
Studies have determined the factors influencing agricultural drought resilience of smallholder farmers and implications for empowerment. Other than the Abbreviated Women's Empowerment in Agriculture Index (A-WEAI), studies do not provide an analysis of cultural or traditional beliefs and reflective dialogues on challenges of smallholder female livestock farmers. This study uses a mixed approach that includes a survey, A-WEAI, Pearson's chi-square coefficient, and reflective dialogue to analyze these challenges. The ability to adapt to agricultural drought is influenced by factors such as access to information, credit, productive resources, and available time, all of which are different for men and women. Our study found that 61.3% and 16.4% of the female and male farmers were disempowered. Domains that contributed the most to the disempowerment of the women and men were respectively time/workload (52.97% and 31.89%), access to and decisions on credit (17.7% and 21.4%), ownership of assets (11.3% and 8.5%), input into productive decisions (10% and 9.1%) and group membership (8% and 19.13%). No significant correlation for age, marital status, or level of education versus empowerment status of women was found. A significant correlation was observed between farming experience and the empowerment status of women. Reflective dialogue during interviews revealed that women struggled with access to finance, grazing, water, stock theft, lack of training and knowledge, and intimidation by male neighbors. Such findings help inform agricultural development strategies to develop or modify existing policies to enhance the resilience of farmers to agricultural drought and empowerment. Gender-specific agricultural projects should be encouraged to empower female farmers to improve their resilience to agricultural drought. The government should assist female livestock farmers in accessing credit and developing clear policies on land tenure issues. Mentorship programs should be encouraged to educate and support female smallholder farmers to enhance their agricultural drought resilience and empowerment.
Citation: Lindie V. Maltitz, Yonas T. Bahta. Empowerment of smallholder female livestock farmers and its potential impacts to their resilience to agricultural drought[J]. AIMS Agriculture and Food, 2021, 6(2): 603-630. doi: 10.3934/agrfood.2021036
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Studies have determined the factors influencing agricultural drought resilience of smallholder farmers and implications for empowerment. Other than the Abbreviated Women's Empowerment in Agriculture Index (A-WEAI), studies do not provide an analysis of cultural or traditional beliefs and reflective dialogues on challenges of smallholder female livestock farmers. This study uses a mixed approach that includes a survey, A-WEAI, Pearson's chi-square coefficient, and reflective dialogue to analyze these challenges. The ability to adapt to agricultural drought is influenced by factors such as access to information, credit, productive resources, and available time, all of which are different for men and women. Our study found that 61.3% and 16.4% of the female and male farmers were disempowered. Domains that contributed the most to the disempowerment of the women and men were respectively time/workload (52.97% and 31.89%), access to and decisions on credit (17.7% and 21.4%), ownership of assets (11.3% and 8.5%), input into productive decisions (10% and 9.1%) and group membership (8% and 19.13%). No significant correlation for age, marital status, or level of education versus empowerment status of women was found. A significant correlation was observed between farming experience and the empowerment status of women. Reflective dialogue during interviews revealed that women struggled with access to finance, grazing, water, stock theft, lack of training and knowledge, and intimidation by male neighbors. Such findings help inform agricultural development strategies to develop or modify existing policies to enhance the resilience of farmers to agricultural drought and empowerment. Gender-specific agricultural projects should be encouraged to empower female farmers to improve their resilience to agricultural drought. The government should assist female livestock farmers in accessing credit and developing clear policies on land tenure issues. Mentorship programs should be encouraged to educate and support female smallholder farmers to enhance their agricultural drought resilience and empowerment.
A BCI provides a pathway for people suffering from neuro-muscular dysfunctions to communicate with the world [1], by decoding electroencephalography (EEG) signals that reflect synchronous activities of neurons in the cerebral cortex beneath the skull [2]. An ERP-based BCI speller, also referred to as the P300 speller in literatures [3,4], relies on characteristics of the ERP components elicited by the attended stimuli in a spelling task [5].
Multi-sensor recordings are normally required to capture the widely distributed ERP features over the cortical surface, as well as to compensate for the poor signal quality from a single sensor. More sensors typically yield better classification accuracies, while it is at the cost of increased complexity and reduced usability of the speller, limiting the popularization of mobile/wearable BCI devices [6,7,8] for the end use. Reduction and centralization of sensors may bring much more comfort to the user, decreases installation time duration, reduce the cost and improve portability and convenience of a BCI [9]. To reduce the number of sensors, recently many computational sensor-selection methods, from the perspectives of the dimensionality reduction and the spatial filtering, have been developed, such as those based on the swarm algorithm [10,11], independent component analysis [12], automatic relevance determination [13], and other strategies [14,15], etc. However, aside from an additional computational burden, these sensor-selection methods are of no help to centralization of sensors, because they only select but do not change the underlying sources. On the contrary, the localization of brain activities could be a solution to both reduction and centralization of sensors.
