
Citation: Robert Ugochukwu Onyeneke, Chinyere Augusta Nwajiuba, Chukwuemeka Chinonso Emenekwe, Anurika Nwajiuba, Chinenye Judith Onyeneke, Precious Ohalete, Uwazie Iyke Uwazie. Climate change adaptation in Nigerian agricultural sector: A systematic review and resilience check of adaptation measures[J]. AIMS Agriculture and Food, 2019, 4(4): 967-1006. doi: 10.3934/agrfood.2019.4.967
[1] | Jing Huang, Ao Han, Huafeng Liu . On a Diophantine equation with prime variables. AIMS Mathematics, 2021, 6(9): 9602-9618. doi: 10.3934/math.2021559 |
[2] | Jing Huang, Wenguang Zhai, Deyu Zhang . On a Diophantine equation with four prime variables. AIMS Mathematics, 2025, 10(6): 14488-14501. doi: 10.3934/math.2025652 |
[3] | Bingzhou Chen, Jiagui Luo . On the Diophantine equations x2−Dy2=−1 and x2−Dy2=4. AIMS Mathematics, 2019, 4(4): 1170-1180. doi: 10.3934/math.2019.4.1170 |
[4] | Xinyan Li, Wenxu Ge . A Diophantine approximation problem with unlike powers of primes. AIMS Mathematics, 2025, 10(1): 736-753. doi: 10.3934/math.2025034 |
[5] | Hunar Sherzad Taher, Saroj Kumar Dash . Repdigits base η as sum or product of Perrin and Padovan numbers. AIMS Mathematics, 2024, 9(8): 20173-20192. doi: 10.3934/math.2024983 |
[6] | Cheng Feng, Jiagui Luo . On the exponential Diophantine equation (q2l−p2k2n)x+(pkqln)y=(q2l+p2k2n)z. AIMS Mathematics, 2022, 7(5): 8609-8621. doi: 10.3934/math.2022481 |
[7] | Jinyan He, Jiagui Luo, Shuanglin Fei . On the exponential Diophantine equation (a(a−l)m2+1)x+(alm2−1)y=(am)z. AIMS Mathematics, 2022, 7(4): 7187-7198. doi: 10.3934/math.2022401 |
[8] | Min Zhang, Fei Xue, Jinjiang Li . On the Waring–Goldbach problem for two squares and four cubes. AIMS Mathematics, 2022, 7(7): 12415-12436. doi: 10.3934/math.2022689 |
[9] | Li Zhu . On pairs of equations with unequal powers of primes and powers of 2. AIMS Mathematics, 2025, 10(2): 4153-4172. doi: 10.3934/math.2025193 |
[10] | Jing Huang, Qian Wang, Rui Zhang . On a binary Diophantine inequality involving prime numbers. AIMS Mathematics, 2024, 9(4): 8371-8385. doi: 10.3934/math.2024407 |
The Annonaceae family is divided in 7 genera, and the genus Annona includes 2,400 species [1]. Custard apple is cultivated on tropical and subtropical areas around the globe for its exotic fruit with high nutritional value [2]. The semi-deciduous tree, slightly shrubby, can reach 9 m in height with alternate simple leaves shifting shape from elliptical to oval of variable size [1]. The hermaphrodite flowers are solitary in groups of 3 or 2 [3]. The fruit is typically conical or heart shaped with white succulent pulp, brown or black seeds, and a smooth peel [4]. Native to South America, custard apple was introduced to Europe in the mid-18th century [5] and has been cultivated in Madeira Island (Portugal) since the 19th century. Custard apple worldwide proliferation allowed the creation of cultivars with distinctive agronomical characteristics. A study showed that Madeira Island varieties form a homogenous group with less correlation to varieties found in other tropical areas, which indicates the existence of unique genetic pool with unique features [6]. The excellence, the differentiation and unique cultivation conditions of this fruit led to "Anona da Madeira" Protected Designation of Origin (PDO) registration by the European Union since 2000 (in agree with Regulation (EU) N.º 1187/2000) [7].
The food industry is a key sector and a leading producer of waste which negatively impacts global economy, environment and climate change [8]. Food waste cannot be prevented; however, new research and techniques can open a wide range of potential applications for biowaste and food by-products [9]. Annona fruits are consumed fresh for their pulp with sweet taste and health benefits, such as being rich in fiber, vitamins (B and C), antioxidants, iron, calcium, phosphorus, and potassium [10]), and there is a growing interest of establishing this species in Europe, but fruit processing and transportation represent a key issue to producer countries, due to its sensitivity and high moisture, leading to 75.8% post-harvest losses. During fruit consumption, it is estimated that 3–8.5% of waste is generated from seeds and 20% from peel [11]. Given its benefits and fruit underappreciation [12], studies including Annona cherimola are of high interest, generating new features and applications not only for its pulp but also its fruit waste. Different studies have been conducted with custard apple species which demonstrate its antifungal activity [13] due to its phytochemical composition and biological activity [14]. Synthetic chemicals are frequently used to prevent fungal infection. However, these antifungal factors carry undesired effects, such as environmental contamination, acquired pathogen resistance, and overall residual toxicity. Hence the need for bioproducts capable of controlling fungal threats with minimal consequences to the surroundings [13].
Fusarium oxysporum is a cosmopolitan soil pathogen found worldwide with specificity to more than 100 different hosts, including many plants of high agricultural interest [15]. The presence of this pathogen originates from an illness known as Fusarium wilt [16]. The host plant initiates the infection process by segregating biomolecules which act as signalers for F. oxysporum spore germination and consequent germ tube development. The germ tubes establish contact with the host tissues allowing the mycelial colonization leading to xylem invasion and restricting water flow. Ultimately, F. oxysporum excretes enzymes and toxins to aid infection alongside virulence factors that dictate pathogenesis [17]. The result is a scope of symptoms that limit plant growth, vascular discoloration, and seed decay [18]. Some Fusarium species produce secondary metabolites that contaminate plants and agricultural fields, decreasing crop yield and thereby impacting human health. Thus, F. oxysporum represents an ongoing threat to worldwide agriculture and crop yield [19]. Due to its pathogenicity, studies have been conducted to minimize plant impairment with the use of environmentally friendly biological agents which can constraint fungal growth.
Aspergillus niger is a common widespread fungus with high adaptability and tolerance to environmental changes [20]. Although is labelled with the GRAS status (Generally Recognized As Safe) from Food and Drug Administration of US government for certain industrial processes, A. niger is one of the most common fungi causing food spoilage [21]. This species can produce extracellular organic acids that enable them to cause decay of several cereals, nuts, beans, fruits, and vegetables [20,22]. Moreover, A. niger is associated to the production of two mycotoxins that are toxic for humans and animals, fumonisin B2 and ochratoxin A [23,24]. Contaminated post-harvest crops can show nutritional, sensorial and qualitative changes, like pigmentation, discoloration, deterioration, and development of off-odors and flavors that reduces its commercial value, thereby causing significant economic losses [20,21]. The produced mycotoxins can survive food production and processing leading to an infected by-product [25].
This study seeks to unfold the benefits of Annona cherimola variants in Madeira Island by showing their seed and peel bio-waste oil extracts antifungal action and further influence on agricultural research. These variants are not well known due to lack of research and this experiment intends to test their antifungal action against two pathogens, Fusarium oxysporum and Aspergillus niger. For data analysis, two measuring methods were used, calculated area (CA) and diameter average (DA), in order to analyze which one would provide more accurate growth ratios and consequently precise inhibition ratios (IR).
Eight A. cherimola varieties were used in this study, of which 4 varieties from Centro Experimental do Bom Sucesso (Funchal, South of Madeira Island), Funchal (FX), Madeira (MD), Matteus (MT), Perry Vidal (PV), and another 4 varieties from a local producer (Santa Cruz, East of Madeira Island), Anis (AN), Dona Mécia (DM), Francesa (FR), and Moreira (MR) (Figure 1).
The unripe fruits were collected between December 2020 and January 2021. The ripening process was done naturally on a bench. Once finished, the ripe fruit mass was registered, and the pulp, peel, and seeds were separated. Seed and peel samples were then dehydrated in a ventilated oven (Heratherm, Thermo-Scientific, Waltham, MA, USA) at 65 ℃ for 48 h and transformed into flour (IKA, Werke M20, Staufen, Germany). The resulting flour was stored in vacuum at −20 ℃ (Liebherr Profiline, Ochsenhausen, Germany) for oil extraction.
Ten grams of seed and peel flour of each variety were used for the oil extracted by Soxhlet method. Each extraction cycle lasted 8 h and was performed in triplicate, using 96% ethanol as solvent. The resulting extract was stored at 4 ℃ (Liebherr Profiline, Ochsenhausen, Germany) overnight. The remaining solvent was separated from the oil by roto-evaporation (Heidolph, Schwabach, Germany) at 40 ℃ and 60 rpm. The oil was poured in petri dishes and placed in a ventilated oven (Heratherm, Thermo-Scientific, Waltham, MA, USA) at 100 ℃ for 48 h to remove any excess solvent. Lastly, the final product was moved to a desiccator, then scrapped off and transferred to 10 mL flasks. The oils were stored at 4 ℃ for further application.
