Improving the water productivity of integrated crop-livestock systems in the semi-arid tropics of Zimbabwe : an ex-ante analysis using simulation modeling

The semi-arid tropics of Zimbabwe are characterised by low levels of economic activity, high incidence of land degradation and a high concentration of the rural poor. Water scarcity is also a principle constraint, and available water is used ineffectively, as evidenced by low crop and livestock water productivity. Low crop productivity is partly attributed to inherent low soil fertility and impoverishment is further exacerbated by continuous cropping without addition of adequate organic and inorganic fertilizers due to unavailability and high costs, while feed shortages especially during the dry season, high incidence of diseases and mortality rates mainly cause low livestock productivity. In this study soil fertility and feed issues were addressed as they were perceived as some of the constraints with solutions that are within the farmers’ capabilities. On-farm surveys and field experiments were done in Nkayi district in northwest Zimbabwe to understand the current situation in crop-livestock systems. A simulation modeling approach was used to evaluate potential interventions, which can be used as entry points to improve crop and livestock water productivity. Crop and livestock production were the main livelihood activities. Average cultivated land was 3 ha while fallow land was 1 ha per household. Livestock holdings that include cattle, donkeys and goats were 9.5 TLU. Soil fertility in terms of N, P and OC was very low, average values were 0.04, 0.01 and 0.37%, respectively. Crop and livestock water productivity was also very low, average values were 0.04 kg m-3 and 0.02 US$m-3, respectively. Interventions which use low cost and locally available organic inputs were evaluated using APSIM and feed deficits using the MLA model. The interventions were the FP, MN and MMR treatments. Their potential effects on crop water productivity, soil fertility and contribution to dry season feed were assessed. Average maize grain water productivity was 0.34 0.42 and 0.76 kg m-3 under the FP, MN and MMR treatments, respectively, while mucuna water productivity was 1.34 kg m-3. Cropping under the FP and MN treatments showed negative trends in SOC and TN over years with losses ranging from 17 to 74 kg ha-1 yr-1 and 6 to 16 kg ha-1 yr-1, respectively. In contrast the MMR treatment showed positive trends in both SOC and TN under the poor and average farmers while for the better-off it was maintained. SOC and TN were increased by about 2.6 to 194 kg ha-1 yr-1 and 6 to 14 kg ha-1 yr-1, respectively. Crude protein (CP) content in maize stover was 29, 32 and 82 g kg-1 under the FP, MN and MMR treatments, respectively. The potential contribution to daily feed requirements during the dry season, in-terms of DM, CP and ME of stover and mucuna biomass was also evaluated. Maize stover obtained from the FP and MN treatment could not supply 100$ m-3. Maßnahmen mit geringen Kosten, die auch lokal verfügbare organische Dünger nutzen, werden mit APSIM modelliert. Zur Ermittlung des Futterbedarfs des Viehs wird der Meat and Livestock Australia (MLA)-Rechner eingesetzt. Die gewählten Maßnahmen sind: von den Farmern üblicherweise eingesetzte Maßnahmen (FP), organische Düngung (MN) und ein Fruchtwechsel von Mais und Mucuna (Mucuna pruriens) (MMR). Die potentiellen Auswirkungen dieser Maßnahmen auf die Wasserproduktivität im Pflanzenbau, auf die Bodenfruchtbarkeit und die Futterproduktion in der Trockenzeit werden geschätzt. Die durchschnittliche Wasserproduktivität bei Maiskörnerertrag beträgt 0.34, 0.42 bzw. 0.76 kg m-3 bei FP, MN bzw. MMR und bei Mucuna 1.34 kg m-3. FP bzw. MN zeigt einen negativen Trend hinsichtlich des organischen Kohlenstoffs im Boden (SOC) und des Gesamtstickstoffgehalts (TN) simuliert über 30 Jahre mit einer Abnahme von 17 bis 74 kg ha-1 Jahr-1 bzw. 6 bis 16 kg ha-1 Jahr-1. Im Gegensatz hierzu zeigt MMR einen positiven Trend sowohl bei SOC und TN in den Wohlstandskategorien arm und durchschnittlich, während in der Kategorie wohlhabende Farmer sich die Werte nicht verändern. SOC und TN nehmen 2.6 bis 194 kg ha-1 Jahr-1 und 6 bis 14 kg ha-1 Jahr-1 zu. Der Roheiweiß-(CP)-Gehalt der Maiserntereste beträgt 29, 32 bzw. 82 g kg-1 bei FP, MN bzw. MMR. Der potentielle Beitrag zum täglichen Futterbedarf hinsichtlich Trockenmasse (DM), CP und metabolisierbare Energie (ME) der Biomasse der Maiserntereste und von Mucuna wird ebenfalls geschätzt. Die Maiserntereste können bei FP und MN nicht 100% des täglich benötigten DM, CP und ME liefern. Jedoch können Maiserntereste und Mucunabiomasse bei MMR diese Menge bei den Kategorien arme bzw. durchschnittlich wohlhabende Farmer liefern und ca. 50% DM und 100% CP und ME bei den wohlhabenden Farmern. Die Ergebnisse der Studie zeigen, dass der Mais-Mucuna-Fruchtwechsel das Potential hat, die Bodenfruchtbarkeit und die Wasserproduktivität sowohl im Pflanzenbau als auch in der Viehhaltung kleinbäuerlicher Systeme in den semiariden Regionen Zimbabwes zu verbessern