The basic concept of localization is to reinforce local rather than global brain activities, and make useful information concentrated on centralized sensors. Some BCI modalities, such as motor imagery BCI [12] and steady-state visual evoked potential (SSVEP)-based BCI [16], are born with alocalization feature. Motor imagery BCIs are based on contralateral ERD/ERS rhythms that are mainly concentrated around the central area over the sensorimotor cortex, and SSVEP BCIs are based on homogeneous SSVEP rhythms that are originated from the visual cortex and spread over the whole cortical surface. Different from these BCI modalities, ERP-based BCIs may suffer from a dispersed distribution of the underlying ERP activities, mainly involving those associated with visual N1, P2, N2, and P3 components. Visual P2 and N2 preponderating over the frontal-central area, P3 preponderating over the parietal area, and N1 preponderating over the occipito-temporal area, have been thought to make major contributions to the working of an ERP speller [5,17,18]. Such a dispersed distribution poses a huge challenge for the localization of an ERP-based BCI, because discarding any of these ERP components may remarkably deteriorate the classification performance, which now is still too low even if considering all of these components. Because characteristics of ERPs are closely related to the perceptual and cognitive meanings of the visual stimulus processed by the brain, one possible way to handle the challenge is to optimize the visual stimulus to obtain more localized activities.
The traditional ERP speller uses a type of character-flashing stimulus, where luminous intensification of characters is used for accentuation [4]. Recently, new stimulus types, such as the face-flashing stimulus [19,20,21], the object rotation stimulus (the FLIP paradigm) [22], and the object motion stimulus (motion-onset paradigm) [23], have been shown to have advantages over the traditional character-flashing type. The face-flashing paradigm, which superimposes face images onto characters for accentuation, was shown to benefit from additional ERPs related to face recognition, such as N170 and N400, and thus significantly improved the classification performance. The FLIP paradigm was revealed to be able to suppress the refractory effect of the P3 component in an ERP speller, and the motion-onset paradigm was revealed to elicit the motion-onset N200 component, and have a merit of low contrast and luminance tolerance. In our previous work, we had investigated a novel visual graphic stimulus, and demonstrated its effectiveness for improving overall classification performance of an ERP speller [18]. Different from the stimulus types examined in literatures [19,20,21,22,23], the visual graphic stimulus has significantly increased complexity and unpredictability, which profoundly affects the perceptual processing and leads to significant enhancement of ERP responses [24,25]. Although many stimulus types have been proposed, hardly any of them has been evaluated referring to the localization performance.
The novelty and main contributions of the present study is to reveal the specific localization effect of brain activities elicited by the visual graphic stimulus, and verify its effectiveness and significance for the centralization and reduction of sensors, for an ERP speller. For this purpose, three sensor settings, i.e., the FS, NS, and LS settings, were tested, and the classification performance of an ERP speller with the visual graphic stimulus, were reevaluated, and compared to those with the traditional character-flashing stimulus. The FS condition uses a full set of all sensors and provides the baseline performance, whereas for the NS condition, ten sensors, Fz, Cz, C3, C4, Pz, Oz, O1, O2, T5, T6, were selected in accordance with relevant state-of-the-art studies [13,15,18], and for the LS condition, only five posterior sensors, Pz, O1, O2, T5, T6, were selected, according to our findings about ERP characteristics under both graphic and character-flashing paradigms. The sensor placement adopted in the present study is illustrated in Figure 1.
A description of the traditional character-flashing paradigm could be found in [4]. The visual graphic stimulus-based paradigm modifies the traditional paradigm by changing the stimulus type from the character-flashing type to a varied graphic pattern flashing type, as shown in Figure 2. For each row/column flashing, the characters involved are accentuated by being superimposed with 6 highlighted geometric patterns, which are selected randomly and uniquely from a set including eleven patterns shown in Figure 2c. We adopt a dynamic presentation scheme to ensure the unpredictability of the stimulus: 1) All patterns in a row/column flashing should be selected randomly and uniquely from the set; 2) patterns for two successive flashings of a character should be distinct. In this way, the user would be uncertain about the morphology of the target stimulus in the next flashing, as well as the morphologies of its non-target surrounding stimuli. More details about the varied graphic pattern flashing paradigm could be found in [18].
A subtrial is defined as the twelve row and column flashings of the matrix, and a trial is defined as several consecutive subtrials required for spelling a character. In our experiment, a trial contained 5 subtrials. Stimulus onset asynchrony (SOA), defined as the time interval from the onset of one flashing to the onset of the next flashing, was set as 160 ms with an accentuation period of 80 ms.
Sixteen healthy participants (12 males and 4 females), all right-handed, with normal or corrected to normal vision, took part in the experiment. All participants were naïve to BCIs. Ethical approval and informed consents were obtained in compliance with the Declaration of Helsinki.
Participants were instructed to copy-spell 72 characters without feedback under both the graphic stimulus-based paradigm (GP) and traditional character-flashing paradigm (CP). Target characters were selected randomly from 36 characters in the character matrix, with each character selected twice. These target characters were divided into 6 groups, and each group contained 12 characters. In each experimental run, a group of characters was selected as targets. Therefore, there were totally 12 runs, with 6 runs under the GP, and the other 6 runs under CP. For counterbalancing, the runs were carried out alternately, and half of the participants began with GP, while the others began with CP.