The strains selected for this antifungal assessment were Fusarium oxysporum (MT10) and Aspergillus niger (MT23) obtained from the fungal collection of ISOPlexis, Centre for Sustainable Agriculture and Food Technology. The mycelia preserved at −20 ℃ was transferred to 4 ℃ to acclimate, then cultivated on potato dextrose agar medium (PDA) (Liofilchem, Italy), and incubated in the dark at 25 ℃. Growth was observed throughout a week under natural conditions to guarantee colony viability. Confirmation of the affiliation of each fungal strain to the species was made through molecular techniques. Fungal DNA was isolated according to Yeates et al. [26], with adaptations. The PCR was performed using the primers ITS1 (5'-TCCGTAGGTGAACCTGCGG-3') and ITS4 (5-TCCTCCGCTTATTATTGATATGC-3') and the fragments were sequenced by the laboratories of STAB VIDA (Lisbon, Portugal). The sequences were analyzed using the GenBank database and the BLAST tool (See Appendix A).
The antifungal assessment involved closely monitoring duplicate replicas over the course of a week. Length (L) and width (W) measurements of Fusarium oxysporum (MT10) and Aspergillus niger (MT23) fungal mycelia were rigorously recorded on the third and sixth days, and the average diameter (DA) was accurately calculated using a ruler (Figure 2A). Furthermore, the precise calculated area (CA) was obtained using the program ImageJ (Figure 2B).
The colony development and morphology were meticulously captured for further in-depth examination. To conduct a rigorous preliminary examination, the treatments for the two fungi implied inoculating seven concentrations of oil extract originating from the skin and seed of the cherimoya varieties in study. The highest concentration, C7, was 100 mg of oil extract per milliliter, followed by C6 (80 mg/mL), C5 (60 mg/mL), C4 (40 mg/mL), C3 (20 mg/mL), C2 (15 mg/mL), and C1 (10 mg/mL). These concentrations were meticulously prepared by diluting C7 in 25% ethanol (EtOH) to a total volume of 1 mL. Three controls were included to this assay, as well as in duplicate replicas: an absolute control (PDA + fungus), a negative control (PDA + fungus + EtOH), and a positive control (PDA + fungus + Benomyl). Benomyl, a commercial systemic benzimidazole fungicide sold as Benlate (Be), was deliberately used for the positive control. The petri dishes were divided into 4 quadrants, each representing one individual concentration. Each treatment involved applying the selected oil extract concentration to a specific quadrant on PDA medium. The same rigorous technique was employed for the positive and negative controls using EtOH and Benomyl, respectively.
The measurements collected on the third and sixth days of the fungal mycelia development were analyzed following the procedure described by Hendricks et al. [27] and Guerrero-Álvarez and Giraldo-Rivera [31]. The duplicated L and W values using a ruler were used to calculate DA and transformed into the area of a circle [27] in cm2, using Equation 1:
DA=πr2=π(AverageDiameter2)² | (1) |
The fungal mycelia development CA, in cm2, were determined using ImageJ, an open-source image processing program for scientific image analysis, from pictures previously obtained on both days.
The values obtained from DA and CA were both converted separately to IR, with Equation 2:
IR=(AverageControlArea−AverageSampleArea)AverageControlArea | (2) |
The IR values for both methods were evaluated using principal component analysis (PCA) on MVSP and Spearman test to determine correlations on SPSS. Data variance was analyzed on SPSS using Kruskal-Wallis H test disclosed with Mann-Whitney U test, non-parametric statistical tests used to compare the statistical differences (p ≤ 0.05) among and within variables in study. The custard apple samples were sorted in two groups according to their local of harvest: South of Island for MD, FX, PV, and MT, and East of Island for MR, AN, DM, and FR, to test their oil concentration (C4–C7) against the F. oxysporum (MT10) and A. niger (MT23) fungal growth measured according to age (3- and 6-day old colonies).
All the IR values obtained were calculated always comparing treatment values with absolute control. The inoculation with higher oil concentrations of 100 mg/mL (C7), 80 mg/mL (C6), 60 mg/mL (C5), and 40 mg/mL (C4) had a positive effect in the IR mycelia growth. In contrast, the lower oil concentrations of 20 mg/mL (C3), 15 mg/mL (C2), and 10 mg/mL (C1) did not demonstrate any inhibitory effect on fungal mycelia growth. Therefore, we included in the results discussion the oil concentrations that have a minimum positive effect in the IR mycelia growth (C7–C4), excluding the lower concentrations (C3–C1).
There were no differences between absolute control (fungus) and negative control (fungus + EtOH). On the other hand, positive control (fungus + Benlate) greatly constrained F. oxysporum (MT10) growth reaching 100% inhibition, especially on younger colonies, when for A. niger (MT23) positive control IRs ranges from 83% to 56%.
Overall, the peel oil had a greater inhibition effect on F. oxysporum (MT10) than on A. niger (MT23), with higher IRs occurring always on the third day of the experience. Inhibition followed the same pattern, being higher on the third day for all concentrations and lower on the sixth day (Figure 3).
For F. oxysporum (MT10) colonies exposed to C7, the variants which had a bigger influence on growth were FX, PV, MT, and DM, reaching all values of 60% inhibition on the third day. DM, FX, FR, and MT extracts exhibited continuous action on MT10, until the third day. PV extract initially showed an inhibition action on fungal colony development, but after 6 days showed little control over the fungus. The PCA (Figure S1A) stated that after 3 days. The PCA eigenvalues for MT10 were 0.017 on Axis 1 and 0.002 on Axis 2, with a cumulative percentage of 81.016% and 92.796% respectively and the highest variable loading of 0.571 on Axis 1 for C7. The spatial distribution of cases shows that FX and MT differed from other varieties extracts with higher inhibition values on MT10, followed by PV, DM, and FR. The vectorial tendencies of C4 differed from C5, C6 and C7. Spearman's test displayed strong correlations, |r|≥0.5, between all concentrations. The correlation between C7 and C4 was lower, |r|=0.549(p≤0.01) and conversely, the similarities between C7/C6 |r|=0.838(p≤0.01) were higher. The inhibitory action was predominantly due to C7 and C6 on both days, while C5 of FR, MD, and PV extracts had a bigger impact on fungal mycelia development. A continuous inhibition was observed with MT, FX, DM, and FR extracts on the sixth day. Over time, higher concentrations (C7 and C6) controlled fungal growth better than lower concentrations (C5 and C4) apart for MT extract, which had stronger action on C4 and C5 even after 6 days. The peel samples of AN showed little effect on MT10 throughout the days.
The inhibitory action of oil extracts on A. niger (MT23) was lower than F. oxysporum (MT10), except for AN which reached 89% inhibition, following the same inhibition pattern on other concentrations and on 6 days old colonies (Figure 3). The FX and MD varieties displayed IRs of 50% on the third day, with lower inhibition on the sixth day (31%–14%). In the PCA (Figure S1B) shows the eigenvalues of 0.075 for Axis 1 and 0.005 for Axis 2, explaining a cumulative variation of 90.012% and 95.662% accordingly, and the highest variable loading of 0.566 on Axis 1 for C7. Most cases (cherimoya variant oil) were separated following the C7 vector. However, FX, MD and PV showed tendency close to the C4 vector separation. Once again, it was observed significant correlations between all concentrations, |r|≥0.6(p≤0.01). Contrary to MT10, there is no evident pattern between the different concentrations, and the highest correlation value was attained between C7 and C5 |r|=0.867(p≤0.01). AN extract overperformed when compared to other custard apple extracts, presenting high IRs, during the whole test and the best inhibition results on the third day. The PCA biplot supports the obtained graphic for A. niger (MT23) (Figure 3), showing FX and MD with the highest IRs after AN, and PV showing greater IR on the sixth day. In contrast, MT peel oil failed to inhibit MT23 growth on both days.
The Kruskal-Wallis test revealed significant differences between the peel oil from East and South groups (p≤0.05) for all concentrations and both fungi. On F. oxysporum (MT10), samples were all grouped according to age, being this the distinctive factor. For A. niger (MT23), samples were grouped only by age on C4. On C5, 6-day old South harvested custard apple samples showed significant changes when compared to other groups. On C6 and C7 there were no significant differences between all groups.
Unlike the peel, the seed oil performed similar against F. oxysporum (MT10) and A. niger (MT23), with a slight weaker inhibition on MT23. Again, the third day showed higher growth delay than the sixth day on every concentration and on both fungi (Figure 4).
For F. oxysporum (MT10) on the third day, FX dominated exhibiting IR values higher than 55% on every concentration, followed by PV, FR, and MR. On the sixth day, FX and MR extracts suppressed MT10 proliferation better than other varieties extracts. Both FX and MR showed similar IRs for all concentrations, while DM only showed high IR on C7, reaching 66% of inhibition. On the third day PV and FR surpassed MR, but inhibition decayed over time. The PCA biplot separated MT10 colonies in two clusters (Figure S2A) according to their age, with the third day demonstrating the best results once more. The PCA eigenvalues were 0.027 on Axis 1 and 0.003 on Axis 2, with cumulative percentages of 80.338% and 89.731% respectively, and with the highest variable loading being C7 with 0.537 on Axis 1. The tested concentrations showed different vectorial tendencies, where C7 had greater impact on DM during the third day, while other 3-day old colonies scattered according to C5 and C4. All concentrations showed significant correlations except for C5/C4 (p≥0.01). C6/C7 obtained the highest correlation coefficient |r|=0.744(p≤0.01). The case scores showed FX accomplished the best inhibition results on both days, followed by MR which had the second-best result for the sixth day. The remaining samples from 6-day old colonies agglomerated similarly, with a higher load on the variables C6 and C5.
