CONTEXT

Cocoa (Theobroma cacao L.) is one of the world's most important agricultural commodity crops with the largest share of global production concentrated in West Africa. Current on-farm yields in this region are low and are expected to decrease in response to climate change, through warming and shifts in rainfall. Interventions intended to improve yields and climate adaptation require an understanding of the main drivers of yields across farms.

OBJECTIVE

In this regard, we quantified the extent to which environmental (i.e., climate and soil) conditions drive cocoa yields and how this differs for farms achieving on average low- and high mean production levels based on an unprecedented dataset of 3827 cocoa farms spanning the environmental gradients of Ghana. We further quantified the relative importance of management practices based on a subset of 134 farms for which management information was available.

METHODS

We modelled on-farm annual cocoa yield as a function of environmental variables for the large dataset and cocoa yield per tree as a function of environmental and management variables for the subset farms using mixed-effects models. Differences in effects on yield between farms with low and high mean production levels were evaluated using quantile mixed-effects models.

RESULTS AND CONCLUSIONS

There was considerable variability in yields across farms, ranging from ~100 to >1000 kg ha⁻¹ (mean = 554 kg ha⁻¹). Mixed-effects models showed that the fixed effects (i.e., environmental variables) only explained 7% of the variability in yields whilst fixed and random effects together explained 80%, suggesting that farm-to-farm variation played a large role. Explained variation in cocoa yields per tree of 134 farms in the subset increased from 10% to 25% when including management variables in addition to environmental variables. In both models, climate-related factors had a larger effect on yields than edaphic factors, with radiation of the main dry season and that of the previous year having the strongest effects on on-farm- and tree yields, respectively. The quantile regression analyses showed that productivity in high-yielding farms (90th percentile) was more strongly driven by environmental factors than in low-yielding farms (10th percentile).

In conclusion, agronomic management is the dominant determinant of on-farm cocoa yields in Ghana, more so than environmental conditions. Furthermore, high-yielding cocoa farms are more sensitive to environmental conditions than low-yielding ones.

SIGNIFICANCE

Our findings suggests that good agricultural practices need to be in place before investing in additional climate adaptation practices.

中文翻译：

---

揭示加纳农场可可产量在不同环境梯度中高度可变的驱动因素

语境

可可 ( Theobroma cacao L.) 是世界上最重要的农产品之一，全球产量的最大份额集中在西非。该地区目前的农场单产较低，预计会因气候变化、变暖和降雨量变化而下降。旨在提高产量和适应气候的干预措施需要了解各农场产量的主要驱动因素。

客观的

在这方面，我们量化了环境（即气候和土壤）条件在多大程度上推动了可可产量，以及基于跨越环境的 3827 个可可农场的史无前例的数据集实现平均低和高平均产量水平的农场之间的差异。加纳的梯度。我们根据可获得管理信息的 134 个农场的子集，进一步量化了管理实践的相对重要性。

方法

我们使用混合效应模型将农场年可可产量建模为大型数据集的环境变量的函数，并将每棵树的可可产量建模为子农场的环境和管理变量的函数。使用分位数混合效应模型评估了平均生产水平低和高的农场之间对产量影响的差异。

结果和结论

不同农场的产量差异很大，从 ~100 到 >1000 kg ha ^-1（平均值 = 554 kg ha ^-1）。混合效应模型表明，固定效应（即环境变量）仅解释了 7% 的产量变异，而固定效应和随机效应共同解释了 80%，表明农场与农场之间的差异发挥了重要作用。当除了环境变量之外还包括管理变量时，子集中 134 个农场的每棵树可可产量的解释变化从 10% 增加到 25%。在这两个模型中，气候相关因素对产量的影响大于土壤因素，主要旱季和前一年的辐射分别对农场和树木产量的影响最强。分位数回归分析表明，与低产农场（第 10 个百分点）相比，高产农场（第 90 个百分点）的生产力更受环境因素的驱动。

总之，农艺管理是加纳农场可可产量的主要决定因素，比环境条件更重要。此外，高产可可农场比低产可可农场对环境条件更敏感。

意义

我们的研究结果表明，在投资于额外的气候适应实践之前，需要制定良好的农业实践。