A run contained 12 trials. For each trial, participants were instructed to attend to the flashings of one target character. When a trial began, they had 3 seconds to focus on the target item in the matrix before the flashings occurred. Then, they should mentally count the number of flashings of the target character. At the end of the trial, there was a 2-second blank, during which they should report the number they counted. Then, they would continue with the next trial until the end of the run. When a run finished, there was a 2-minute break before the next run began. To reduce the ocular artefacts, participants were told to try not to blink during flashings.
Finally, we got 144 trials for each participant, with 72 trials for each paradigm.
Brain signals were acquired with a Nuamps 40 amplifier (Neuroscan Inc.) at a sampling rate of 250 Hz, with linked-mastoid reference and a forehead ground. Thirty-two Ag/AgCl sensors, including 30 recording sensors and 2 reference sensors, were placed according to the international 10–20 EEG systems, as shown in Figure 1. The sensor impedance was kept below 5 kilohm. Data from all sensors were recorded in the experiment. Data storage and speller implementation are achieved by BCI2000 [26].
For classification, the recorded brain signals were filtered successively using a causal 3-order Butterworth high-pass filter with a cutoff of 0.5 Hz, and a causal 6-order Butterworth low-pass filter with a cutoff of 5.5 Hz. Whereas for ERP analysis, non-causal zero-phased filters were used instead, with the cutoff of the low-pass filter broadened to 40 Hz. To overcome possible ERP distortion arising from epoch overlapping due to a short SOA, only epochs with target-to-target intervals greater than 5*SOA were used for examining ERP responses.
The epoch length was 800 ms, starting from 200 ms preceding stimulus onset. For classification, data epochs were down sampled to a rate of 25 Hz, and then the resultant epochs from the selected sensors were concatenated to form feature vectors. A stepwise linear discriminant analysis (SWLDA) classifier [27] was adopted.
Grand-average difference ERP responses between both spellers were compared, obtained by subtracting non-target responses from target responses averaged across all participants.
Based on the comparison on ERP responses, a localized sensor set was determined. Then, classification accuracies and information transfer rates (ITR) [28] between GP and CP, were compared respectively, through three-way 2 × 3 × 5 repeated measures ANOVA (PARADIGM [GP, CP] × SENSORSETTING [FS, NS, LS] × TRIAL LENGTH [1,2,3,4,5]). Trial length was defined as the number of subtrials used in a trial. For a trial length level less than 5, a corresponding number of subtrials in the front of each trial were always used, without screening, for the classification, e.g., for trial length 1, only the first subtrial in each trial was used for the classification. For the FS condition, all the 30 recording sensors were selected. ANOVA were carried out with the Statistic Package for Social Science (SPSS).
A 4-fold cross validation was adopted to obtain the estimation of classification accuracies. For each participant, the obtained 72-character dataset were divided sequentially into four groups, with 18 characters in each group. Every time, one of the four groups was selected for training the classifier, while the remaining were used for evaluating the character-wise accuracy. Then, accuracies from the 4 folds were averaged to obtain a final result. ITRs were calculated using [28]
where N indicates the number of items in the speller matrix, P indicates the character-wise classification accuracy, T indicates the time span of a trial (unit: Min (minute)), and ITR means the number of bits being sent in a trial (unit: Bits/min).
Grand-average difference ERP responses for both paradigms are shown in Figure 3. Several differences could be found between GP and CP. First, at occipito-temporal sensor sites, e.g., O1 and O2 sites, an enhanced negative N1 component with a latency of about 200 ms could be observed for GP. Second, at T5, T6, O1, and O2 sites, a pronounced positive peak with a latency around 320 ms is observed for GP, which seems absent for CP. Third, at frontal-central sites, typically Fz and Cz, a more negative N2 component with a latency around 300 ms, is elicited for GP than that for CP. While for the P2 component at frontal-central sites, with a latency around 230 ms, and for the P3 component at parietal and occipito-temporal sites, with a latency around 400 ms, there seem no much differences between both spellers.
From the ERP responses, it can be found that GP elicited enhanced ERP features on occipito-temporal sensor sites. These differences are mainly concentrated on several minority posterior sites, indicating a localization effect. The localization effect brought out by GP could be seen in more detail in individual scalp distributions of amplitudes of the posterior negative (named N1) and positive (named P2b) peaks in difference responses between GP and CP, as shown in Figures 4 and 5, respectively. It should be noted that, in Figure 4, amplitudes of inverse N1 are shown such that a greater value in the map indicates more pronounced N1 amplitudes. For evaluating the amplitudes of these components, these negative and positive peaks were first found in a range of 100 to 400 ms referring to Oz, and then mean amplitudes within 40 ms around the peaks were evaluated. It can be seen obviously that, for most participants, enhancement of ERP activities by GP is primarily localized over the posterior region. Therefore, four occipito-temporal sites, T5, T6, O1, and O2, were selected for the localized sensor setting. On the other hand, a parietal site Pz was also included in the localized sensor setting, in consideration of the contribution of P3 component to classification, which preponderates at the parietal area. Finally, five posterior sensors, at T5, T6, O1, O2, and Pz sites, respectively, were selected for the localized sensor setting.
