	3.3	6.2	2011	15.5 Days	CHF 2600	Submit
Crops crops	-	-	2021	24.2 Days	CHF 1000	Submit
Foods foods	4.7	7.4	2012	14.3 Days	CHF 2900	Submit
Plants plants	4.0	6.5	2012	18.2 Days	CHF 2700	Submit

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All Agriculture Agronomy Crops Foods Plants

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13 pages, 1914 KiB  

Open AccessArticle

Climate Change and Its Positive and Negative Impacts on Irrigated Corn Yields in a Region of Colorado (USA)

by Jorge A. Delgado, Robert E. D’Adamo, Alexis H. Villacis, Ardell D. Halvorson, Catherine E. Stewart, Jeffrey Alwang, Stephen J. Del Grosso, Daniel K. Manter and Bradley A. Floyd

Crops 2024, 4(3), 366-378; https://doi.org/10.3390/crops4030026 - 9 Aug 2024

Viewed by 428

Abstract

The future of humanity depends on successfully adapting key cropping systems for food security, such as corn (Zea mays L.), to global climatic changes, including changing air temperatures. We monitored the effects of climate change on harvested yields using long-term research plots [...] Read more.

The future of humanity depends on successfully adapting key cropping systems for food security, such as corn (Zea mays L.), to global climatic changes, including changing air temperatures. We monitored the effects of climate change on harvested yields using long-term research plots that were established in 2001 near Fort Collins, Colorado, and long-term average yields in the region (county). We found that the average temperature for the growing period of the irrigated corn (May to September) has increased at a rate of 0.023 °C yr⁻¹, going from 16.5 °C in 1900 to 19.2 °C in 2019 (p < 0.001), but precipitation did not change (p = 0.897). Average minimum (p < 0.001) temperatures were positive predictors of yields. This response to temperature depended on N fertilizer rates, with the greatest response at intermediate fertilizer rates. Maximum (p < 0.05) temperatures and growing degree days (GDD; p < 0.01) were also positive predictors of yields. We propose that the yield increases with higher temperatures observed here are likely only applicable to irrigated corn and that irrigation is a good climate change mitigation and adaptation practice. However, since pan evaporation significantly increased from 1949 to 2019 (p < 0.001), the region’s dryland corn yields are expected to decrease in the future from heat and water stress associated with increasing temperatures and no increases in precipitation. This study shows that increases in GDD and the minimum temperatures that are contributing to a changing climate in the area are important parameters that are contributing to higher yields in irrigated systems in this region. Full article

(This article belongs to the Topic The Future of Farming in a Changing World: From Physiology to Technology)

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Figure 1
<p>Changes in average temperature (<b>a</b>), growing degree days (GDD) (<b>b</b>), and total precipitation (<b>c</b>) during the corn growing season from 1900 to 2019 in Fort Collins, Colorado (Data from National Oceanic and Atmospheric Administration [NOAA] station ID #: GHCND:USC00053005). Note: Daily mean temperature (T_mean) was calculated from the daily maximum temperature (T_max) and daily minimum temperature (T_min) as follows: T_mean = (T_max + T_min)/2).</p> Full article ">Figure 2
<p>CSU pan evaporation vs. mean daily temperature, May-September, Selected Years, 1949–2019. Weather information collected at NOAA station ID #: GHCND:USC00053005. Note that daily mean temperature (T_mean) was calculated from the daily maximum temperature (T_max) and daily minimum temperature (T_min) as follows: T_mean = (T_max + T_min)/2).</p> Full article ">Figure 3
<p>Average harvested corn yields (15.5% water content) in Larimer County, Colorado versus average minimum temperatures during the corn growing season, May to September, from 1963 to 2019 (data from NOAA station ID # GHCND:USC00053005).</p> Full article ">Figure 4
<p>Average harvested corn yields (15.5% water content) in Larimer County, Colorado from 1963 to 2019.</p> Full article ">Figure 5
<p>Average harvested corn yields (15.5% water content) in Larimer County versus June Stress Degree Days (SDD), from 1991 to 2018 (data from National Oceanic and Atmospheric Administration [NOAA] station ID # GHCND:USC00053005).</p> Full article ">

20 pages, 1532 KiB  

Open AccessArticle

“What’s Good for the Bees Will Be Good for Us!”—A Qualitative Study of the Factors Influencing Beekeeping Activity

by Aliz Feketéné Ferenczi, István Szűcs and Andrea Bauerné Gáthy

Agriculture 2024, 14(6), 890; https://doi.org/10.3390/agriculture14060890 - 4 Jun 2024

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Abstract

Beekeepers play a crucial role in the survival of honey bee populations, so it is essential to understand the drivers behind their activities. This qualitative study aims to explore the factors influencing beekeepers’ decision-making and to assess the relationship between beekeepers and their [...] Read more.