Regarding A. niger (MT23) colonies, on 3-day old, AN obtained IR values of 26%, 50%, 72%, and 41% on C7, C6, C5, and C4, respectively, while MD established the highest IR value for C7, reaching 59% inhibition and over 40% inhibition on other concentrations. The MT23 PCA biplot (Figure S2B) pointed that C5 had a heavy influence on AN scattering on both days and C7 on MR on the 3rd day. The PCA eigenvalues for MT23 were 0.025 on Axis 1 and 0.007 on Axis 2 with cumulative percentages of 74.496% and 94.662%, respectively. The highest variable loading value was C5 with 0.651 on Axis 1. All correlations between concentrations were significant, with C4/C5, C4/C6, C5/C6 and C7/C6 presenting strong correlations at (p≤0.01), and C7/C4, C7/C5 displaying significant correlation values at (p≤0.05). The lowest correlation value was observed between C7 and C4 |r|=0.394(p≤0.05) and the highest between C4 and C6 |r|=0.702(p≤0.01). Apart from MR, AN and DM, all samples followed the C4/C6 vector tendencies on the third day. On the sixth day distribution was mainly affected by C6 and C4 vectors and with lower inhibition than third day IRs except for AN. MD also exceeded inhibition on the 3rd and 6th days. Contrary to MT10, MR seed oil performed poorly on MT23, specially on the sixth day.
The Kruskal-Wallis test affirmed the existence of significant differences between the seed oil from East and South groups (p≤0.05) for all concentrations on F. oxysporum (MT10) and A. niger (MT23). For MT10, substantial differences were observed between 3-day old and 6-day old groups, with a noticeable split in C7. On MT23, the grouping pattern was repeated except for C5, which was not able to disclaim any differences between the presented groups. Furthermore, C4 exhibited greater differences between East variants from the sixth day and South variants from the third day.
The peel IR from CA values for F. oxysporum (MT10) and A. niger (MT23) do not discriminate a higher inhibition tendency on any strain (Figure 5). Instead, similar to the peel DA inhibition ratios, the third day has highest IRs than the 6th day on both fungi, and C7 attained higher IRs than C4.
For F. oxysporum (MT10), MR and PV performed the best on every concentration on the 3rd day, reaching values of 60% and 57% inhibition on C7, respectively. Afterwards, MR and DM action were maintained while PV had reduced influence on growth, reaching low IRs on the sixth day. The PCA biplot for MT10 (Figure S3A) sorted samples according to their age, except for FX and MD. Each variable formed a distinct vector, with most samples scattering according to C7 vector. The PCA eigenvalues were 0.050 on Axis 1 and 0.009 on Axis 2 with cumulative percentages of 76.866% and 90.641%, respectively. The highest variable loading value was 0.526 on C6. The case scores showed MR high influence on MT10 colony growth on the third day and sixth day, performing the best when compared to other variants, followed by PV and DM. The PCA biplot supports data previously observed which showed PV scarce influence on 6th day growth while DM prevailed on both days. Both FX and MD exerted poor inhibitory action on MT10 throughout a week. All concentrations are strongly correlated, with C6/C5 presenting significant correlation at p≤0.05 and the remaining at p≤0.01. The strongest correlation was observed between C7 and C4 |r|=0.842(p≤0.01).
On A. niger (MT23), AN peel oil had the highest IRs on every concentration and age, reaching 100% inhibition on C6 after 3 days of development. Both PV and MR limited fungal expansion continuously, contrariwise after 6 days of growth MT and FX showed low impact on MT23 maturation. The PCA obtained for MT23 samples (Figure S3B), failed to form clear clusters, contrasting previous results, and suggesting a lower inhibitory action overall. The eigenvalues were 0.108 on Axis 1 and 0.007 on Axis 2 with cumulative percentages of 89.685% and 95.510%, respectively, the variable with the highest loading was C6 with 0.591 on Axis 1. Most samples dispersed according to C7 and C6, except for AN and FR which followed C4 and C5, respectively. The case scores scatter, confirmed AN high inhibitory activity on both days and MT poor action against colonies. The four variables are all strongly correlated once more, with C5/C4, C5/C6, C5/C7 at p≤0.05 and the remaining at p≤0.01. C5 exhibited lower correlation values with other concentrations |r|≤0.6, while C7, C6 and C4 obtained high correlation coefficients |r|≥0.85. The highest correlation value was observed between C6 and C4 |r|=0.913(p≤0.01).
The Kruskal-Wallis test of the peel inhibition ratios obtained from CA outcome diverged from previous results obtained for peel oil DA inhibition ratios. For F. oxysporum (MT10), only C7 displayed significant statistical differences (p≤0.05), with other variables not showing prominent differences between East and South cherimoya variants. C7 East variants on the third day attained significant differences from South variants and East variants on the sixth day. For A. niger (MT23), the same pattern was repeated, with only C5 attaining significant differences (p≤0.05).
The CA suggests that the seed oil performed similar on both fungi and IR was greater on the third day (Figure 6). MR and PV attenuated F. oxysporum (MT10) growth on all concentrations, with MR reaching 70% IR on C7 and C6 and over 60% inhibition on both C5 and C4. MR effect was prolonged throughout the 6 days of testing on MT10, along with DM and FR. Meanwhile, MD, FX and MT acted poorly on MT10 colonies, allowing the colonies to grow bigger. The MT10 PCA case scores (Figure S4A) distributed similarly, following C7, C5, and C4 mostly, except for DM and FX on the sixth day of growth, on which C6 had a higher impact. The eigenvalues obtained by MT10 samples were 0.096 for Axis 1 and 0.006 for Axis 2 with cumulative percentages of 88.560% and 94.129%, respectively. MR, PV, and AN, attenuated growth from the beginning, with MR prolonging its action throughout the week. DM maintained the inhibit action, initially with C7 restraining growth the most and after the 6 days C6 exhibited better action. On the contrary, MD, FX, and MT had low impact on fungal growth. C7 was the variable with the heaviest loading of 0.540 on Axis 1. All variables strongly correlated with each other, |r|≥0.8(p≤0.01). The strongest correlation was between C7 and C4 |r|=0.929(p≤0.01).
Again, AN performed the best against A. niger (MT23) growth achieving 100% inhibition on C5, with further 58% growth constrain on the sixth day. AN, FX and MR also limited colony expansion on every concentration, but FX lost effectiveness on the sixth day. The PCA biplot for MT23 (Figure S4B), resulted on centered case scores, with no evidence of clear clusters. The eigenvalues were 0.030 on Axis 1 and 0.010 on Axis 2, with cumulative percentages of 67.723% and 90.929%, respectively. The variable with the highest loading was C5 with 0.777 and FR placed along the C5 vector while other variants aggregated with the remaining concentrations. On the sixth day, FX clearly displayed the worst IRs, positioning it far apart from other samples. The variables C7, C6, and C4 were all correlated, with C4/C6, C4/C7 |r|≥0.7(p≤0.01), and C5/C4, C6/C7 |r|≥0.5(p≤0.05). C5 was only correlated with C4 |r|=0.510(p≤0.05) and the strongest correlation was C6/C4 |r|=0.714(p≤0.01).
Kruskal-Wallis test showed that there were no significant statistical differences between East and South variants on both fungi, except for A. niger (MT23) at seed oil C4 (p≤0.05).
The morphologies observed for some treatments and negative control can be observed in Figure 7.
Most F. oxysporum colonies (MT10) exposed to the oils developed phenotypical differences when compared to absolute control colonies. The absolute control colonies exhibited a pinkish white color and a cottony-like aerial mycelium. The center of the colonies had pink coloration with white hyphae colonizing a whole quadrant on the sixth day of growth. Meanwhile, MT10 colonies subjected to Annona oils presented an overall dark pink coloration and slower growth when compared to control. Both FX and MD caused MT10 colonies to develop an intense pink color, but the colonies grew as much as the control, taking over the whole plate. In most variants, the peel oil induced the development of stronger pigmentation on exposed colonies, except for AN. The colonies which interact with MR and PV oils, grew less when compared to the control and other variants but exhibited lighter coloration. However, on C7 it was possible to note a darker pigmentation than other concentrations, especially on MR seed oil.
A. niger colonies (MT23) presents yellow coloration on controls and treatments. The colonies exposed to oils grew less when compared to the negative control but overall, there were no significant morphological changes induced by the oils.
The Plant kingdom is a reliable source of diverse phytochemicals and bioactive elements with strong action against various pathogens. These bioactive substances have numerous botanical origins and chemical classifications, the most abundant of which are polyphenols [29]. It has been stated that Annona sp. incorporates various phytochemicals which are normally classified as flavonoids, terpenoids, alkaloids, and polyphenols [4,30]. Various studies have accomplished data that demonstrates its rich phytochemical composition and added antimicrobial action [2]. The following experiment reinforces this idea, showing that Annona cherimola Mill. variants found exclusively in Madeira Island stimulated a defense response on Fusarium oxysporum and Aspergillus niger. Interestingly, different variants originate different levels of inhibition on the tested fungi, suggesting phytochemical differences among them. Gentile et al. [30] conducted nutritional analysis on 7 different A. cherimola variants: Campas, Fino de Jete, Chaffey, White, Daniela, Torre I, and Torre II. All variants presented differences in nutritional, mineral, and vitamin contents, as well as total phenolic content (TPC) demonstrated changes between variants, proposing also heterogenous biological activity. Likewise, Puccio et al. [29] carried out phytochemical analysis on two variants, Fino de Jete and Torre I, concluding there were bioactive differences. AN peel demonstrated effective action against A. niger ranging from 56% to 100% inhibition on both used fungal growth measuring methods, CA and DA, and performing better than the commercial fungicide which inhibited growth up to 86%. Comparing to other variants, this suggests that AN composition might differ resulting in inhibition against this specific fungus [32]. Data related to fungal radial growth was analyzed with two different methods previously described [28,33]. On both methods, sensitivity was observed on the two fungi, with greater results on F. oxysporum (MT10). The attained IRs revealed that the oils had a heavier impact on 3-day old colonies than 6-day old colonies, an effect that was previously observed in another study [33]. This outcome can be related to the fast adaptative response characteristic of these fungal strains and efficient stress-induced defense mechanisms [34,35]. Overall, both seed and peel oils caused similar growth limitations with different levels of inhibition depending on concentration and variant. Although there were no significant differences between all tested concentrations, since all of them were correlated to each other, it was possible to observe an inhibition pattern, where the higher the concentration, the greater the inhibition. This phenomenon was equally observed by Méndez-Chávez et al. [13], opening room for further research, testing higher concentrations in order to reach total inhibition.