Mean accuracies and mean information transfer rates of both spellers, obtained from the 4-fold cross validation, are shown in Figure 6a and b, respectively.
Results from ANOVA on classification accuracies revealed a significant PARADIGM × SENSORSETTING × TRIALLENGTH interaction (F(8,120) = 2.68, p = 0.010). GP significantly outperforms CP irrespective of sensor setting and trial length (5 trial length levels (1~5) within 3 sensor settings (FS, NS, LS): F(1, 15) = 34.85, 47.23, 18.50, 13.71, 9.23; 41.68, 32.99, 23.07, 15.75, 9.95; 25.20, 39.56, 31.51, 32.33, 19.59 respectively. All p-values < 0.01). PARADIGM × TRIALLENGTH interactions are also revealed to be significant for each sensor settings (F(4, 60) = 12.60, 10.30, 2.62; p = 0.000, 0.000, 0.043 respectively). At short trial lengths (1~2), the differences between both paradigms become more obvious than those at long trial lengths (3~5).
Results from ANOVA on information transfer rates revealed a significant PARADIGM × TRIAL LENGTH interaction (F(4, 60) = 21.61, p = 0.000). Similar to the results of ANOVA on accuracies, at each trial length level, GP outperformed CP (F(1, 15) = 34.77, 53.25, 33.75, 29.15, 18.13 respectively; all p-values < 0.01), and the differences become more obvious for short trial lengths than for long trial lengths. The PARADIGM × SENSOR × TRIALLENGTH interaction does not reach the significant level (F(8,120) = 1.70, p = 0. 104).
From Figure 6, it can be seen that even if with the localized sensor setting, GP still achieves better results than CP with any of the three sensor settings. To verify this argument, we further performed two-tailed paired-samples T-tests between the LS-GP condition and each of the FS-CP, NS-CP, and LS-CP conditions. Results from T-tests on accuracies show that, at short trial lengths (1~2), the LS-GP outperforms either of the FS-CP and NS-CP (p < 0.05); while when trial length increases (3~4), differences between the LS-GP and the NS-CP become less significant (p < 0.1), and differences between the LS-GP and the FS-CP do not reach the significant level (p > 0.1); at trial length 5, there are no significant differences between the LS-GP and either of the NS-CP and the FS-CP (p = 0.271 and 0.332 respectively); however, at all trial lengths (1~5), the LS-GP is significantly better than the LS-CP (all p-values = 0.000). T-tests on information transfer rates give the similar results to T-tests on accuracies. Therefore, it can be concluded that, at short trial lengths (1~2), GP with the localized sensor set outperforms CP with any of FS, NS, and LS, while the differences become less significant as the trial length increases.
Sensor reduction is an important issue for lowering complexity of an ERP speller. However, a tradeoff between the accuracy and the sensor set size should always be considered when pursuing the minimization of sensors. Because the amount of information available for classification may decrease with reduced sensors, classification accuracy would typically degenerate as well. One way for sensor reduction is to exploit signal processing methods to find the optimal sensor set. These methods, also commonly called the sensor/channel selection methods [9,10,11,12,13,14,15], depend on the established characteristics of the ERP signals within an experimental paradigm. Another way for sensor reduction is through localization of brain activities, which is the main purpose of the present study. Different from the signal processing methods, which do not change the physical properties of the underlying ERP components, localization methods could directly improve the quality of the ERP components, and make the useful information more concentrated on minority sensors, so it would be more powerful for sensor reduction.
We evaluated the localization performance under a novel visual graphic stimulus paradigm. Performance of GP was compared to that of CP under three sensor set conditions, i.e., the full 30-sensor set, the normal 10-sensor set, and the localized 5-sensor set. Results showed that, GP achieves significantly greater classification accuracies and information transfer rates than CP, irrespective of sensor settings. Furthermore, even with the localized sensor set, GP still shows its advantage over CP using any of the three sensor settings, especially at short trial lengths. For instance, at trial length 2, the average accuracy of GP under LS reaches up to 80.24%, significantly greater than those of CP under FS, NS, and LS, i.e., 75.38, 75.00 and 67.64% respectively (all p-values < 0.05). To increase the output rate of an ERP speller, researchers have always been finding ways, from either signal processing or experimental paradigm directions, to increase the classification accuracy at short trial lengths. In this sense, GP is also especially valuable for BCI studies with short trial lengths. GP also obtained a maximum average ITR of 69.76 bit/min under LS, significantly higher than those of CP under FS, NS, and LS, i.e., 58.36, 56.47 and 49.38 bit/min respectively (all p-values < 0.01). In conclusion, for GP, the sensor-set size could be reduced half, from the normal frontal-central and posterior sites to only the localized posterior sites, whereas the performance remains better than CP. Therefore, we think GP is remarkably effective for sensor reduction and localization of brain activities of an ERP BCI. Besides the localization performance, the trial length effect needs also be discussed. As shown in Figure 6, a clear trial length effect can be observed for both accuracies and ITRs, under each of the six conditions, where mean accuracies show a positive correlation trend, and mean ITRs show a negative correlation trend, with respect to the trial length. That is because, a greater trial length means more stimulus repetitions, and thus brings about increased signal-to-noise ratio for the obtained ERP samples, which naturally leads to increased accuracies. This may explain the trial length effect on accuracies shown in Figure 6a. However, an increase of the trial length may also prolong the time span of a trial, and thus, according to the definition of the ITR, may cause a reduction of ITRs, as observed from our results given in Figure 6b.