Accurate crop yield predictions from modelling tree-crop interactions in gliricidia-maize agroforestry

Agroforestry systems, containing mixtures of trees and crops, are often promoted because the net effect of interactions between woody and herbaceous components is thought to be positive if evaluated over the long term. From a modelling perspective, agroforestry has received much less attention than monocultures. However, for the potential of agroforestry to impact food security in Africa to be fully evaluated, models are required that accurately predict crop yields in the presence of trees. The positive effects of the fertiliser tree gliricidia (Gliricidia sepium) on maize (Zea mays) are well documented and use of this tree-crop combination to increase crop production is expanding in several African countries. Simulation of gliricidia-maize interactions can complement field trials by predicting crop response across a broader range of contexts than can be achieved by experimentation alone. We tested a model developed within the APSIM framework. APSIM models are widely used for one dimensional (1D), process-based simulation of crops such as maize and wheat in monoculture. The Next Generation version of APSIM was used here to test a 2D agroforestry model where maize growth and yield varied spatially in response to interactions with gliricidia. The simulations were done using data for gliricidia-maize interactions over two years (short-term) in Kenya and 11 years (long-term) in Malawi, with differing proportions of trees and crops and contrasting management. Predictions were compared with observations for maize grain yield, and soil water content. Simulations in Kenya were in agreement with observed yields reflecting lower observed maize germination in rows close to gliricidia. Soil water content was also adequately simulated, except for a tendency for slower simulated drying of the soil profile each season. Simulated maize yields in Malawi were also in agreement with observations. Trends in soil carbon over a decade were similar to those measured, but could not be statistically evaluated. These results show that the agroforestry model in APSIM Next Generation adequately represented tree-crop interactions in these two contrasting agro-ecological conditions and agroforestry practices. Further testing of the model is warranted to explore tree-crop interactions under a wider range of environmental conditions

Including soil organic carbon into nationally determined contributions: Insights from Zambia

Healthy soils are the foundation of sustainable and regenerative food systems and provide several vital ecosystem services. Sequestering carbon in agricultural soils, for example, can have mutual benefits for climate change mitigation and adaptation, food and nutrition security, biodiversity, and water resilience. Despite these benefits, there are few policies that incentivize farmers to invest in maintaining and improving soil health. This policy brief highlights opportunities for the inclusion of soil health and soil organic carbon (SOC) into the Nationally Determined Contributions (NDCs) as a key step for governments to support farmers in investing in their soil. We interviewed key informants involved in the NDC process to understand the process for the developing the NDC targets and investigated reasons why policy makers did or did not include soil in these targets