When comparing both growth measuring methods, some variants presented incongruent results: the estimated DA areas showed a great inhibitory action while CA method contradicted this fact or vice-versa (Figures 4, 6). CA however has a higher sensitivity to small changes in colony morphology and growth than DA areas. Hendricks et al. [27] tested these two methods on Phyllosticta critricarpa and also observed significant differences between DA and CA values, especially at lower fungicide concentrations: CA was more sensitive to growth changes than DA. Radial growth estimations should be performed in fungal colonies which have an equal superficial rate growth in all directions, describing a circular shape. This condition can be applied to F. oxysporum and A. niger. Nevertheless, the results attained in this study confirm that both methods successfully showed growth differences between control colonies and colonies exposed to the different oils, but CA has a greater perceptiveness to slight changes which are imperceptible to an estimated area.
The study exposed the bioaction diversity present in these variants when fighting different fungi. MR peel and seed oil performed greatly against F. oxysporum (MT10), along with DM and PV and curiously, some variants presented prolongated control over colony growth (MR and DM), while others constrained early growth only (PV) (Figures 5, 6). For A. niger (MT23), most variants revealed little control over colony expansion apart from AN, which reached 100% inhibition. A. cherimola Mill. antifungal action has been also described by Méndez-Chávez et al. [13], using leaf extracts against F. oxysporum. The extract limited colony growth on all tested concentrations, ranging from 49% to 100% inhibition. The statistical results on this study were supported by morphological differences observed on the colonies (Figure 7). According to previous research, F. oxysporum negative control colonies produced a phenotype expected under normal conditions [36]. The F. oxysporum (MT10) colonies cultured in the presence of A. cherimola oils developed a different morphology: most of the colonies presented a much darker coloration especially when in direct contact with the oil. This occurrence can be described as phenotypic plasticity, a short-time response to environmental changes [37] and it has been shown that the Fusarium genus produce different pigments according to growth conditions [38]. The different variants promoted morphological change, for instance, although MD and FX colonies grew as much as absolute and negative controls, they produced stronger pigmentation than other oils. These pigmentation changes could be related to an alteration of spores development [39] and it has been stated that darker color in F. oxysporum equals reduced spore production resulting in reduced virulence. This discover could be a promising solution for food contamination and storage or soil health. The controlled virulence equals less spore production and reduced reproductive action. Although colony development was not fully inhibited as observed with Benlate, MR and PV led to lighter pink colorations on F. oxysporum (MT10) colonies and restricted growth originating smaller colonies when compared to negative control, which could suggest a lack of defensive response from the fungi due to oil toxicity (Figure 7). Seemingly this indicates that the oils did in fact trigger distinctive responses in F. oxysporum (MT10) which could be justified with the possible phytochemical heterogenicity present in Madeira variants. On the other hand, there were no significant morphological differences between the absolute control of A. niger (MT23) and the tested colonies apart from size.
Furthermore, Annona sp. properties have been investigated by several authors which demonstrated both its antifungal and antibacterial action. Abdul-Hammed et al. [39] tested the inhibitory influence of 131 phytochemicals isolated from Annona muricata against Candida albicans. The results showed that some Annona flavonoids and alkaloids achieved better inhibition against the tested pathogens than fluconazole and standard voriconazole, medicine used to treat fungal infections. Rabêlo et al. [14] achieved antimicrobial activity using leaf and stem extracts of Annona atemoya. The study concluded that Gram-positive bacteria exhibited a stronger reaction to the oil, suggesting a possible interaction with the peptidoglycans present in the cell membrane. Additionally, three different solvents were tested for oil extraction (methanol, ethanol, and hexanol), resulting in different levels of inhibition with only ethanol and methanol-based extracts inducing microbial sensitivity. The outcome was justified with the high concentration of flavonoids present in the ethanol and methanol substrates due to their molecular polarity, while the hexanol solvent extracted mostly terpenoids. However, Abdel-Rahman et al. [40] demonstrated that terpenoids extracted from Annona seeds have an inhibitory effect against diverse bacterial strains; the resulting extracts constrained P. aeruginosa proliferation greatly and controlled Escherichia coli and Staphylococcus aureus growth. Guerrero-Álvarez & Giraldo-Rivera [31] reported how different Annona species behave against selected bacteria. The results showed that A. cherimola exhibited action against E. faecalis and A. montana, while A. glabra and A. reticulata originated a response on S. aureus. The study concluded that this antimicrobial property could be associated to saponins which interact with the cell membrane present in Gram-positive bacteria. Once more, it was showed that the extracts obtained from different species had phytochemical differences which expressed distinctive bioaction.
The Anonanaceae family possesses exclusive substances of high interest that have been described previously by multiple studies [41,42]. Annonaceae acetogenins (ACGs) derive from long-chain fatty acids and are formed by hydroxyl groups, one to three tetrahydrofuran (THF) rings and 35 to 37 carbons attached to a terminal γ-lactone. ACGs can be found on different parts of Annona plants, including peel and seed which are not consumed and represent a considerable percentage of the fruit. Therefore, the biowaste has significant interest due to ACGs strong antimicrobial and antitumoral properties and studies have been carried with the intent of optimizing its extraction and further applications [43]. Research has unveiled the main mechanisms of action involved in ACGs activity. These metabolites compromise aerobic respiration by inhibiting ubiquinone oxidoreductase NADH, thus constraining the mitochondrial respiratory complex I. ACGs also interact with plasma enzymes responsible for the production of ATP under anoxic conditions, and histones interfering in gene regulation. Lastly, ACGs can induce early apoptosis by mitochondrial interaction or caspases 9 and 8 activation [44]. These characteristics make Annona ACGs great candidates for the creation of bio-antimicrobial products and further research to discover which mechanisms provoke sensitivity and growth inhibition in certain fungal and bacterial species.
Like other Annona species, A. cherimola Mill. composition is rich in phytochemicals, with distinctive properties and biological activity [42]. Research conducted with A. cherimola extracts, compared its antioxidant activity to Annona muricata L. extracts, showing higher yields on A. cherimola, associated to the presence of ACGs. Aguilar-Villalva et al. [44] confirmed once more the inhibitory impact of A. cherimola against Gram-positive bacteria. Thereby, A. cherimola is an effective source of ACGs, comprising an array of 41 ACGs, isolated from seeds, leaves, stems, and roots. These compounds present chemical diversity which originates distinct substances: the number of hydroxyl groups and positioning of the THF ring plays a crucial role, defining bioactivity [2]. Some identified ACGs remain unstudied and their effects unknown. There are no current studies describing the ACG profile of A. cherimola variants found in Madeira Island, and these clearly trigger defensive mechanisms in F. oxysporum and A. niger, suggesting the presence of antifungal compounds. The nutritional value and phytochemical analysis of Madeira variants are of high importance in order to understand the agents responsible for antifungal activity and to unfold potential new properties.
Plants are a rich source of bioactive elements with various beneficial effects, and diverse studies have shown that Annona cherimola Mill. has beneficial properties that act against fungal threats. All cherimoya variants tested in the present study triggered a reaction on both Fusarium oxysporum and Aspergillus niger, showing promising results for the use of peel and seed residues as ingredients for a potential bio-fungicide. The fungi presented different responses, from growth constriction to morphological changes when exposed to peel and seed oils and compared to absolute and negative controls. There were no significant differences between the tested tissues, but data suggests that 100 mg/mL peel oils from FX, PV, MT, and DM controlled better F. oxysporum growth, reducing up to 60% on the third day of post-inoculation and AN limited A. niger growth, reaching 100% inhibition. Apart from growth limitations, the oils originated morpho-changes on Fusarium oxysporum, which could be related to attenuated virulence. The different concentrations applied to the colonies exhibited similar action, with no considerable change between treatments. However, the measuring method using CA values to calculate IR obtained more accurate results when compared to the pictures, and is therefore a better representation of IR. The data acquired in this study confirmed the value of A. cherimola variants found in Madeira Island and provided useful information about their antifungal activity. Further research should be carried out to better understand the phytochemical background responsible for these results and ideally establish a nutritional profile on all variants, as well as the extraction and isolation of present ACGs.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
The authors acknowledge the funding from the project FRUTTMAC (INTERREGMAC 2014-2020, Ref. MAC/1.1.b/310). For the authors integrated in the CITAB research Center, this work was co-supported by National Funds by FCT – Portuguese Foundation for Science and Technology, under the projects UID/04033/2020: Centro de Investigação e de Tecnologias Agro-Ambientais e Biológicas and LA/P/0126/2020 (https://doi.org/10.54499/LA/P/0126/2020). We thank the Regional Secretary of Agriculture and Rural Development of the Autonomous Region of Madeira and Mr. Henrique Alves for providing the cherimoya varieties in study.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Conceptualization, C.T. and C.G.; methodology, C.T.; software, C.T. and C.G.; validation, M.O. and M.C.; formal analysis, C.T., C.G. and C.O.; investigation, C.T. and C.G; resources, H.N. and J.F.; data curation, C.T. and C.G.; writing – original draft preparation, C.T. and C.G.; writing – review and editing, C.T., C.G., M.O, H.N., J.F. and M.C.; visualization, C.T. and C.G.; supervision, C.G. and M.C.; project administration, C.G. and M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.