According to our results, five posterior sensors, including one parietal and four occipito-temporal sensors, were enough for GP to obtain a comparable or even higher performance than CP. Because these sensors are concentrated over the local posterior area, the proposed method may facilitate the design and setup of an ERP speller device, and so it would be of great value for the practical use. These results could be supported by the underlying ERP responses at these sensors. As shown in Figure 3, for GP, at Pz site, differences between target and non-target responses mainly come from P2, N2, and P3 components, whose latencies are 230,300 and 400 ms respectively, whereas for O1, O2, T5, and T6 sites, N1, P3, and a pronounced positive component with a latency of about 320ms, provide discriminative information for classification. Performance enhancement of GP should probably come from the enhancement of N1, and the elicitation of a pronounced positive component (denoted as P2b here) earlier than P3 at occipito-temporal sensor sites.
In this paper, we examined the localization issue for an ERP speller, which may be especially useful for reduction and centralization of sensors, and thus for popularization of wearable/mobile BCIs for the end use. A novel visual graphic stimulus was used to yield localized posterior activities, which, from our experimental results, was demonstrated to achieve comparable or even better classification performance than those obtained by the traditional character-flashing stimulus using global activities, and was also revealed to be able to reduce the number of sensors required by half. Participants also reported that they felt more concentrated and less fatigue with the graphic stimulus than the character-flashing stimulus. Future work may involve incorporating dimensionality reduction approaches, such as the Grassmannian subspace method [29,30], to further reduce the computational load and boost the classification performance, as well as revealing the cognitive origins of the localized posterior activities under visual graphic stimulus, by solving an EEG inverse problem [2,31].
This work was supported in part by the National Natural Science Foundation of China (61772508, U1713213, 61906183, 61671105), Shenzhen Technology Project (JCYJ20170413152535587, JCYJ20180507182610734), Key Research and Development Program of Guangdong Province (2019B090915001), CAS Key Technology Talent Program, Shenzhen Engineering Laboratory for 3D Content Generating Technologies (NO. [2017]476).
All authors declare that they have no conflict of interest in relation to this scientific work.
[1] |
Freire-González J, Decker C, Hall J (2017) The economic impacts of droughts: A framework for analysis. Ecol Econ 132: 196-204. doi: 10.1016/j.ecolecon.2016.11.005
![]() |
[2] |
Logan I, van den Bergh J (2013) Methods to assess costs of drought damages and policies for drought mitigation and adaptation: Review and recommendations. Water Resour Manag 27: 1707-1720. doi: 10.1007/s11269-012-0119-9
![]() |
[3] | FAO (Food and Agricultural Organization of the United Nations) (2013) UN lays foundations for more drought resilient societies. Meeting urges disaster risk reduction instead of crisis management. Available from: https://www.fao.org/news/story/en/item/172030/icode/ (Accessed 30 March 2021). |
[4] | Ferrari E, McDonald S, Osman R (2016) Water scarcity and irrigation efficiency in Egypt. Water Econ Pol 2: 165009. |
[5] | Gunst L, Castro Rego F, et al. (2015) Impact of meteorological drought on crop yield on pan-European scale, 1979-2009. In: Andreu J, Solera A, Paredes-Arquiola J, et al. (Eds.), Drought: Research and Science-Policy Interfacing, CRC Press, Taylor & Francis Group, London. |
[6] | Mendelsohn R, Dinar A (2009) Climate change and agriculture: An economic analysis of global impacts, adaptation and distributional effects. Edward Elgar, Cheltenham, UK. |
[7] |
Musolino A, Massarutto A, de Carli A (2018) Does drought always cause economic losses in agriculture? An empirical investigation on the distributive effects of drought events in some areas of Southern Europe. Sci Total Environ 633: 1560-1570. doi: 10.1016/j.scitotenv.2018.02.308
![]() |
[8] |
Salami H, Shahnooshi N, Thomson KJ (2008) The economic impacts of drought on the economy of Iran: An integration of linear programming and macroeconomic modelling approaches. Ecol Econ 68: 1032-1039. doi: 10.1016/j.ecolecon.2008.12.003
![]() |
[9] | Yu C, Huang X, Chen H, et al. (2018) Assessing the impacts of extreme agricultural droughts in China under climate and socioeconomic changes. Earth's Future 6: 689-703. |
