A meta-analysis of intercropping in Africa: impacts on crop yield, farmer income, and integrated pest management effects

Himmelstein, J.; Ares, Adrián; Gallagher, Daniel L.; Myers, J. R.

doi:10.1080/14735903.2016.1242332

Cited by 91 publications

(59 citation statements)

References 32 publications

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“…The current study is aimed at providing a quantitative review on how these management practices could influence the impact of cover crops on weed suppression through an analytical approach: meta-analysis. The adoption of metaanalysis for quantitative review is becoming more popular among agronomy researchers, as this approach provides a summarized quantitative estimate or information from data extracted through a comprehensive survey of previous studies (Egan et al, 2014;Pittelkow et al, 2015;Li et al, 2016;Himmelstein et al, 2017;Osipitan et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Impact of Cover Crop Management on Level of Weed Suppression: A Meta‐Analysis

et al. 2019

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We conducted a previous systematic and meta‐analysis review that showed differences in results from studies that evaluated the effectiveness of cover crops for weed suppression in cropping systems; these differences were largely due to management approaches used in growing the cover crop and main crop. The current meta‐analysis provides a quantitative review on how cover crop and main crop management practices influence the impact of cover crops on weed suppression. The meta‐analysis used observations from 53 studies published from 1990 to 2018. Cover crop biomass was inversely related to the amount of weed biomass (r2 = 0.67) and weed density (r2 = 0.64). In general, the meta‐analysis shows that cover crops provided a range of weed suppression depending on management decisions such as choice of cover crop species, cover crop sowing season (fall or spring), sowing dates within seasons, seeding rate, termination date, delay in main crop planting date after cover crop termination, tillage system under which the cover crop was produced, and integrating the cover crop with other weed control inputs. For example, grass cover crop species provided greater weed suppression than broadleaf species. Fall‐sown cover crops provided greater weed suppression (weighted mean of response ratio [R*] = 0.19) than spring‐sown cover crops (R* = 0.48) by the summer. Weed suppression increased by increasing seeding rate of cover crops from 1× (R* = 0.50) to 2× (R* = 0.27) or 3× (R* = 0.10). In addition, cover crops provided greater weed suppression in reduced tillage systems (R* = 0.19) than no tillage (R* = 0.29). The differential weed suppression provided by these management approaches suggests that a cover crop management approach should be rightly selected for weed suppression benefits.

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Section: Introductionmentioning

confidence: 99%

Impact of Cover Crop Management on Level of Weed Suppression: A Meta‐Analysis

et al. 2019

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“…Intercropping is a method employed for agricultural sustainability in which two or more plant species are simultaneously cultivated together on the same land [1][2][3] Intercropping systems are favored by a better land occupation, a decrease in the use of agrochemicals, better soil conservation, and yield diversification, among other benefits [4][5][6][7]. Management challenges for intercropping include the use of an optimum plant density, cultivation design or plant strip widths, and the best times of seeding for each of the crops to maximize crop productivities [8].…”

Section: Introductionmentioning

confidence: 99%

Intercropping Simulation Using the SWAP Model: Development of a 2×1D Algorithm

et al. 2019

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Intercropping is a common cultivation system in sustainable agriculture, allowing crop diversity and better soil surface exploitation. Simulation of intercropped plants with integrated soil–plant–atmosphere models is a challenging procedure due to the requirement of a second spatial dimension for calculating the soil water lateral flux. Evaluations of more straightforward approaches for intercrop modeling are, therefore, mandatory. An adaptation of the 1D model Soil, Water, Atmosphere and Plant coupled to the World Food Studies (SWAP/WOFOST) to simulate intercropping (SWAP 2×1D) based on solar radiation and water partitioning between plant strips was developed and the outcomes are presented. An application of SWAP 2×1D to maize–soybean (MS) strip intercropping was evaluated against the monocropping maize (M) and soybean (S) simulated with the 1D model SWAP/WOFOST, and a sensitivity analysis of SWAP 2×1D was carried out for the intercropping MS. SWAP 2×1D was able to simulate the radiation interception by both crops in the intercropping MS and also to determine the effect of the radiation attenuation by maize on soybean plants. Intercropped plants presented higher transpiration and resulted in lower soil evaporation when compared to their equivalent monocropping cultivation. A numerical issue involving model instability caused by the simulated lateral water flux in the soil from one strip to the other was solved. The most sensitive plant parameters were those related to the taller plant strips in the intercropping, and soil retention curve parameters were overall all significantly sensitive for the water balance simulation. This implementation of the SWAP model presents an opportunity to simulate strip intercropping with a limited number of parameters, including the partitioning of radiation by a well-validated radiation sharing model and of soil water by simulating the lateral soil water fluxes between strips in the 2×1D environment.

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“…Studies of the returns to investments in agricultural research and development often rely on yield measures (Maredia and Raitzer 2010;Perez and Rosegrant 2015), and the calculated benefits of a fertilizer subsidy program are grounded in the measured effects of fertilizer on crop yield (Jayne et al 2013). Research on the yield effects of intercropping in smallholder systems also hinges on good measures of crop yield (Himmelstein et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Methods of crop yield measurement on multi-cropped plots: Examples from Tanzania

et al. 2019

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Precise agricultural statistics are necessary to track productivity and design sound agricultural policies. Yet, in settings where multi-cropping is prevalent, even crop yield-perhaps the most common productivity metric-can be challenging to measure. In a survey of the literature on crop yield in low-income settings, we find that scholars specify how they estimate the area denominator used to measure yield in under 10% of cases. Using household survey data from Tanzania, we consider four alternative methods of allocating land area on multi-cropped plots, ranging from treatment of the entire plot as the yield denominator to increasingly precise approaches that account for the space taken up by other crops. We then explore the implications of this measurement decision for analyses of yield, focusing on one staple crop that is often grown on its own (rice) and one that is frequently found on mixed plots and in intercropped arrangements (maize). A majority (64%) of cultivated plots contain more than one crop, and average yield estimates vary with different methods of calculating area plantedparticularly for maize. Importantly, the choice among area methods influences which of these two crops is found to be more calorie-productive per hectare. This choice also influences the statistically significant correlates of crop yield, such that the benefits of intercropping and including legumes on a maize plot are only evident when using an area measure that accounts for mixed cropping arrangements. We conclude that the literature would benefit from greater clarity regarding how yield is measured across studies.

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A meta-analysis of intercropping in Africa: impacts on crop yield, farmer income, and integrated pest management effects

Cited by 91 publications

References 32 publications

Impact of Cover Crop Management on Level of Weed Suppression: A Meta‐Analysis

Impact of Cover Crop Management on Level of Weed Suppression: A Meta‐Analysis

Intercropping Simulation Using the SWAP Model: Development of a 2×1D Algorithm

Methods of crop yield measurement on multi-cropped plots: Examples from Tanzania

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