MT10–ITS1
CCAACCCCTGTGAACATACCACTTGTTGCCTCGGCGGATCAGCCCGCTCCCGGTAAAACGGGACGGCCCGCCAGAGGACCCCTAAACTCTGTTTCTATATGTAACTTCTGAGTAAAACCATAAATAAATCAAAACTTTCAACAACGGATCTCTTGGTTCTGGCATCGATGAAGAACGCAGCAAAATGCGATAAGTAATGTGAATTGCAGAATTCAGTGAATCATCGAATCTTTGAACGCACATTGCGCCCGCCAGTATTCTGGCGGGCATGCCTGTTCGAGCGTCATTTCAACCCTCAAGCACAGCTTGGTGTTGGGACTCGCGTTAATTCGCGTTCCCCAAATTGATTGGCGGTCACGTCGAGCTTCCATAGCGTAGTAGTAAAACCCTCGTTACTGGTAATCGTCGCGGCCACGCCGTTAAACCCCAACTTCTGAATGTTGACCTCGGATCAGGTAGGAATACCCGCTGAACTTAAGCATATCAATAAGCCGGAGGAA
Reference strain: Fusarium oxysporum Fo47; Sequence ID (Genebank): NC_072843.1; Percent identity: 99, 60%; Query cover: 99%
MT23–ITS1
CTTTGGGCCCACCTCCCATCCGTGTCTATTGTACCCTGTTGCTTCGGCGGGCCCGCCGCTTGTCGGCCGCCGGGGGGGCGCCTCTGCCCCCCGGGCCCGTGCCCGCCGGAGACCCCAACACGAACACTGTCTGAAAGCGTGCAGTCTGAGTTGATTGAATGCAATCAGTTAAAACTTTCAACAATGGATCTCTTGGTTCCGGCATCGATGAAGAACGCAGCGAAATGCGATAACTAATGTGAATTGCAGAATTCAGTGAATCATCGAGTCTTTGAACGCACATTGCGCCCCCTGGTATTCCGGGGGGCATGCCTGTCCGAGCGTCATTGCTGCCCTCAAGCCCGGCTTGTGTGTTGGGTCGCCGTCCCCCTCTCCGGGGGGACGGGCCCGAAAGGCAGCGGCGGCACCGCGTCCGATCCTCGAGCGTATGGGGCTTTGTCACATGCTCTGTAGGATTGGCCGGCGCCTGCCGACGTTTTCCAACCATTCTTTCCAGGTTGACCTCGGATCAGGTAGGGATACCCGCTGAACTTAAGCATATCAATAAGCCGGAGGAA
Reference strain: Aspergillus niger supercontig An03; Sequence ID (Genebank): NT_166520.1; Percent identity: 99, 64%; Query cover: 100%
[1] | IPCC (2014) Climate Change 2014: Impacts, adaptation, and vulnerability-Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom: Cambridge University Press. |
[2] | IPCC (2014) Climate Change 2014: Synthesis Report. Geneva, Switzerland: Intergovernmental Panel on Climate Change. Retrieved July 1, 2019, Available from: https://www.ipcc.ch/site/assets/uploads/2018/05/SYR_AR5_FINAL_full_wcover.pdf. |
[3] |
Abidoye BO, Kurukulasuriya P, Mendelsohn R (2017) South-East Asian farmer perceptions of climate change. Clim Change Econ 8: 1740006. doi: 10.1142/S2010007817400061
![]() |
[4] |
Konchar KM, Staver B, Salick J, et al. (2015) Adapting in the shadow of Annapurna: a climate tipping point. J Ethnobiol 35: 449-471. doi: 10.2993/0278-0771-35.3.449
![]() |
[5] | Aldunce P, Handmer J, Beilin R, et al. (2016) Is climate change framed as 'business as usual' or as a challenging issue? The practitioners' dilemma. Environ Plann 34: 999-1019. |
[6] | Scheffers BR, Meester L, Bridge TC, et al. (2016) The broad footprint of climate change from genes to biomes to people. Science 354: 6313. |
[7] |
Voccia A (2012) Climate change: What future for small, vulnerable states? Int J Sust Dev World Ecol 19: 101-115. doi: 10.1080/13504509.2011.634032
![]() |
[8] |
Spires M, Shackleton S, Cundill G (2014) Barriers to implementing planned community-based adaptation in developing countries: A systematic literature review. Clim Dev 6: 277-287. doi: 10.1080/17565529.2014.886995
![]() |
[9] | Bockel L, Vian L, Torre C (2016) Towards sustainable impact monitoring of green agriculture and forestry investments by NDBs: Adapting MRV methodology. Rome, Italy: Food and Agriculture Organization. |
[10] |
Glenn A, James WE, Fuller RA (2016) Global mismatch between greenhouse gas emissions and the burden of climate change. Sci Rep 6: 20281. doi: 10.1038/srep20281
![]() |
[11] |
Jackson G, McNamara K, Witt B (2017) A framework for disaster vulnerability in a small island in the Southwest Pacific: A case study of Emae Island, Vanuatu. Int J Disaster Risk Sci 8: 358-373. doi: 10.1007/s13753-017-0145-6
![]() |
[12] |
Easterling WE (1996) Adapting North American agriculture to climate change in review. Agric For Meteorol 80: 1-53. doi: 10.1016/0168-1923(95)02315-1
![]() |
[13] | FAO (2007) Adaptation to climate change in agriculture, forestry and fisheries: Perspective, framework and priorities. Rome: Food and Agriculture Organization. |
[14] |
Berrang-Ford L, Pearce T, Ford JD (2015) Systematic review approaches for climate change adaptation research. Reg Environ Chang 15: 755-769. doi: 10.1007/s10113-014-0708-7
![]() |
[15] | Ifejika-Speranza C (2010) Resilient adaptation to climate change in African Agriculture. Bonn: Deutsches Institut für Entwicklungspolitik (DIE). |
[16] | Abiodun BJ, Salami AT, Tadross M (2011) Climate change scenarios for Nigeria: Understanding biophysical impacts. Ibadan, Nigeria: Building Nigeria's Response to Climate Change (BNRCC) Project. |
[17] | IPCC (2018) Global warming of 1.5 ℃ Geneva, Switzerland: Intergovernmental Panel on climate change. |
[18] | NBS (2017) Nigerian Gross Domestic Product Report, Abuja, Nigeria: National Bureau of Statistics. |
[19] | Yakubu MM, Akanegbu BN (2015) The Impact of international trade on economic growth in Nigeria: 1981-2012. Eur J Bus Econ Account 3: 26-36. |
[20] |
Berg A, de Noblet-Ducoudre N, Benjamin S, et al. (2013) Projections of climate change impacts on potential C4 crop productivity over tropical regions. Agric For Meteorol 170: 89-102. doi: 10.1016/j.agrformet.2011.12.003
![]() |
[21] | Mereu V, Santini M, Cervigni R, et al (2018) Robust decision making for a climate-resilient development of the agricultural sector in Nigeria. In: Lipper L, McCarthy N, Zilberman D, et al., Eds., Climate Smart Agriculture: Building Resilience to Climate Change, Rome, Italy: Food and Agriculture Organization of the United Nations (FAO), 277-306. |
[22] | Adejuwon JO (2005) Food crop production in Nigeria. Present effects of climate variability. Clim Res 30: 53-60. |
[23] |
Odekunle TO (2004) Rainfall and the length of the growing season in Nigeria. Int J Climatol 24: 467-479. doi: 10.1002/joc.1012
![]() |
[24] |
Remling E, Veitayaki J (2016) Community-based action in Fiji's Gau Island: A model for the Pacific? Int J Clim Chang Str Manage 8: 375-398. doi: 10.1108/IJCCSM-07-2015-0101
![]() |
[25] | Ogbo A, Lauretta NE, Ukpere W (2013) Risk management and challenges of climate change in Nigeria. J Hum Ecol 41: 221-235. |
[26] |
Obioha EE (2008) Climate change, population drift and violent conflict over land resources in Northeastern Nigeria. J Hum Ecol 23: 311-324. doi: 10.1080/09709274.2008.11906084
![]() |
[27] |
Mburu BM, Kung'u JB, Muriuku JN (2015) Climate change adaptation strategies by small-scale farmers in Yatta District, Kenya. Afr J Environ Sci Technol 9: 712-722. doi: 10.5897/AJEST2015.1926
![]() |
[28] | Cooper C, Booth A, Varley-Campbell J, et al. (2018) Defining the process to literature searching in systematic reviews: A literature review of guidance and supporting studies. BMC Med Res Methodol 85: 1-14. |
[29] |
Babatunde KA, Begum RA, Said FF (2017) Application of computable general equilibrium (CGE) to climate change mitigation policy: A systematic review. Renew Sust Energ Rev 78: 61-71. doi: 10.1016/j.rser.2017.04.064
![]() |
[30] |
Escarcha JF, Lassa JA, Zander KK (2018) Livestock under climate change: A systematic review of impacts and adaptation. Climate 6: 54. doi: 10.3390/cli6030054
![]() |
[31] |
Folke C (2006) Resilience: The emergence of a perspective for social-ecological systems analyses. Global Environ Chang 16: 253-267. doi: 10.1016/j.gloenvcha.2006.04.002
![]() |
[32] |
Ifejika-Speranza C (2013) Buffer capacity: Capturing a dimension of resilience to climate change in African smallholder agriculture. Reg Environ Chang 13: 521-535. doi: 10.1007/s10113-012-0391-5
![]() |
[33] |
Pretty J (2008) Agricultural sustainability: Concepts, principles and evidence. Philos Trans R Soc Lond B Biol Sci 363: 447-465. doi: 10.1098/rstb.2007.2163
![]() |
[34] | Dorward A, Anderson S, Clark S (2001) Asset functions and livelihood strategies: A framework for pro-poor analysis, policy and practice. Imperial College at Wye, Department of Agricultural Sciences: ADU Working Papers 10918. |
[35] |
Shaffril HA, Krauss SE, Samsuddin SF (2018) A systematic review on Asian's farmers' adaptation practices towards climate change. Sci Total Environ 644: 683-695. doi: 10.1016/j.scitotenv.2018.06.349
![]() |
[36] |
Moher D, Liberati A, Tetzlaff J, et al. (2009) Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med 6: e1000097. doi: 10.1371/journal.pmed.1000097
![]() |
[37] | Singh C, Deshpande T, Basu R (2017) How do we assess vulnerability to climate change in India? A systematic review of literature. Reg Environ Chang 17: 527-538. |
[38] |