[10] |
Erfurt M, Glaser R, Blauhut V (2019) Changing impacts and societal responses to drought in southwestern Germany since 1800. Reg Environ Change 19: 2311-2323. doi: 10.1007/s10113-019-01522-7
![]() |
[11] | van Niekerk D, Tempelhoff J, Faling W, et al. (2009) The effects of climate change in two flood laden and drought stricken areas in South Africa: Responses to climate change—past, present and future. Report to the National Disaster Management Centre. African Centre for Disaster Studies, Potchefstroom. |
[12] |
Bahta YT (2020) Smallholder livestock farmers coping and adaptation strategies to agricultural drought. AIMS Agric Food 5: 964-982. doi: 10.3934/agrfood.2020.4.964
![]() |
[13] |
Ahmadalipour A, Moradkhani H, Castelletti A, et al. (2019) Future drought risk in Africa: Integrating vulnerability, climate change, and population growth. Sci Total Environ 662: 672-686. doi: 10.1016/j.scitotenv.2019.01.278
![]() |
[14] |
Perez C, Jones EM, Kristjanson P, et al. (2015) How resilient are farming households and communities to a changing climate in Africa? A gender-based perspective. Global Environ Chang 34: 95-107. doi: 10.1016/j.gloenvcha.2015.06.003
![]() |
[15] | Parker H, Oates N, Mason N, et al. (2016) Gender, agriculture and water insecurity. Available from: https://www.odi.org/sites/odi.org.uk/files/resource-documents/10356.pdf (Accessed 10 April 2020). |
[16] | IPCC (Intergovernmental Panel on Climate Change) (2018) Special report: Global warming of 1.5 ℃. Available from: https://www.ipcc.ch/sr15/ (Accessed 2 July 2020). |
[17] | Southwick SM, Bonanno GA, Masten AS, et al. (2014) Resilience definitions, theory, and challenges: Interdisciplinary perspectives. Eur J Psychotraumatol 5: PMC4185134. |
[18] |
Mutero J, Munapo E, Seaketso P (2016) Operational challenges faced by smallholder farmers: A case of Ethekwini Metropolitan of South Africa. Environ Eco 7: 40-52. doi: 10.21511/ee.07(2).2016.4
![]() |
[19] | Habtezion S (2016) Gender and climate change: Overview of linkages between gender and climate change. United Nations Development Programme. Available from: https://www.undp.org/content/dam/undp/library/gender/Gender%20and%20Environment/UNDP%20Linkages%20Gender%20and%20CC%20Policy%20Brief%201-WEB.pdf (Accessed 10 May 2020). |
[20] | Von Maltitz L (2020) The resilience of female smallholder livestock farmers to agricultural drought in the Northern Cape, South Africa. MSc. Dissertation, University of the Free State, South Africa. |
[21] | DAFF (Department of Agriculture, Forestry, and Fisheries) (2012) A framework for the development of smallholder farmers through cooperatives development. Pretoria, South Africa. |
[22] | Matlou RC, Bahta YT (2019) Factors influencing the resilience of smallholder livestock farmers to agricultural drought in South Africa: Implication for adaptive capabilities. J Disast Risk Stud 11: art805. |
[23] | Stats SA (Statistics South Africa) (2019) Labour Force Survey. Pretoria: Statistics South Africa. |
[24] | Stats SA (Statistics South Africa) (2016) Community Survey 2016: Provincial Profile Northern Cape, Report No. 03-01-14. Pretoria: Statistics South Africa. |
[25] | Opondo M, Abdi U, Nangiro P (2016) Assessing gender in resilience programming: Uganda. Resilience Intel, Part of the BRACED Knowledge Manager Series. Available from: https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/10215.pdf (Accessed 10 April 2020). |
[26] | Theis BYS, Martinez E (2018) How gender shapes responses to climate change : New tools for measuring rural women's empowerment. IFPRI Blog Issue Post. Available from: http://www.ifpri.org/blog/how-gender-shapes-responses-climate-change-new-tools-measuring-rural-womens-empowerment (Accessed 25 April 2020). |
[27] | Le Masson V (2016) Gender and resilience: From theory to practice. In BRACED Knowledge Manager. Available from: https://www.odi.org/publications/9967-gender-and-resilience-theory-practice (Accessed on 21 April 2020). |
[28] | DRDLR (Department of Rural Development and Land Reform) (2017) Land Audit Report. Phase II: Private Land Ownership by Race, Gender and Nationality. Version 2. Pretoria: South Africa. |
[29] | CARE (2016) Enhancing resilience through gender equality. Gender equality and women's voice in asia-pacific resilience programming. Research report. Available from: https://careclimatechange.org/wp-content/uploads/2016/08/enhancing-resilience.pdf (Accessed 14 June 2020). |
[30] | Ugwu P (2019) Women in agriculture: Challenges facing women in African farming. Research report: Postgraduate school of agricultural and food economics, Catholic University of the Sacred Heart, Italy. |