Beekeepers play a crucial role in the survival of honey bee populations, so it is essential to understand the drivers behind their activities. This qualitative study aims to explore the factors influencing beekeepers’ decision-making and to assess the relationship between beekeepers and their bees, to identify the relationship between them by building a theoretical model, and to assess the perception of pollination services as a potential source of income diversification among Hungarian beekeepers. Based on the grounded theory method, we created a paradigm model of beekeeping management based on semi-structured interviews with beekeepers in Hungary. In the analysis of the interviews, we first used open coding to develop categories according to the concepts used by the beekeepers, and then structured and linked these categories (axial coding). Finally, we identified the most relevant main categories (selective coding) and outlined the conceptual framework for beekeeping management. We mapped the strategies and beekeeping practices beekeepers use and the consequences they generate. The results show that several causal conditions influence beekeeping decisions and strategies. In an environment where beekeepers’ costs are increasing and their incomes are decreasing while implementing adaptation strategies, more targeted measures are needed to protect bees and increase beekeepers’ profitability. Full article

(This article belongs to the Topic The Future of Farming in a Changing World: From Physiology to Technology)

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Figure 1

Figure 1
<p>Representation of the total number of bee colonies and apiaries per county interviewed.</p> Full article ">Figure 2
<p>Paradigm model of decision-making in beekeeping.</p> Full article ">

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Supplementary material:
Supplementary File 1 (ZIP, 963 KiB)

17 pages, 4215 KiB  

Open AccessArticle

15-cis-Phytoene Desaturase and 15-cis-Phytoene Synthase Can Catalyze the Synthesis of β-Carotene and Influence the Color of Apricot Pulp

by Ningning Gou, Xuchun Zhu, Mingyu Yin, Han Zhao, Haikun Bai, Nan Jiang, Wanyu Xu, Chu Wang, Yujing Zhang and Tana Wuyun

Foods 2024, 13(2), 300; https://doi.org/10.3390/foods13020300 - 17 Jan 2024

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Abstract

Fruit color affects its commercial value. β-carotene is the pigment that provides color for many fruits and vegetables. However, the molecular mechanism of β-carotene metabolism during apricot ripening is largely unknown. Here, we investigated whether β-carotene content affects apricot fruit color. First, the [...] Read more.

Fruit color affects its commercial value. β-carotene is the pigment that provides color for many fruits and vegetables. However, the molecular mechanism of β-carotene metabolism during apricot ripening is largely unknown. Here, we investigated whether β-carotene content affects apricot fruit color. First, the differences in β-carotene content between orange apricot ‘JTY’ and white apricot ‘X15’ during nine developmental stages (S1–S9) were compared. β-carotene contents highly significantly differed between ‘JTY’ and ‘X15’ from S5 (color transition stage) onwards. Whole-transcriptome analysis showed that the β-carotene synthesis genes 15-cis-phytoene desaturase (PaPDS) and 15-cis-phytoene synthase (PaPSY) significantly differed between the two cultivars during the color transition stage. There was a 5 bp deletion in exon 11 of PaPDS in ‘X15’, which led to early termination of amino acid translation. Gene overexpression and virus-induced silencing analysis showed that truncated PaPDS disrupted the β-carotene biosynthesis pathway in apricot pulp, resulting in decreased β-carotene content and a white phenotype. Furthermore, virus-induced silencing analysis showed that PaPSY was also a key gene in β-carotene biosynthesis. These findings provide new insights into the molecular regulation of apricot carotenoids and provide a theoretical reference for breeding new cultivars of apricot. Full article

(This article belongs to the Topic The Future of Farming in a Changing World: From Physiology to Technology)