Rusinamhodzi L, Corbeels M, Nyamangara J, et al. (2012) Maize-grain legume intercropping is an attractive option for ecological intensification that reduces climatic risk for smallholder farmers in central Mozambique. Field Crops Res 136: 12-22. doi: 10.1016/j.fcr.2012.07.014
![]() |
[39] |
Challinor A, Wheeler T, Garfoth C, et al. (2007) The vulnerability of food crop systems in Africa to climate change. Clim Chang 83: 381-399. doi: 10.1007/s10584-007-9249-0
![]() |
[40] |
Morton JF (2007) The impact of climate change on smallholder and subsistence agriculture. Proc Natl Acad Sci USA 104: 19680-19685. doi: 10.1073/pnas.0701855104
![]() |
[41] |
Armbrecht I, Gallego-Ropero MC (2007) Testing ant predation on the coffee berry borer in shaded and sun coffee plantations in Colombia. Entomol Exp Appl 124: 261-267. doi: 10.1111/j.1570-7458.2007.00574.x
![]() |
[42] |
Lin BB (2011) Resilience in agriculture through crop diversification: Adaptive management for environmental change. BioScience 61: 183-193. doi: 10.1525/bio.2011.61.3.4
![]() |
[43] | Grubben G, Klaver W, Nono-Womdim R, et al. (2014) Vegetables to combat the hidden hunger in Africa. Chronica Hort 54: 24-32. |
[44] | Luoh JW, Begg CB, Symonds RC, et al. (2014) Nutritional yield of African indigenous vegetables in water-deficient and water-sufficient conditions. Food Nutri Sci 5: 812-822. |
[45] |
Lunduka RW, Mateva KL, Magoroshoko C, et al. (2019) Impact of adoption of drought-tolerant maize varieties on total maize production in south Eastern Zimbabwe. Clim Dev 11: 35-46. doi: 10.1080/17565529.2017.1372269
![]() |
[46] | Akinnagbe OM, Irohibe IJ (2014) Agricultural adaptation strategies to climate change impacts in Africa: A review. Bangladesh J Agric Res 39: 407-418. |
[47] |
Waha K, Müller C, Bondeau A, et al. (2013) Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa. Global Environ Chang 23: 130-143. doi: 10.1016/j.gloenvcha.2012.11.001
![]() |
[48] | Atedhor GO (2015) Strategies for agricultural adaptation to climate change in Kogi state, Nigeria. Ghana J Geogr 7: 20-37. |
[49] |
Westengen OT, Brysting AK (2014) Crop adaptation to climate change in the semi-arid zone in Tanzania: The role of genetic resources and seed systems. Agri Food Secur 3: 1-12. doi: 10.1186/2048-7010-3-1
![]() |
[50] | Sanz MJ, de Vente J, Chotte JL, et al. (2017) Sustainable land management contribution to successful land-based climate change adaptation and mitigation: A report of the science-policy interface. Bonn, Germany: United Nations Convention to Combat Desertification (UNCCD). |
[51] | FAO (2017) Voluntary guidelines for sustainable soil management. Rome, Italy: Food and Agriculture Organization. |
[52] |
Stavi I (2013) Biochar use in forestry and tree-based agro-ecosystems for increasing climate change mitigation and adaptation. Int J Sust DevWorld Ecol 20: 166-181. doi: 10.1080/13504509.2013.773466
![]() |
[53] |
Lal R (2015) Sequestering carbon and increasing productivity by conservation agriculture. J Soil Water Conserv 70: 55-62. doi: 10.2489/jswc.70.3.55A
![]() |
[54] |
Stavi I, Bel G, Zaady E (2016) Soil functions and ecosystem services in conventional, conservation, and integrated agricultural systems. A review. Agron Sustain Dev 36: 1-12. doi: 10.1007/s13593-015-0343-9
![]() |
[55] |
Agbonlahor MU, Aromolaran AB, Aiboni VI (2003) Sustainable soil management practices in small farms of southern Nigeria: A poultry-food crop integrated farming approach. J Sustain Agric 22: 51-62. doi: 10.1300/J064v22n04_05
![]() |
[56] | Thierfelder C, Matemba-Mutasa R, Rusinamhodzi L (2015) Yield response of maize (Zea mays L.) to conservation agriculture cropping system in Southern Africa. Soil Till Res 146: 230-242. |
[57] | Oyekale AS, Oladele OI (2012) Determinants of climate change adaptation among cocoa farmers in Southwest Nigeria. ARPN J Sci Technol 2: 154-168. |
[58] |
Merrey DJ, Sally H (2008) Micro-AWM Technologies for food security in Southern Africa: Part of the solution or a red herring? Water Policy 10: 515-530. doi: 10.2166/wp.2008.025
![]() |
[59] | CGIAR (2016) Agricultural practices and technologies to enhance food security, resilience and productivity in a sustainable manner: Messages to SBSTA 44 agriculture workshops, CCAFS Working Paper no. 146, Copenhagen, Denmark, 2016. |
[60] |
Abraham TW, Fonta WM (2018) Climate change and financing adaptation by farmers in northern Nigeria. Financ Innov 4: 11. Available from: https://doi.org/10.1186/s40854-018-0094-0. doi: 10.1186/s40854-018-0094-0
![]() |
[61] | BNRCC (2011) Reports of pilot projects in community-based adaptation to climate change in Nigeria. Ibadan, Nigeria: Building Nigeria's Response to Climate Change (BNRCC) Project. |
[62] |
Asfaw A, Simane B, Hassen A, et al. (2017) Determinants of non-farm livelihood diversification: Evidence from rainfed-dependent smallholder farmers in Northcentral Ethiopia (Woleka sub-basin). Dev Stud Res 4: 22-36. doi: 10.1080/21665095.2017.1413411
![]() |
[63] | Nzegbule EC, Nwajiuba C, Ujor G, et al. (2019) Sustainability and the effectiveness of BNRCC community-based adaptation (CBA) to address climate change impact in Nigeria. In: Leal FW Eds., Handbook of Climate Change Resilience, Cham: Springer, 1-22. |
[64] |
Akrofi-Atitianti F, Ifejika-Speranza C, Bockel L, et al. (2018) Assessing climate smart agriculture and its determinants of practice in Ghana: A case of the cocoa production system. Land 7: 30. doi: 10.3390/land7010030
![]() |
[65] | Adepoju AO, Osunbor PP (2018) Small scale poultry farmers' choice of adaption strategies to climate change in Ogun State, Nigeria. Rural Sustain Res 40: 32-40. |
[66] | Salem BH, López-Francos A (2012) Feeding and management strategies to improve livestock productivity, welfare and product quality under climate change. 14th International Seminar of the Sub-Network on Nutrition of the FAO-CIHEAM Inter-Regional Cooperative Research and Development Network on Sheep and Goats. Hammamet, Tunisia. |
[67] | IAEA (2010) Improving livestock production using indigenous resources and conserving the environment. Vienna, Austria: International Atomic Energy Agency. |
[68] | Lamy E, van Harten S, Sales-Baptista E, et al. (2012) Factors influencing livestock productivity. In: Sejian V, Naqvi SM, Ezeji T, et al. Eds., Environmental Stress and Amelioration in Livestock Production, Berlin, Germany: Springer, 19-51. |
[69] | Gebremedhin B, Hoekstra D, Jemaneh S (2007) Heading towards commercialization? The case of live animal marketing in Ethiopia. Nairobi, Kenya: Improving Productivity and Market Success (IPMS) of Ethiopian Farmers. Working Paper 5. ILRI (International Livestock Research Institute). |
[70] | Batima P (2006) Climate change vulnerability and adaptation in the livestock sector of Mongolia. Washington, DC: Assessments of Impacts and Adaptations to Climate Change (AIACC), Project No. AS 06. |
[71] | Okonkwo WI, Akubuo CO (2001) Thermal analysis and evaluation of heat requirement of a passive solar energy poultry chick brooder. Nig J Renew Energ 9: 83-87. |
[72] |
Nyoni NM, Grab S, Archer ER (2019) Heat stress and chickens: Climate risk effects on rural poultry farming in low-income countries. Clim Dev 11: 83-90. doi: 10.1080/17565529.2018.1442792
![]() |
[73] |
Elijah OA, Adedapo A (2006) The effect of climate on poultry productivity in Ilorin Kwara State, Nigeria. Int J Poult Sci 5: 1061-1068. doi: 10.3923/ijps.2006.1061.1068
![]() |
[74] | Ampaire A, Rothschild MF (2010) Effects of training and facilitation of farmers in Uganda on livestock development. Livest Res Rural Dev 22: 1-7. |
[75] | Shelton C (2014) Climate change adaptation in fisheries and aquaculture: Compilation of initial examples. FAO Fisheries and Agriculture Circular No. 1088. Rome: Food and Agriculture Organization. |
[76] |
Ficke AD, Myrick CA, Hansen LJ (2007) Potential impacts of global climate change on freshwater fisheries. Rev Fish Biol Fisher 17: 581-613. doi: 10.1007/s11160-007-9059-5
![]() |
[77] | Nwabeze GO, Erie AP, Erie GO (2012) Fishers' adaptation to climate change in the Jebba Lake Basin, Nigeria. J Agric Ext 16: 68-78. |
[78] | Adebayo OO (2012) Climate change perception and adaptation strategies on catfish farming in Oyo State, Nigeria. Glob J Sci Frontier Res Agric Vet Sci 12: 1-7. |