[31] | Le Masson V, Norton A, Emily W (2012) Gender and resilience. In BRACED Knowledge Manager. Available from: https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/9890.pdf (Accessed on 25 September 2020). |
[32] |
Khapung S (2016) Transnational feminism and women's activism: Building resilience to climate change impact through women's empowerment in climate-smart agriculture. Asian J Women Stud 22: 497-506. doi: 10.1080/12259276.2016.1242946
![]() |
[33] |
Ravera F, Iniesta-Arandia I, Martin-Lopez B, et al. (2016) Gender perspectives in resilience, vulnerability and adaptation to global environmental change. Ambio 45: 235-247. doi: 10.1007/s13280-016-0842-1
![]() |
[34] |
Price M, Galie A, Marshall J, et al. (2018) Elucidating linkages between women's empowerment in livestock and nutrition: a qualitative study. Dev in Practice 28: 510-524. doi: 10.1080/09614524.2018.1451491
![]() |
[35] | Quandt A (2018) Variability in perceptions of household livelihood resilience and drought at the intersection of gender and ethnicity. Climatic Change 152: 1-16. |
[36] | Lambrecht I, Schuster M, Asare S, et al. (2017) Changing gender roles in agriculture? Evidence from 20 years of data in Ghana. Discussion Paper 01623, International Food Policy Research Institute. Available from: http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/131105/filename/131316.pdf (Accessed on 13 May 2020). |
[37] | UNFCCC (United Nations Framework Convention on Climate Change) (2019) Building resilient livelihood in Sudan. Available from: https://unfccc.int/climate-action/momentum-for-change/women-for-results/building-resielient-livelihoods-i-sudan (Accessed 15 April 2020). |
[38] |
Wouterse F (2019) The role of empowerment in agricultural production: evidence from rural households in Niger. J Dev Stud 55: 565-580. doi: 10.1080/00220388.2017.1408797
![]() |
[39] |
Luthar SS Cicchetti D (2000) The construct of resilience: Implications for interventions and social policies. Dev Psychopathol 12: 857-885. doi: 10.1017/S0954579400004156
![]() |
[40] |
Keil A, Zeller M, Wida A, et al. (2007) What determines farmers' resilience towards ENSO-related drought? An empirical assessment in Central Sulawesi, Indonesia. Climatic Change 86: 291-307. doi: 10.1007/s10584-007-9326-4
![]() |
[41] |
Lokosang L, Ramroop S, Zewotir T (2014) Indexing household resilience to food insecurity shocks: The case of South Sudan. Agrekon 53: 137-159. doi: 10.1080/03031853.2014.915486
![]() |
[42] | Lieber E (2009) Mixing qualitative and quantitative methods: Insights into design and analysis issues.J Ethnographic Qual Res 3(4): 218-227. |
[43] | Mason KO (2005) Measuring women's empowerment: Learning from cross-national research. In: Narayan D, (ed.), Measuring empowerment: Cross-disciplinary perspectives, Washington, DC: World Bank, 89-102. |
[44] | FBDM (Frances Baard Municipal District Municipality) (2019) Frances Baard Municipal District in the Northern Cape. Kimberly, South Africa. Available from: https://municipalities.co.za/map/134/frances-baard-district-municipality (Accessed 31 March 2021). |
[45] | DAFF (Department of Agriculture, Forestry and Fisheries) (2018) Drought status in the agriculture sector. Portfolio Committee on Water and Sanitation. Pretoria, South Africa. |
[46] | Malapit H, Kovarik C, Sproule K, et al. (2015). Instructional guide on the abbreviated women's empowerment in Agriculture Index (A-WEAI). World Dev 52: 71-91. |
[47] | Cochran WG (1977) Sampling techniques, 3de Edition. New York: John Wiley and Sons. |
[48] | Bartlett JE, Kotrick JW, Higgins CC (2001) Organizational research : Determining appropriate sample size in survey research. Inf Technol Learn Perform J 19: 43-50. |
[49] | Northern Cape Department of Agriculture (2019) List of smallholder women livestock farmers. Kimberly, South Africa. |
[50] | Alkire S, Meinzen-Dick R, Peterman A, et al. (2013) The women's empowerment in Agriculture Index. World Dev 52: 71-91. |
[51] | Malhotra A, Schuler SR (2005) Women's empowerment as a variable in international development. In: Narayan D, (ed.), Measuring empowerment: Cross-disciplinary perspectives, Washington, DC: World Bank, 71-88. |
[52] | SNV Netherlands (Stichting Nederlandse Vrijwilligers) (2017) Empowering women in agribusiness through social and behavioral change. Available from: http://www.snv.org/public/cms/sites/default/files/explore/download/empowering_women_in_agribusiness_through_social_behaviour_change_kenya_vietnam.pdf (Accessed on 21 October 2020). |