Mesure, notification et vérification de l’agriculture intelligente face au climat: changement de perspective, changement de possibilités ? Conclusions de l’auto-évaluation nationale des besoins, systèmes et opportunités

Depuis 2009, des milliards de dollars ont été investis dans des programmes d’AIC dans le but d’aider les petits exploitants à augmenter leur productivité tout en s’adaptant aux changements climatiques et en contribuant à les atténuer. Cependant, l’AIC a récemment dépassé les cercles de l’aide au développement et de la société civile, et les pays se sont mis à adopter des stratégies d’AIC dans le cadre de leurs politiques et stratégies de riposte aux changements climatiques et de développement agricole, notamment leurs Contributions déterminées au niveau national (CDN)

Measurement, reporting and verification of climate-smart agriculture: Change of perspective, change of possibilities?

The World Agroforestry Centre (ICRAF), Unique Forestry and Land Use and Vuna have been working with stakeholders in four countries in eastern and southern Africa (Tanzania, Malawi, Zambia and Zimbabwe) to assess the current state of national CSA M&E and to set out country-specific roadmaps for developing systems for monitoring and reporting on CSA. The project took a country-driven approach to documenting stakeholders’ information needs, exploring how to build on and align with existing M&E systems and international reporting frameworks, and encouraging cross-country comparisons. Though the research was grounded in southern Africa, these lessons are applicable to CSA and other topic-driven initiatives (such as land restoration and the Bonn Challenge) across similar environments and social contexts on the continent and around the world. Here we detail three key findings from the assessment

Vision for Adapted Crops and Soils (VACS) Research in Action: Opportunity Crops for Africa

The Vision for Adapted Crops and Soils (or “VACS”) brings together dedicated communities and individuals from research, advocacy, and policy to shine a light on opportunities that opportunity crops provide to build more resilient and food systems. Scaling up production and access to more diverse, climate-resilient crop varieties that support good nutrition and better livelihoods is a goal shared across national, regional, and international communities. VACS has gained important visibility, including from the U.S. Secretary of State, Antony Blinken, which has galvanized further international support. This report outlines the guiding concepts of the VACS approach, provides an overview of the research conducted as part of VACS through crop-modeling and evidence synthesis approaches, and recommends areas of focus for the movement going forward as well as ways to engage in VACS

Understanding the Role of Soils and Management on Crops in the Face of Climate Uncertainty in Zimbabwe: A Sensitivity Analysis

Although climate change is likely to affect a wide variety of sectors in Zimbabwe, the risk to agriculture stands out most since agriculture is the mainstay of the country’s economy. In addition, there is little information available on how to help smallholder farming systems and livelihoods respond to these risks. To determine the effects on crop production of expected changes in precipitation patterns and projected increases in carbon dioxide (CO2) and temperature, we used two process-based crop models—the Decision Support System for Agrotechnology Transfer (DSSAT) model and the Agricultural Production Systems Simulator (APSIM) model. The models were calibrated and validated to assess the effects of single and combined climatic factors on grain and stover yield performance of maize and groundnut, across three soil types. The two models generally agree on the effects that different climatic factors have on both maize and groundnuts, however, the magnitude of the effects varied. For example, reductions on maize grain yields are more pronounced in the APSIM model while the DSSAT model shows more pronounced reduction of maize stover yields. Both models show yield benefits under elevated CO2 concentration for groundnuts negating the effects of increased temperatures when evaluating the combined effects of the climatic factors. However, yield increases for both groundnut grain and stover are more pronounced in the DSSAT model. The key finding is that soils play an important role in determining outputs of crop-climate interactions: they can buffer or aggravate climatic impacts