[79] | Huq S, Reid H (2007) Community-based adaptation: A vital approach to the threat climate change poses to the poor. London: International Institute for Environment and Development. |
[80] | Achoja FO, Oguh VO (2018) Income effect of climate change adaptation technologies among crop farmers in Delta State, Nigeria. Int J Agric Rural Dev 21: 3611-3616. |
[81] | Agomuo CI, Asiabaka CC, Nnadi FN, et al. (2015) Rural women farmers' use of adaptation strategies to climate change in Imo State. Nigeria Int J Agric Rural Dev 18: 2305-2310. |
[82] | Ajayi JO (2015) Adaptation strategies to climate change by farmers in Ekiti State, Nigeria. Appl Trop Agric 20: 01-07. |
[83] | Ajieh PC, Okoh RN (2012) Constraints to the implementation of climate change adaptation measures by farmers in delta state, Nigeria. Glob J Sci Frontier Res Agric Vet Sci 12: 1-7. |
[84] | Akinbile LA, Oluwafunmilayo AO, Kolade RI (2018) Perceived effect of climate change on forest dependent livelihoods in Oyo State, Nigeria. J Agric Ext 22: 169-179. |
[85] | Akinwalere BO (2017) Determinants of adoption of agroforestry practices among farmers in Southwest Nigeria. Appl Trop Agric 22: 67-72. |
[86] | Anyoha NO, Nnadi FN, Chikaire J, et al. (2013) Socio-economic factors influencing climate change adaptation among crop farmers in Umuahia South Area of Abia State, Nigeria. Net J Agric Sci 1: 42-47. |
[87] | Apata TG (2011) Factors influencing the perception and choice of adaptation measures to climate change among farmers in Nigeria: Evidence from farm households in Southwest Nigeria. Environ Econ 2: 74-83. |
[88] | Arimi K (2014) Determinants of climate change adaptation strategies used by rice farmers in Southwestern, Nigeria. J Agr Rural Dev Trop 115: 91-99. |
[89] | Asadu AN, Ozioko RI, Dimelu MU (2018) Climate change information source and indigenous adaptation strategies of cucumber farmers in Enugu State, Nigeria. J Agric Ext 22: 136-146. |
[90] |
Ayanlade A, Radeny M, Morton JF (2017) Comparing smallholder farmers' perception of climate change with meteorological data: A case study from southwestern Nigeria. Weather Clim Extremes 15: 24-33. doi: 10.1016/j.wace.2016.12.001
![]() |
[91] | Ayoade AR (2012) Determinants of climate change on cassava production in Oyo State, Nigeria. Glob J Sci Frontier Res Agric Vet Sci 12: 1-7. |
[92] | Chukwuone N (2015) Analysis of impact of climate change on growth and yield of yam and cassava and adaptation strategies by farmers in Southern Nigeria. African Growth and Development Policy Modelling Consortium Working Paper 0012. Dakar-Almadies, Senegal: African Growth and Development Policy Modelling Consortium. |
[93] | Chukwuone NA, Chukwuone C, Amaechina EC (2018) Sustainable land management practices used by farm households for climate change adaptation in South East Nigeria. J Agric Ext 22: 185-194. |
[94] | Emodi AI, Bonjoru FH (2013) Effects of climate change on rice farming in Ardo Kola Local Government Area of Taraba State, Nigeria. Agric J 8: 17-21. |
[95] | Enete AA, Madu II, Mojekwu JC, et al. (2011) Indigenous agricultural adaptation to climate change: Study of Imo and Enugu States in Southeast Nigeria. African Technology Policy Studies Network Working Paper No. 53. Nairobi: African Technology Policy Studies Network. |
[96] | Enete AA, Otitoju MA, Ihemezie EJ (2015) The choice of climate change adaptation strategies among food crop farmers in Southwest Nigeria. Nig J Agric Econ 5: 72-80. |
[97] | Eregha PB, Babatolu JS, Akinnubi RT (2014) Climate change and crop production in Nigeria: An error correction modelling approach. Int J Energ Econ Policy 4: 297-311. |
[98] | Esan VI, Lawi MB, Okedigba I (2018) Analysis of cashew farmers adaptation to climate change in South-Western Nigeria. Asian J Agric Ext Econ Sociol 23: 1-12. |
[99] |
Ezeh AN, Eze AV (2016) Farm-level adaptation measures to climate change and constraints among arable crop farmers in Ebonyi State of Nigeria. Agric Res J 53: 492-500. doi: 10.5958/2395-146X.2016.00098.3
![]() |
[100] | Ezike KN (2019) Implications for mitigation and adaptation measures: Rice farmers' response and constraints to climate change in Ivo Local Government Area of Ebonyi State. In: Leal FW Eds., Handbook of Climate Change Resilience, Cham: Springer, 1787-1799. |
[101] | Falola A, Achem BA (2017) Perceptions on climate change and adaptation strategies among sweet potato farming households in Kwara State, Northcentral Nigeria. Ceylon J Sci 46: 55-63. |
[102] | Farauta BK, Egbule CL, Idrisa YL, et al. (2011) Farmers' perceptions of climate change and adaptation strategies in Northern Nigeria: An empirical assessment. African Technology Policy Studies Network Research Paper No 15. Nairobi, Kenya: African Technology Policy Studies Network. |
[103] | Henri-Ukoha A, Adesope OM (2019) Sustainability of climate change adaptation measures in Rivers State, South-South, Nigeria. In: Leal FW, Eds., Handbook of Climate Change Resilience, Cham: Springer, 675-683. |
[104] | Ifeanyi-Obi CC, Asiabaka CC, Matthews-Njoku E, et al. (2012) Effects of climate change on fluted pumpkin production and adaptation measures used among farmers in Rivers State. J Agric Ext 16: 50-58. |
[105] | Ifeanyi-Obi CC, Asiabaka CC, Adesope OM (2014) Determinants of climate change adaptation measures used by crop and livestock farmers in Southeast Nigeria. J Human Soc Sci 19: 61-70. |
[106] | Igwe AA (2018) Effect of livelihood factors on climate change adaptation of rural farmers in Ebonyi State. J Biol Agric Healthc 8: 10-15. |
[107] | Iheke OR, Agodike WC (2016) Analysis of factors influencing the adoption of climate change mitigating measures by smallholder farmers in Imo State, Nigeria. Sci Papers Ser Manag Econ Eng Agric Rural Dev 16: 213-220. |
[108] | Ihenacho RA, Orusha JO, Onogu B (2019) Rural farmers use of indigenous knowledge systems in agriculture for climate change adaptation and mitigation in Southeast Nigeria. Ann Ecol Environ Sci 3: 1-11. |
[109] | Ikehi ME, Onu FM, Ifeanyieze FO, et al. (2014) Farming families and climate change issues in Niger Delta Region of Nigeria: Extent of impact and adaptation strategies. Agric Sci 5: 1140-1151. |
[110] | Kim I, Elisha I, Lawrence E, et al. (2017) Farmers adaptation strategies to the effect of climate variation on rice production: Insight from Benue State, Nigeria. Environ Ecol Res 5: 289-301. |
[111] | Koyenikan MJ. Anozie O (2017) Climate change adaptation needs of male and female oil palm entrepreneurs in Edo State, Nigeria. J Agric Ext 21: 162-175. |
[112] | Mbah EN, Ezeano CI, Saror SF (2016) Analysis of climate change effects among rice farmers in Benue State, Nigeria. Curr Res Agric Sci 3: 7-15. |
[113] | Mustapha SB, Undiandeye UC, Gwary MM (2012) The role of extension in agricultural adaptation to climate change in the Sahelian Zone of Nigeria. J Environ Earth Sci 2: 48-58. |
[114] | Mustapha SB, Alkali A, Zongoma BA, et al. (2017) Effects of climatic factors on preference for climate change adaptation strategies among food crop farmers in Borno State, Nigeria. Int Acad Inst Sci Technol 4: 23-31. |
[115] | Nnadi FN, Chikaire J, Nnadi CD, et al. (2012) Sustainable land management practices for climate change adaptation in Imo State, Nigeria. J Emerg Trends Eng Appl Sci 3: 801-805. |
[116] | Nwaiwu IU, Ohajianya DO, Orebiyi JS, et al. (2014) Climate change trend and appropriate mitigation and adaptation strategies in Southeast Nigeria. Glob J Biol Agric Health Sci 3: 120-125. |
[117] | Nwalieji HU, Onwubuya EA (2012) Adaptation practices to climate change among rice farmers in Anambra State of Nigeria. J Agric Ext 16: 42-49. |
[118] | Nwankwo GC, Nwaobiala UC, Ekumankama OO, et al. (2017) Analysis of perceived effect of climate change and adaptation among cocoa farmers in Ikwuano Local Government Area of Abia State, Nigeria. ARPN J Sci Technol 7: 1-7. |
[119] | Nzeadibe TC, Egbule CL, Chukwuone NA, et al. (2011) Climate change awareness and adaptation in the Niger Delta Region of Nigeria. Nairobi, Kenya: African Technology Policy Studies Network Working Paper Series No.57. Nairobi, Kenya: African Technology Policy Studies Network. |
[120] | Obayelu OA, Adepoju AO, Idowu T (2014) Factors influencing farmers' choices of adaptation to climate change in Ekiti State, Nigeria. J Agric Environ Int Dev 108: 3-16. |