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Figure 1

Figure 1
<p>Phenotypic β-carotene and chlorophyll a and b content across different developmental stages in ‘X15’ and ‘JTY’. (<b>A</b>) β-carotene content of different orange and white pulp apricot cultivars. (<b>B</b>) Phenotypic changes of JTY’ and ‘X15’ at different developmental stages. The contents of β-carotene (<b>C</b>), chlorophyll a (<b>D</b>), and chlorophyll b (<b>E</b>) at different developmental stages. Note: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, ns: not significant.</p> Full article ">Figure 2
<p>Preliminary analysis of transcriptome data in ‘X15’ and ‘JTY’. (<b>A</b>) Principal component analysis of 18 samples based on FPKM. (<b>B</b>) The proportion of expressed genes at four different expression levels in ‘JTY’ (SS3, SS5, and SS8) and ‘X15’ (XS3, XS5, and XS8). (<b>C</b>) The numbers of upregulated and downregulated genes at three stages of fruit development in ‘JTY’ as compared with ‘X15’. (<b>D</b>) KEGG enrichment of DEGs. The X-axis indicates comparative combinations, the Y-axis indicates enriched KEGG items, size indicates number of identified genes in the background, and color indicates significance level.</p> Full article ">Figure 3
<p>Schematic diagram and heat maps of differentially expressed genes (DEGs) involved in carotenoid metabolic pathways during different developmental stages in ‘X15’ and ‘JTY’. DEGs linked to enzymes and their log2FC values are presented in the heat maps. Redder and bluer colors represent higher and lower log2FC values, respectively. PSY, 15-<span class="html-italic">cis</span>-phytoene synthase; PDS, 15-<span class="html-italic">cis</span>-phytoene desaturase; ZISO, zeta-carotene isomerase; ZDS, zeta-carotene desaturase; CRTISO, prolycopene isomerase; LCYB, lycopene beta-cyclase; CrtZ, beta-carotene 3-hydroxylase; ZEP, zeaxanthin epoxidase; VDE, violaxanthin de-epoxidase; NSY, neoxanthin synthase; NCED, 9-<span class="html-italic">cis</span>-epoxycarotenoid dioxygenase; LCYE, lycopene epsilon-cyclase; LUT5, beta-ring hydroxylase; LUT1, carotenoid epsilon hydroxylase.</p> Full article ">Figure 4
<p>Structure analysis of the PaPDS gene. (<b>A</b>) Schematic diagram of PaPDS base mutation sites in ‘X15’ and ‘JTY’, the red arrow indicates a 5 bp mutation. Note: red indicates the CDS of the PaPDS gene, and gray indicates the introns. (<b>B</b>) Schematic diagram of the PaPDS amino acid domain in ‘X15’ and ‘JTY’. Note: red indicates the functional domain of PaPDS, * indicates termination of translation.</p> Full article ">Figure 5
<p>PaPDS positively regulates β-carotene accumulation in apricots. (<b>A</b>) Coloration of ‘Yinbai’ injected with PaPDS overexpression Agrobacterium plasmid mixture (pBWA-X15-PaPDS, pBWA-JTY-PaPDS). An empty pBWA vector was used as a control. (<b>B</b>) Determination of carotenoid content in overexpression experiments. (<b>C</b>) Relative expression levels of β-carotene synthesis pathway genes in overexpression experiments. (<b>D</b>) Coloration of ‘Pingguoxing’ injected with PaPDS silencing Agrobacterium plasmid mixture (TRV1 + TRV2: PaPDS). An empty TRV1 + TRV2 vector was used as a control. (<b>E</b>) Determination of carotenoid content in silencing experiments. (<b>F</b>) Relative expression levels of β-carotene synthesis pathway genes in silencing experiments. The different letters represent significant differences (LSD test, <span class="html-italic">p</span> &lt; 0.05). The asterisks indicate significant differences (** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, ns: not significant) based on the Student’s <span class="html-italic">t</span>-test.</p> Full article ">Figure 6
<p>PaPSY may be regulated by transcription factors to influence β-carotene metabolism in apricots. (<b>A</b>) Schematic diagram of cis-acting elements of the PaPSY promoter in ‘X15’ and ‘JTY’. (<b>B</b>) GUS staining for transient expression of the PaPSY promoter of ‘X15’ and ‘JTY’ in tobacco leaves. (<b>C</b>) Expression levels of possible transcription factors regulating PaPSY. (<b>D</b>) Coloration of ‘Pingguoxing’ injected with PaPSY silencing Agrobacterium plasmid mixture (TRV1 + TRV2: PaPSY). An empty TRV1 + TRV2 vector was used as a control. (<b>E</b>) Determination of carotenoid content in silencing experiments. (<b>F</b>) Relative expression levels of β-carotene synthesis pathway genes in silencing experiments. The different letters represent significant differences (LSD test, <span class="html-italic">p</span> &lt; 0.05). The asterisks indicate significant differences (** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, ns: not significant) based on the Student’s <span class="html-italic">t</span>-test.</p> Full article ">Figure 7
<p>PaPSY may be regulated by lncRNA to influence β-carotene metabolism in apricots. (<b>A</b>) Pattern diagram of lncRNA regulation of genes related to the carotene metabolism pathway. (<b>B</b>) Expression of PaPSY at three growth and developmental stages in ‘JTY’ and ‘X15’. (<b>C</b>) Expression of LTCONS_00032302 at three growth and developmental periods in ‘JTY’ and ‘X15’.</p> Full article ">Figure 8
<p>Working model for PaPSY and PaPDS function in the regulation of ’JTY’ and ‘X15’. Dashed balloon indicates that ERF, NAC, and MYB may bind to cha-CAM1a, MBSI, Myb, and other cis-acting elements on the PaPSY promoter, thereby affecting the expression and function of PaPSY. Dotted arrows indicate that LTCONS_00032302 targets PaPSY, which may affect the expression and function of PaPSY.</p> Full article ">

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