Evaluation of multiple satellite precipitation products for rainfed maize production systems over Vietnam

High-resolution reliable rainfall datasets are vital for agricultural, hydrological, and weather-related applications. The accuracy of satellite estimates has a significant effect on simulation models in particular crop simulation models, which are highly sensitive to rainfall amounts, distribution, and intensity. In this study, we evaluated five widely used operational satellite rainfall estimates: CHIRP, CHIRPS, CPC, CMORPH, and GSMaP. These products are evaluated by comparing with the latest improved Vietnam-gridded rainfall data to determine their suitability for use in impact assessment models. CHIRP/S products are significantly better than CMORPH, CPC, and GsMAP with higher skill, low bias, showing a high correlation coefficient with observed data, and low mean absolute error and root mean square error. The rainfall detection ability of these products shows that CHIRP outperforms the other products with a high probability of detection (POD) scores. The performance of the different rainfall datasets in simulating maize yields across Vietnam shows that VnGP and CHIRP/S were capable of producing good estimates of average maize yields with RMSE ranging from 536 kg/ha (VnGP), 715 kg/ha (CHIRPS), 737 kg/ha (CHIRP), 759 kg/ha (GsMAP), 878 kg/ha (CMORPH) to 949 kg/ha (CPC). We illustrated that there is a potential for use of satellite rainfall estimates to overcome the issues of data scarcity in regions with sparse rain gauges.

Effect of conservation agriculture on soil properties and maize grain yield in the semi-arid Laikipia county, Kenya.

Low and unreliable rainfall, along with poor soil health, is a main constraint to maize production in the semi-arid parts of Kenya that account for over 79% of the country's land area. In the vast county of Laikipia, farmers continue to plant maize despite the predominantly low quantities of precipitation. Participatory farmer experimentation with Conservation Agriculture (CA) was undertaken for six consecutive growing seasons between July 2013 and December 2016 to determine the effectiveness of CA as a method of improving soil properties and enhancing maize yields with the limited rainfall quantities received in these parts of Kenya. The main CA practices tested include chisel tine furrow opening (ripping) and live legume (Lablab purpureus) cover crop, as well as maize stover mulches, all implemented under varying inorganic fertilizer rates. The research was done across 12 administrative locations of Laikipia County where soils are mainly Phaeozems and Vertisols with a clay-loam texture. The research design used was researcher-designed and farmer-managed. In each of the 12 trial sites, participatory farmers' assessments and field days were carried out as a way of outreach to the bigger farming communities around the trial sites. The research findings obtained demonstrated that the use of CA impacts positively on soil properties and is a viable practice for enhancing maize yields in these moisture deficit-prone parts of the country. Soil chemical analysis assessment results showed that CA impacted positively on a number of soil mineral components including organic carbon, total nitrogen, phosphorus, potassium, calcium and pH. Midseason chlorophyll content assessment of the maize crop showed that there was good response to fertilizer application, as well as to mulching with crop residues for soil cover. Maize grain yield data also showed that the use of a CA package comprising chisel tine ripping combined with mulching by plant residues and use of mineral fertilizer resulted in a two- to threefold increase in grain yields above the farmer practice control. Mean maize grain yield in farmer practice plots was 1067 kg ha-1 compared with the CA-treated plot with mineral fertilization that yielded 2192 kg ha-1.

Spatiotemporal monsoon characteristics and maize yields in West Africa

To assess the vulnerability of rainfed agriculture in West Africa (WA) to climate change, a detailed understanding of the relationship between food crop yields and seasonal rainfall characteristics is required. The highly seasonal rainfall in the region is expected to change characteristics such as seasonal timing, duration, intensity, and intermittency. The food crop yield response to changes in these characteristics needs greater understanding. We follow a data-driven approach based on historical yield and climate data. Such an approach complements model-based approaches. Previous data-driven studies use spatially and temporally averaged precipitation measures, which do not describe the high degree of spatial and temporal variability of the West African Monsoon (WAM), the primary source of water for agriculture in the region. This has led previous studies to find small or insignificant dependence of crop yields on precipitation amount. Here, we develop metrics that characterize important temporal features and variability in growing season precipitation, including total precipitation, onset and duration of the WAM, and number of non-precipitating days. For each temporal precipitation metric, we apply several unique spatial aggregation functions that allow us to assess how different patterns of high-resolution spatial variability are related to country-level maize yields. We develop correlation analyses between spatiotemporal precipitation metrics and detrended country-level maize yields based on findings that non-climatic factors, such as agricultural policy reform and increased investment, have driven the region’s long-term increase in maize yields. Results show that that the variability in the number of days without rain during the monsoon season and the lower bounds to the spatial rain pattern and end to the monsoon season are most strongly associated with maize yields. Our findings highlight the importance of considering spatial and temporal variability in precipitation when evaluating impacts on crop yields, providing a possible explanation for weak connections found in previous studies.

Vulnerability Assessment of Maize Yield Affected by Precipitation Fluctuations: A Northeastern United States Case Study

Crop yields are threatened by global climate change. Maize has high water requirements, and precipitation fluctuations can impact its yield. In this study, we used the Environmental Policy Integrated Climate (EPIC) model to simulate maize yields in eight northeastern U.S. states. We used precipitation fluctuations and the coefficient of variation (CV) of yield as indicators to construct a vulnerability curve for the CV of yield and precipitation fluctuations. We then evaluated the vulnerability of maize yields under precipitation fluctuations in the region. We obtained the following results: (1) the fitted vulnerability curves were classified into three categories (positive slope, negative slope, and insignificant fit), of which the first category accounted for about 92.7%, indicating that the CV of maize yield was positively correlated with precipitation fluctuations in most parts of the study area; and (2) the CV of maize yield under 11 precipitation fluctuation scenarios was mapped to express the CV at the spatial level, and the maize yield in Connecticut and Maryland proved to be the most sensitive to precipitation fluctuations. This study provided a theoretical and experimental basis for the prevention of maize yield risk under fluctuating precipitation conditions.

Comparative effects of the combination of biofertilizer, NPK, and mycorrhizal application on maize production system

The use of uncontrolled an-organic fertilizer continuously will degrade soil fertility and nutrients balance. To minimize those impacts, biofertilizers and an-organic fertilizer are needed to maintain a sustainable maize production system. The study used a randomized complete block design with 3 replications with different levels of fertilizer application, conducted at University of Brawijaya experimental research station at Jatimulyo-Malang-East Java. Several parameters were measured to examine those effects on soil and crops. The treatments affect crop height, leaf chlorophyll indices, leaf area indices, maize yields, total number, and mycorrhizal infection. The best treatment was detected under the combination of 100% biofertilizer and 100% NPK, along with the addition of 100% microelements. The lowest was observed under mycorrhizal applications. There was a positive correlation between chlorophyll, crop height, leaf area, and maize yields.