[53] | Petesch P, Smulovitz C, Walton M (2005) Evaluating empowerment: A framework with cases from Latin America. In: Narayan D, (ed.), Measuring empowerment: Cross-disciplinary perspectives, Washington, DC: World Bank, 39-67. |
[54] | Anderson H (2014) Collaborative-dialogue based research as everyday practice: Questioning our myths. In: Simon G, Chard A, (ed.), Systemic Inquiry Innovations in Reflexive Practice Research, Farnhill, UK, 60-73. |
[55] |
Metelerkamp L, Drimie S, Biggs R (2019) We're ready, the system's not—youth perspectives on agricultural careers in South Africa. Agrekon 58: 154-179. doi: 10.1080/03031853.2018.1564680
![]() |
[56] |
Achandi EL, Kidane A, Hepelwa A, et al. (2019) Women's empowerment: The case of smallholder rice farmers in Kilombero District, Tanzania. Agrekon 58: 324-339. doi: 10.1080/03031853.2019.1587484
![]() |
[57] | Nabikolo D, Bashaasha B, Mangheni MN, et al. (2012) Determinants of climate change adaptation among male and female-headed farm households in Eastern Uganda. Afr Crop Sci J 20: 203-212. |
[58] | Leder S (2015) Linking women's empowerment and their resilience. Literature review. International Water Management Institute (IWMI), Nepal, South Asia. |
[59] | Isaga N (2018) Access to bank credit by smallholder farmers in Tanzania: A case study. Afrika Focus 31: 241-256. |
[60] |
Johnson NL, Kovarik C, Meinzen-Dick R, et al. (2016) Gender, assets, and agricultural development: Lessons from eight projects. World Dev 83: 295-311. doi: 10.1016/j.worlddev.2016.01.009
![]() |
[61] |
Anderson CL, Reynolds TW, Gugerty MK (2017) Husband and wife perspectives on farm household decision-making authority and evidence on Intra-household Accord in Rural Tanzania. World Dev 90: 169-183. doi: 10.1016/j.worlddev.2016.09.005
![]() |
[62] |
Huyer S (2016) Closing the gender gap in agriculture. Gender Techn Dev 20: 105-116. doi: 10.1177/0971852416643872
![]() |
[63] |
Fisher M, Carr ER (2015) The influence of gendered roles and responsibilities on the adoption of technologies that mitigate drought risk: The case of drought-tolerant maize seed in eastern Uganda. Global Environ Chang 35: 82-92. doi: 10.1016/j.gloenvcha.2015.08.009
![]() |
[64] | Shean A, Alnouri S (2014) Rethinking resilience: Prioritizing gender integration to enhance household and community resilience to food insecurity in the Sahel. Available from: https://www.mercycorps.org/research-resources/rethinking-resilience (Accessed on 12 April 2020). |
[65] | Tambo JA (2016) Adaptation and resilience to climate change and variability in north-east Ghana. Int J Disast Risk Re 17: 85-94. |
[66] | Hassen A (2008) Vulnerability to drought risk and famine: Local responses and external interventions among the afar of Ethiopia, a study on the Aghini pastoral community. Ph.D. thesis, University of Bayreuth, Germany. |
[67] | Bahta YT, Jordaan A, Muyambo F (2016) Communal farmers' perception of drought in South Africa: Policy implication for drought risk reduction. Int J Disast Risk Re 20: 39-50. |
[68] | Iglesias A, Moneo M, Quiroga S (2007) Methods for evaluating social vulnerability to drought, Opt Méditerr Ser B 58: 129-133. |
[69] |
Galiè A, Teufel N, Korir L, et al. (2018) The women's empowerment in Livestock Index. Soc Indic Res 142: 799-825. doi: 10.1007/s11205-018-1934-z
![]() |
[70] |
Akter S, Rutsaert P, Luis J, et al. (2017) Women's empowerment and gender equity in agriculture: A different perspective from Southeast Asia. Food Policy 69: 270-279. doi: 10.1016/j.foodpol.2017.05.003
![]() |
[71] |
van den Bold M, Dillon A, Olney D, et al. (2015) Can integrated agriculture-nutrition programs change gender norms on land and asset ownership? Evidence from Burkina Faso. J Dev Stud 51: 1155-1174. doi: 10.1080/00220388.2015.1036036
![]() |
[72] |
Opiyo FEO, Wasonga OV, Nyangito MM (2014) Measuring household vulnerability to climate-induced stresses in pastoral rangelands of Kenya: Implications for resilience programming. Pastoralism 4: 1-15. doi: 10.1186/2041-7136-4-1
![]() |
[73] |
Mustaffa CS, Asyiek F (2015) Conceptualizing framework for women empowerment in indonesia: Integrating the role of media, interpersonal communication, cosmopolite, extension agent and culture as predictors variables. Asian Soc Sci 11: 225-239. doi: 10.5539/ass.v11n16p225
![]() |
[74] |
Bhattarai B, Beilin R, Ford R (2015) Gender, agrobiodiversity, and climate change: A study of adaptation practices in the Nepal Himalayas. World Dev 70: 122-132. doi: 10.1016/j.worlddev.2015.01.003
![]() |
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