[121] |
Ofuoku AU (2011) Rural farmers' perception of climate change in central agricultural zone of Delta State, Nigeria. Indones J Agric Sci 12: 63-69. doi: 10.21082/ijas.v12n2.2011.p63-69
![]() |
[122] | Ogbodo JA, Anarah SE, Abubakar SM (2018) GIS-based assessment of smallholder farmers' perception of climate change impacts and their adaptation strategies for maize production in Anambra State, Nigeria. In: Amanullah, & S. Fahad (Eds.), Corn production and human health in changing climate, 115-138. |
[123] | Ogogo AU, Ekong MU, Ifebueme NM (2019) Climate change awareness and adaptation measures among farmers in Cross River and Akwa Ibom States of Nigeria. In: Leal FW (Ed), Handbook of Climate Change Resilience, 1983-2002, Cham: Springer. |
[124] | Okpe B, Aye GC (2015) Adaptation to climate change by farmers in Makurdi, Nigeria. J Agric Ecol Res Int 2: 46-57. |
[125] | Oluwatusin FM (2014) The perception of and adaptation to climate change among cocoa farm households in Ondo State, Nigeria. Acad J Interdiscipli Stud 3: 147-156. |
[126] | Oluwole AJ, Shuaib L, Dasgupta P (2016) Assessment of level of use of climate change adaptation strategies among arable crop farmers in Oyo and Ekiti States, Nigeria. J Earth Sci Clim Chang 7: 369. |
[127] | Onyeagocha SU, Nwaiwu IU, Obasi PC, et al. (2018) Encouraging climate smart agriculture as part solution to the negative effects of climate change on agricultural sustainability in Southeast Nigeria. Int J Agric Rural Dev 21: 3600-3610. |
[128] |
Onyegbula CB, Oladeji JO (2017) Utilization of climate change adaptation strategies among rice farmers in three states of Nigeria. J Agric Ext Rural Dev 9: 223-229. doi: 10.5897/JAERD2017.0895
![]() |
[129] | Onyekuru NA (2017) Determinants of adaptation strategies to climate change in Nigerian forest communities. Nig Agric Policy Res J 3: 42-59. |
[130] | Onyeneke RU (2016) Effects of livelihood strategies on sustainable land management practices among food crop farmers in Imo State, Nigeria. Nig J Agric Food Environ 12: 230-235. |
[131] | Onyeneke RU (2018) Challenges of adaptation to climate change by farmers Anambra State, Nigeria. Int J BioSciences Agric Technol 9: 1-7. |
[132] | Onyeneke RU, Madukwe DK (2010) Adaptation measures by crop farmers in the Southeast Rainforest Zone of Nigeria to climate change. Sci World J 5: 32-34. |
[133] | Onyeneke RU, Iruo FA, Ogoko IM (2012) Micro-level analysis of determinants of farmers' adaptation measures to climate change in the Niger Delta Region of Nigeria: Lessons from Bayelsa State. Nig J Agric Econ 3: 9-18. |
[134] | Tarfa PY, Ayuba HK, Onyeneke RU, et al. (2019) Climate change perception and adaptation in Nigeria's Guinea Savanna: Empirical evidence from farmers in Nasarawa State, Nigeria. Appl Ecol Environ Res 17: 7085-7112. |
[135] |
Onyeneke R, Mmagu CJ, Aligbe JO (2017) Crop farmers' understanding of climate change and adaptation practices in South-east Nigeria. World Rev Sci Technol Sust Dev 13: 299-318. doi: 10.1504/WRSTSD.2017.089544
![]() |
[136] | Oriakhi LO, Ekunwe PA, Erie GO, et al. (2017) Socio-economic determinants of farmers' adoption of climate change adaptation strategies in Edo State, Nigeria. Nig J Agric Food Environ 13: 115-121. |
[137] | Orowole PF, Okeowo TA, Obilaja OA (2015) Analysis of level of awareness and adaptation strategies to climate change among crop farmers in Lagos State, Nigeria. Int J Appl Res Technol 4: 8-15. |
[138] | Oruonye ED (2014) An Assessment of the level of awareness of climate change and variability among rural farmers in Taraba State, Nigeria. Int J Sustain Agric Res 1: 70-84. |
[139] | Oselebe HO, Nnamani CV, Efisue A, et al. (2016) Perceptions of climate change and variability, impacts and adaptation strategies by rice farmers in south east Nigeria. Our Nature 14: 54-63. |
[140] | Oti OG, Enete AA, Nweze NJ (2019) Effectiveness of climate change adaptation practices of farmers in Southeast Nigeria: An empirical approach. Int J Agric Rural Dev 22: 4094-4099. |
[141] |
Owombo PT, Koledoye GF, Ogunjimi SI, et al. (2014) Farmers' adaptation to climate change in Ondo State, Nigeria: A gender analysis. J Geog Reg Plann 7: 30-35. doi: 10.5897/JGRP12.071
![]() |
[142] | Ozor N, Madukwe MC, Enete AA, et al. (2012) A framework for agricultural adaptation to climate change in Southern Nigeria. Int J Agric 4: 243-251. |
[143] | Sangotegbe NS, Odebode SO, Onikoyi MP (2012) Adaptation strategies to climate change by food crop farmers in Oke-Ogun Area of South Western Nigeria. J Agric Ext 16: 119-131. |
[144] | Sanni DO (2018) Local knowledge of climate change among arable farmers in selected locations in Southwestern Nigeria. In: Leal FW, Eds., Handbook of Climate Change Resilience, Cham: Springer, 1-18. |
[145] | Solomon E, Edet OG (2018) Determinants of climate change adaptation strategies among farm households in Delta State, Nigeria. Curr Invest Agric Curr Res 5: 615-620. |
[146] | Tanko L, Muhsinat BS (2014) Arable crop farmers' adaptation to climate change in Abuja, Federal Capital Territory, Nigeria. J Agric Crop Res 2: 152-159. |
[147] | Usman MN, Ibrahim FD, Tanko L (2016) Perception and adaptation of crop farmers to climate change to in Niger State, Nigeria. Nig J Agric Food Environ 12: 186-193. |
[148] | Uzokwe UN, Okonkwo JC (2012) Survival strategies of women farmers against climate change in Delta State and implication for extension services. Banat J Biotechnol 3: 97-103. |
[149] |
Weli VE, Bajie S (2017) Adaptation of Root crop farming system to climate change in Ikwerre Local Government Area of Rivers State, Nigeria. Am J Clim Chang 6: 40-51. doi: 10.4236/ajcc.2017.61003
![]() |
[150] | Chah JM, Odo E, Asadu AN, et al. (2013) Poultry farmers' adaptation to climate change in Enugu North Agricultural Zone of Enugu State, Nigeria. J Agric Ext 17: 100-114 |
[151] | Chah JM, Attamah CO, Odoh EM (2018) Differences in climate change effects and adaptation strategies between male and female livestock entrepreneurs in Nsukka Agricultural Zone of Enugu State, Nigeria. J Agric Ext 22: 105-115 |
[152] | Ibrahim FD, Azemheta T (2016) Climate change effects and perception on smallholder poultry farms in Lokoja Local Government Area of Kogi State: Implications for Policy Intervention. Nig J Agric Food Environ 12: 164-173. |
[153] | Tologbonse EB, Iyiola-Tunji AO, Issa FO, et al. (2011) Assessment of climate change adaptive strategies in small ruminant production in rural Nigeria. J Agric Ext 15: 40-57. |
[154] | Ume SI, Ezeano CI, Anozie R (2018) Climate change and adaptation coping strategies among sheep and goat farmers in Ivo Local Government Area of Ebonyi State, Nigeria. Sustain Agri Food Environ Res 6: 50-68. |
[155] | Adeleke ML, Omoboyeje VO (2016) Effects of climate change on aquaculture production and management in Akure Metropolis, Ondo State, Nigeria. Nig J of Fish Aquacult 4: 50-58. |
[156] | Aphunu A, Nwabeze GO (2012) Fish farmers' perception of climate change impact on fish production in Delta State, Nigeria. J Agric Ext 16: 1-13. |
[157] | Owolabi ES, Olokor J (2016) Climate change and fish farmers adaptation: A case study of New Bussa fishing population. J Natur Sci Res 6: 123-141. |
[158] | Amusa TA, Okoye CU, Enete AA (2015) Determinants of climate change adaptation among farm households in Southwest Nigeria: A heckman's double stage selection approach. Rev Agric Appl Econ 18: 3-11. |
[159] | NEST, Woodley E (2012) Learning from experience: Community-based adaptation to climate change in Nigeria. Ibadan, Nigeria: Building Nigeria's response to climate change. |
[160] | BNRCC, FederalMinistry of Environment (2011) National Adaptation Strategy and Plan of Action on Climate Change for Nigeria (NASPA-CCN). Abuja, Nigeria: Federal Ministry of Environment (Climate Change Department). |
[161] | Oladipo E (2010) Towards enhancing the adaptive capacity of Nigeria: A Review of the Country's state of preparedness for climate change adaptation. Abuja, Nigeria: Report Submitted to Heinrich Böll Foundation Nigeria. |
[162] | Tijjani AR, Chikaire JU (2016) Fish farmers perception of the effects of climate change on water resource use in Rivers State, Nigeria. J Sci Eng Res 3: 347-353. |