Landscape-scale assessment of soil properties, water quality and related nutrient fluxes under oil palm cultivation: a case study in Sumatra, Indonesia
Notice bibliographique
Résumé
The rapid expansion of oil palm cultivation in Southeast Asia raises environmental concerns. Oil palm growers in Indonesia are faced with the challenge of sustaining high yields to keep pace with the growing global demand for oil and fats, while reducing the environmental impacts of oil palm cultivation. Environmental impacts associated with the deforestation at the initial phase of an oil palm plantation establishment are well documented, however the impacts of mature oil palm plantation on water quality remain poorly investigated. Oil palm is a perennial crop cultivated predominantly on weathered tropical soils, so high fertilizer input is necessary to sustain high yields, which is expected to endanger neighboring aquatic ecosystems. In Indonesia, 39 % of oil palm planted area is owned by smallholder farmers, who rely on mineral fertilizers to support oil palm production, and 52 % are large private plantations operated by private industries. In addition to mineral fertilizers, industrial plantations also apply mill byproducts as organic fertilizers. Soil characteristics and fertilizer management in oil palm plantations were expected to alter the soil fertility status and nutrient loads to waterways. Oil palm plantations generally extend over thousands of contiguous hectares, so the effect of fertilizer management on the soil response and nutrient loads to waterways requires landscape-scale studies accounting for soil variability and long-term fertilization sequences across the plantation. The first objective of the thesis was to (i) perform a literature review that provides an overview of the agricultural practices in oil palm plantations as well as hydrological processes involved in the nutrient transfers to waterways. Then I aimed to (ii) assess the effect of long term mineral and organic fertilizer sequences on the soil response, considering different soil types, (iii) characterize the dominant hydrological processes involved in the nutrient fluxes to waterways, and (iv) assess the effect of fertilizer management and soil characteristics on groundwater quality and nutrient fluxes to streams. The study area was located in Central Sumatra, Indonesia, which has a tropical humid climate and weathered soils (Ferralsols). The study area was a landscape including a 4000 ha industrial plantation and a 1500 ha smallholder plantation using rational fertilizer programs. Low-fertility Ferralsols responded significantly to continuous applications of organic fertilizers, with greater improvement on coarser-textured soils, compared to repeated applications of mineral fertilizers. I proposed that spatial fertilizer management at the landscape-scale should complement the current plot-scale fertilizer management to get higher nutrient use efficiency and improve soil fertility in an oil palm plantation. One year multi-site monitoring of stream water quality showed nutrient concentrations below Indonesian standards for water quality. In this case study, mature oil palm cultivation did not contribute to the eutrophication of aquatic ecosystems. This was ascribed to nutrient dilution in streams from the high rainfall as well as high nutrient demand by oil palm that was met with a rational fertilizer program. Assessment of nutrient fluxes from baseflow showed that loamy-sand uplands were more sensitive to nutrient losses than loamy lowlands, and organic fertilization helped to reduce nutrient losses to streams. The study also showed high dissolved organic matter content in streams, likely from natural sources. Oil palm agroecosystems in the study area are characterized by fast groundwater renewal indicating the potential for inputs to be quickly transported from soils to the streams. This may be of concern when unbalanced fertilizer management leads to over-application of nutrients or persistent agrochemicals like pesticides bind to dissolved organic matter, since they will be susceptible to contribute to nonpoint source pollution in streams.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».