| Literature DB >> 32042980 |
Abdourhimou Amadou Issoufou1, Idrissa Soumana2, Garba Maman2, Souleymane Konate1,3, Ali Mahamane4.
Abstract
Climate change increases the vulnerability of agrosystems to soil degradation and reduces the effectiveness of traditional soil restoration options. The implementation of some practices need to be readjusted due to steadily increasing temperature and lowering precipitation. For farmers, the best practice found, should have the potential to achieve maximum sustainable levels of soil productivity in the context of climate change. A study was conducted in South-West Niger to investigate the use of the suitable practice, through (i) a meta-analysis of case studies, (ii) using field survey and (iii) by using AquaCrop model. Results showed that the effects of the association zaï + mulch on crop yield was up to 2 times higher than control plots depending on climate projections scenario RCP 8.5 under which carbon dioxide (CO2) concentrations are projected to reach 936 ppm by 2100. The practice appeared to be an interesting option for enhancing crop productivity in a context of climate change. Concerning its ability, it offers the best prospects to reverse soil degradation in the study area. In addition, the simulation showed that this strategy was suitable for timely sowing and therefore confirmed scholars and farmers views. Furthermore, this practice is relatively more effective compared to the others practices. These results show that association zaï + mulch could be considered as the best practice that can participate to a successful adaptation to reduce risk from climate change at the same time by reducing the vulnerability of farmers in Southwest of Niger for now and even for the future.Entities:
Keywords: Agricultural science; AquaCrop model; Climate change adaptation; Environmental science; Scholars and local knowledge; Soil restoration strategies; Southwest Niger
Year: 2020 PMID: 32042980 PMCID: PMC7002823 DOI: 10.1016/j.heliyon.2020.e03265
Source DB: PubMed Journal: Heliyon ISSN: 2405-8440
Adoption studies of traditional soil restoration strategies and binary code of the parameters of each practice.
| Param | runoff | biodiversity | Soil nutrients cycling | water Infiltration | Relative cost | Labour | Mean yield | Adapation to climate change |
|---|---|---|---|---|---|---|---|---|
| Strategies | ||||||||
| Mulch | 0 [ | 1 [ | 1 [ | 1 [ | 0 [ | 1 [ | 1 [ | 1 [ |
| Half moon | 1 [ | 0 [ | 0 [ | 0 [ | 0 [ | 0 [ | 0 [ | 0 [ |
| Stones line | 1 [ | 0 [ | 0 [ | 1 [ | 0 [ | 0 [ | 0 [ | 0 [ |
| Zaï | 1 [ | 1 [ | 1 [ | 1 [ | 0 [ | 0 [ | 0 [ | 0 [ |
| Zaï + mulch | 1 [ | 1 [ | 1 [ | 1 [ | 0 [ | 0 [ | 1 [ | 1 [ |
| Fallow | 0 [ | 0 [ | 1 [ | 1 [ | 1 [ | 1 [ | 1 [ | 0 [ |
| Parcage system (coralling) | 0 [ | 1 [ | 1 [ | 0 [ | 1 [ | 0 [ | 1 [ | 1 [ |
| Stones + Zaï | 1 [ | 0 [ | 1 [ | 1 [ | 0 [ | 0 [ | 1 [ | 1 [ |
| Crop rotation | 0 [ | 0 [ | 1 [ | 0 [ | 1 [ | 1 [ | 1 [ | 1 [ |
| Agroforestery | 0 [ | 0 [ | 1 [ | 0 [ | 1 [ | 1 [ | 1 [ | 1 [ |
| zero tillage | 1 [ | 0 [ | 0 [ | 1 [ | 1 [ | 1 [ | 0 [ | 0 [ |
| Minimum tillage | 1 [ | 0 [ | 0 [ | 0 [ | 0 [ | 0 [ | 0 [ | 0 [ |
[ …]: the reference number; 0 and 1 are codes respectively attributed to the lowest and the highest score attributed to the efficiency of a practice on a dependent variable.
Summary of parameters used in the simulation.
| Parameters | Definition | Way of Determination |
|---|---|---|
| Sowing Date | 08 June | F |
| Crop type | millet | F |
| Variety sown | HPK | F |
| Sowing density | 10000 plants/ha | F |
| Soil evaporation | mm/day | F |
| Crop evapotranspiration | mm/day | F |
| Relative humidity | % | F |
| Source of weather data | Nasa | F |
| Soil type | Clay Loam | F |
| Maximum rooting depth | 80 cm | F |
| Date of last rainfall entry | 09 Sep | F |
| Harvest index | % | E |
| Initial canopy cover | % | E |
F= Field observed/measured data; E = calibrated.
Marginal effects after probit of meanyield zaimulch and of adap.climchan zaimulch.
| Variables | Probit | |
|---|---|---|
| 0.25 | 0.10 | |
Note: Explanatory variable is set equal to its median in the sample; N = 12; Log likelihood = -8.116 and 8.092. are for discrete change of dummy variables from 0 to 1.
Figure 1Specific adoption rates of traditional soil restoration practices.
Logistic regression between climate change awareness and the traditional soil restoration choice.
| Variables | coef | Odds Ratio | std.Err | z | P>/|z| |
|---|---|---|---|---|---|
| 0.31 | 19.2 | 21.87 | 2.59 | 0.009 |
Note: N = 49; Log-Likelihood = -15.066.
Figure 2Model validation results in simulating and observed of pearl millet under rainfed treatment of the 2017–2018 season under climate change RCP 8.5. R2, coefficient of determination; RMSE, Root-mean-square error; E, model efficiency; MAE, mean absolute Error.
Figure 3Millet grain yield simulated by AquaCrop under Climate Change Scenario RCP 8.5.